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• Published: 07 May 2024

A filtering reconfigurable intelligent surface for interference-free wireless communications

• Jing Cheng Liang 1 , 2   na1 ,
• Lei Zhang   ORCID: orcid.org/0000-0002-8791-6374 1 , 2   na1 ,
• Zhangjie Luo 1 , 2 ,
• Rui Zhe Jiang 1 , 2 ,
• Zhang Wen Cheng 1 ,
• Si Ran Wang   ORCID: orcid.org/0000-0002-8937-9031 1 , 2 ,
• Meng Ke Sun 1 , 2 ,
• Shi Jin 3 ,
• Qiang Cheng   ORCID: orcid.org/0000-0002-2442-8357 1 , 2 , 4 &
• Tie Jun Cui   ORCID: orcid.org/0000-0002-5862-1497 1 , 2 , 4  

Nature Communications volume  15 , Article number:  3838 ( 2024 ) Cite this article

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The powerful capability of reconfigurable intelligent surfaces (RISs) in tailoring electromagnetic waves and fields has put them under the spotlight in wireless communications. However, the current designs are criticized due to their poor frequency selectivity, which hinders their applications in real-world scenarios where the spectrum is becoming increasingly congested. Here we propose a filtering RIS to feature sharp frequency-selecting and 2-bit phase-shifting properties. It permits the signals in a narrow bandwidth to transmit but rejects the out-of-band ones; meanwhile, the phase of the transmitted signals can be digitally controlled, enabling flexible manipulations of signal propagations. A prototype is designed, fabricated, and measured, and its high quality factor and phase-shifting characteristics are validated by scattering parameters and beam-steering phenomena. Further, we conduct a wireless communication experiment to illustrate the intriguing functions of the RIS. The filtering behavior enables the RIS to perform wireless signal manipulations with anti-interference ability, thus showing big potential to advance the development of next-generation wireless communications.

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Introduction.

Reconfigurable intelligent surface (RIS) is also called programmable metasurface, which is a two-dimensional electromagnetic (EM) metamaterial integrated with tunable components and is controlled by digital modules such as field-programmable gate arrays (FPGAs) circuit 1 , 2 , 3 , 4 , 5 , 6 . It can dynamically and flexibly manipulate the properties of EM waves and fields in a programmable way, including amplitude, phase, polarization, and frequency, and thus it is naturally compatible with the information world 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 . Specifically, it can actively control wireless propagation and create an intelligent wireless environment. Together with other advantages like simple architecture, low cost, low power consumption, and easy deployment, RIS has attracted broad attention from the wireless community, and numerous theoretical innovations and prototype measurements have demonstrated the broad applications in both 5 G and future 6 G networks, bringing a new paradigm to the future wireless communications 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 . Recent studies have demonstrated the immense potential of RIS in realistic deployment, propelling its value in practical applications to unprecedented heights 33 , 34 .

RISs have brought about revolutionary changes to the wireless community in two aspects. The first is the RIS-based simplified-architecture wireless transmitters that directly realize signal modulations without using complicated digital-analog converters, mixers, or other devices in the conventional transmitting systems 23 , 24 , 25 , 26 . The other is the RIS-assisted wireless environment modulations, which means optimizing the communication quality by the powerful beam manipulation capabilities of the RIS 27 , 28 , 29 , 30 , 31 , 32 . Recently, the second application has garnered widespread interest, especially in the 5 G and next-generation mobile communications. It should be noticed that the practical wireless mobile communication scenarios often have multiple networks occupying closely adjacent spectra. However, existing RISs typically have broadband properties that not only tune signals in the target spectrum but also affect nontarget signals, resulting in serious network coexistence problems or even security concerns 35 , 36 . In addition, most of the current RISs failed to consider the impact of electromagnetic interference or noise, no matter if it is intentional or non-intentional 37 . This lack of frequency selectivity, which can be measured in terms of a quality factor (Q factor), hinders their practical deployment. This issue should be taken seriously in today’s wireless environment with the increasingly crowded spectrum.

To tackle the above problems, we propose a filtering RIS that can selectively manipulate the wireless channels in a specific narrow frequency band and reject the signals out of the band. Figure  1 depicts an exemplary scenario to illustrate its typical applications. Three base stations, namely BS 1 , BS 2 , and BS 3 , are located outside of the room and operate at three adjacent frequencies f 1 , f 2 , and f 3 , respectively. A large-scale RIS is mounted on the wall indoors, aiming at enhancing the wireless communications between the indoor users (IU 1 , IU 2 , and IU 3 ) and BS 2 at the frequency f 2 . This is realized by generating multiple narrow beams and collimating them precisely towards the indoor users. More significantly, the RIS possesses a powerful frequency-selecting feature to allow only the f 2 signals to enter the room but block out the f 1 and f 3 signals. As a result, the presence of the RIS ensures that the wireless communication indoors is not disturbed by signals from BS 1 and BS 3 . We remark that the aim of the filtering RIS is different from the widely-used filters in user devices to enhance their performance; instead, they serve to actively regulate the spectrum within a confined environment without requiring a temporary refitting of all devices.

The base stations, named BS 1 , BS 2 , and BS 3 , are located outdoors working at three adjacent frequencies f 1 , f 2 , and f 3 , respectively. The filtering RIS placed indoors on the wall aims to enhance the quality of the wireless communications between the base station BS 2 and the indoor users IU 1 , IU 2 , and IU 3 by generating specific pencil beams and collimating them accurately toward the targets. Different from the conventional RISs, the filtering RIS exhibits a powerful frequency-selecting ability that allows only the f 2 signal to enter the room, but strongly rejects the outdoor f 1 and f 3 signals. Hence, potential interference issues caused by the out-of-band signals can be eliminated.

As a proof of concept, we design the RIS with advanced filtering and reconfigurable phase-shifting functionalities, which features exceptional capabilities in precisely selecting incoming signals in the frequency domain and provides flexible beamforming in the spatial domain, showing stronger power than its predecessors to modulate wireless environments. It is built based on the receiver-transmitter metasurface structure for the sake of low profile and wide phase-shifting range with 2-bit modulations 38 , 39 , 40 , 41 , 42 . Notably, the integrated filtering modules endow the frequency-selective characteristic, which is described by an enhanced Q factor that surpasses most previous studies. Its central frequency is 3.5 GHz with a 200 MHz passband, which falls in the 4 G LTE Band 42 and the primary band for the 5 G technology ( https://www.4g-lte.net/about/lte-frequency-bands/lte-band-42/ , “5G Spectrum Public Policy Position” in white paper, 2017). An RIS prototype is fabricated and a series of numerical simulations and experiments are conducted for the property validations and application demonstrations. The transmission coefficients in the frequency domain are measured, showing the rejection rate of over 20 dB in the stopbands and reconfigurable 2-bit phase coding capabilities in the passband. Then, the RIS’s ability to customize the wireless propagation environment is proved by manipulating far-field transmission patterns with steered EM beams. To further showcase the practical applications, a wireless communication experiment is carried out with the RIS placed between the transmitting and receiving modules. With the help of the RIS, the out-of-band signals are denied; only the signals in the passband can transmit through and be dynamically redirected to desired directions. By correctly setting the frequency of the system and the position of the receiving module, wireless connections are established through the RIS, and the information of color pictures is transferred and recovered successfully. The proposed RIS offers a promising solution to the issue of frequency interference that is not addressed in previous RIS-assisted wireless systems. It advances the practical deployment of RIS, particularly in complex propagation environments with congested spectrum. Compared with the conventional frequency selective surfaces (FSSs) that have been widely employed for frequency selectivity 43 , 44 , 45 , 46 , 47 , the proposed single-layer RIS has the advantage of significantly improved filtering performance. Beyond that, the proposal’s flexible beamforming capability makes it more useful for increasing the signal strength in target directions and enhancing the interference immunity for indoor wireless communications 30 , 34 , 48 , 49 , 50 . Detailed comparisons between the RISs and FSSs are provided in Supplementary Information Note  10 . Unlike the conventional repeaters or relays that contain active components like analog-to-digital/digital-to-analog converters, mixers, and power amplifiers, the proposed RIS has significantly lower power consumption and complexity, and is free of additive noises 51 . More discussions on the RIS and conventional repeaters or relays are provided in Supplementary Information Note  11 .

Design of filtering RIS

Figure  2 a, b show the two commonly used schematic frameworks for the conventional RISs: the stacked multilayer RIS 44 , 45 , 52 , 53 , 54 and receiver-transmitter RIS 38 , 39 , 40 , 41 , 42 . Based on the concept of frequency-selective-surface (FSS), the stacked multilayer RIS can exhibit satisfactory phase-shifting property performance, but the high profile makes it less preferred in wireless communication systems. Moreover, its transmission phase is shifted by adjusting the relatively wide instantaneous transmission band. As can be seen in Fig.  2a , the regulated phase-shifting behavior happens only in the narrow overlapping spectrum of the tunable band; the signals outside of this overlapping area but still within the instantaneous band are also allowed to be transmitted with random phase changes. The second type, receiver-transmitter RIS, modulates the phase in the operating band by using the transmission-line phase shifters or reversing the mode current. Compared with the first type, its profile is much lower, but its operating bandwidth is relatively wider, as illustrated in Fig.  2b . The two types of RIS have low Q factors, which means that much wider bandwidths are occupied than the frequency range with expected phase-shifting properties.

a A unit cell of the conventional stacked multilayer RIS. b A unit cell of the conventional receiver-transmitter RIS. These two types of RIS have low Q factors and wide occupied bandwidths. c A subarray of the proposed filtering RIS with a high Q factor for strong frequency-selective property and a narrow occupied bandwidth. A functional module is integrated into the subarray.

The working mechanism of the proposed transmission-type filtering RIS is plotted in Fig.  2c . It is based on the receiver-transmitter RIS architecture, with a “functional module” inserted between them that allows the RIS to be extended with more complex functionalities and greatly enhances the freedom of RIS designs 55 . Here, a lumped filter and a phase shifter are integrated into the module. The high-Q filter provides a strong out-of-band rejection and low passband insertion loss, thus addressing the low-Q problem of the conventional receiver-transmitter RIS. The phase shifter enables reconfigurable modulations of the passband signals without degrading the filtering performance. By fully exploiting the advantages of each component, a large phase-shifting range in the passband and enhanced filtering performance can be achieved.

In practical application scenarios, the proposed filtering RIS would be implemented on a much larger scale, requiring a significant number of elements. In this study, we focus on a small panel that features 1 × 4 subarrays to demonstrate the feasibility of our proposed concept, as depicted in Fig.  3 . Each subarray includes a receiver, a transmitter, and a functional module. Each receiver contains four parasitic rectangular patches and the microstrip lines below. The microstrip lines are specially designed such that the signals received by the four patches are in phase. The details of the module, which is composed of a phase shifter integrated with a filter chip, are shown in the upper left inset of Fig.  3 . The phase shifter consists of a 0°/180° phase shifter cascaded with a 0°/90° phase shifter. Eight PIN diodes are embedded as the modulating components in the phase shifter. Several metallic vias and narrow lines are connected to the microstrip through RF chokes, acting as the DC routes to bias the diodes. The output of the module is connected to the transmitter on the other side of the ground by a metallic through-via. Round clearances are located on the ground such that the through-vias do not touch it. The transmitter is located on the other side of the metallic ground, which shares exactly the same structure as the receiver. More details on the filter chip, the phase shifter, the receiver, and the transmitter can be found in Supplementary Information Notes  2 , 3 , and 4 .

The details of a subarray are shown at the bottom, and the functional module is presented in the upper left inset.

When the spatial waves impinge on the receiver, they are converted to guided waves, which are then injected into the functional module. Firstly, the signals are transmitted through the filter that offers a rejection of over 20 dB in the stopbands. It should be mentioned that the phase of the signals in the passband is not affected by the filter. Then the signals go into the phase shifter. By switching the ON or OFF states of PIN diodes, different lengths of microstrip paths are chosen, and thus the phase can be adjusted by four reconfigurable states with a 90° interval. After the phase-shifting, the signals are guided to the transmitter on the other side of the ground by the through-via and finally radiated into space. Three aspects should be mentioned. Firstly, the filters in the subarrays are the same, but the phase-shifting behaviors are independent. Hence, flexible manipulations of EM waves can be realized, such as beam-steering and more advanced multichannel communications. Secondly, the RIS is a reciprocal device, that is to say, the wave propagation through it is reversible when the roles of the receiver and transmitter are interchanged. These are important properties for channel modulations of wireless communications. Thirdly, although the filtering RIS is currently designed to support a single polarization, the polarization-independent properties can be practically realized by adopting the dual-polarized transmitter and receiver in the element 55 , 56 .

To evaluate the performance of the filtering RIS, field-circuit cosimulations are performed using the commercial software CST Microwave Studio. An array of 1 × 4 subarrays is analyzed here, and the simulated transmission coefficients are shown in Fig.  4 a, b, where four coding states (states 0, 1, 2, 3) are defined by the four transmission phase-shifting values with a 90° interval. The 2-bit phase coding states are switched by tuning the PIN diodes. From the transmission amplitude spectra in Fig.  4a , we observe that the four curves exhibit a significant overlap, suggesting the consistency of the amplitude-frequency responses of the RIS. An insertion loss of <2.5 dB is achieved in the passband from 3.4 to 3.6 GHz, along with a 20-dB rejection on the two sides of the passband. As the transmission amplitudes are extremely low in the stopbands, the phase response is meaningless. Therefore, the transmission phase spectra in the passband are presented in Fig.  4b . In the passband, stable 90° phase differences are exhibited between the curves, which enables a good 2-bit phase coding characteristic. The simulation results of the transmission coefficients demonstrate that the out-of-band signals are effectively suppressed, while the passband signals are transmitted with the stable 2-bit reconfigurable phase shifting.

a Amplitude and ( b ) phase spectra of the four digital states (States 0–3). A stable 90° phase difference between adjacent states is achieved in the passband. c The E -field intensity distributions on the yoz -plane. The position of RIS is marked by the white dashed frame. d Simulated and ( e ) theoretical 2D far-field patterns of the filtering RIS with the beam scanning angles of −28°, −12°, 0°, 12°, and 28° at 3.5 GHz. f The E -field intensity distributions on the yoz -plane when the anomalous transmissions happen at 3.5 GHz. The position of the RIS is marked by the white dashed frame.

To visually demonstrate the filtering capability of the RIS, Fig.  4c shows the electric-field ( E -field) intensity distribution on the yoz -plane at 3.3–3.7 GHz when the EM waves normally impinge on the array. The propagation direction is along the ­ z -axis, and the polarization is along the y -axis. The RIS is situated in the division between the upper and lower spaces in the figures, marked by a white dashed frame. The incident plane wave propagates downwards. At 3.4, 3.5, and 3.6 GHz, the EM waves can be transmitted to the lower half-space through the RIS. As all RIS elements are operated with the same transmission amplitude and phase, the EM waves propagate vertically downwards on the yoz -plane. In contrast, the EM waves can hardly penetrate at 3.3 and 3.7 GHz, demonstrating the excellent rejection performance of the RIS in the stopbands. These full-wave simulations effectively illustrate the filtering functionality of the proposed RIS, allowing the EM waves in the passband to pass through while blocking those outside.

Table  1 provides a comparison of the filtering properties between the proposed RIS and the ones from refs. 41 , 42 , 44 , 45 . Here the operating bandwidth (BW) means the frequency range with the acceptable phase-shifting ability; BW n dB refers to the bandwidth in which the transmission amplitude is lower than the maximum value by less than n dB. The ratio BW/BW3dB is used to evaluate the operating BW as a percentage of BW3dB. It can be observed that the BW/BW3dB of the stacked multilayer RIS is very small because the operating BW is much narrower than BW3dB 44 , 45 , 52 , 53 , 54 . Compared with them, the proposed RIS has a much larger BW/BW3dB value of 96%, suggesting a very narrow occupied bandwidth. Besides, the following two parameters are employed to quantitively measure the filtering property. The first one is the Q factor, which is defined by the ratio of the center frequency f 0 over BW3dB. The second one is the rectangle coefficient K20dB, which is defined by the ratio BW20dB/BW3dB. Normally, the rectangle coefficient K20dB is larger than 1; the ideal value of 1 means steep transitions on the two edges of the transmission curve, thus exhibiting a perfect filtering effect. Therefore, an ideal RIS should have a large Q factor and a K20dB close to 1. We are delighted to read from Table  1 that the Q factor is 14 in our design, which is much higher than the current studies; the K20dB is 1.3, suggesting a comparable value to the stacked multilayer RISs. The high rejection characteristics of the RIS outside its operating band, combined with its steep transition bands, can effectively avoid potential interferences caused by out-of-band signals.

By independently controlling the transmission phase of each subarray, beam-steering on the yoz- plane is enabled here. In the passband, the four subarrays of RIS are encoded with five sequences (“3210”, “1100”, “0000”, “0011”, and “0123”, where “0, 1, 2, 3” abbreviate the digital states 0–3) for examples. The simulated far-field patterns of the transmitted waves at 3.5 GHz are plotted in Fig.  4d , showing that the transmitted beams are steered to the directions with elevation angles of −28°, −12°, 0°, 12°, and 28°, respectively. These patterns agree quite well with the theoretical results presented in Fig.  4e , which are calculated by ref. 1

where M and N are numbers of elements in the x - and y -directions; \({E}_{m,n}(\theta,\varphi )\) is the transmission far-field pattern of the element ( m , n ); \({\varPhi }_{m,n}\) is the transmission phase of the element ( m , n ); k is the wavenumber of the EM wave in free space; d is the period of the receiver and transmitter elements; \(\theta\) and \(\varphi\) are the elevation and azimuthal angles, respectively.

To further illustrate the anomalous transmission effects, Fig.  4f presents the simulated near-field spatial distributions of the E -field intensities on the yoz -plane at 3.5 GHz when the RIS is controlled by the five coding sequences. It is clearly observed that the plane waves propagate along the - z direction before they interact with the RIS; after they pass through, they are deflected off the normal direction with the specific angles, which are determined by the coding sequences, that is, the phase distributions on the RIS.

The full-wave simulations effectively illustrate the beam manipulation capabilities of the proposed RIS, which can dynamically steer the passband waves towards the specified directions by encoding the RIS. This highlights its potential to control the wireless channels and enhance the wireless communication service quality. It is important to note that only four subarrays are used in this study as a proof of concept to demonstrate the functions of RIS. The elements are controlled in columns, which is consistent with most of the previous studies 22 , 32 , 33 , 34 , 50 , 57 , 58 , 59 . Based on the validated mechanism, it is feasible to design a filtering RIS with a pair of transmitter and receiver and a functional module for filtering and phase-shifting, which can realize the two-dimensional (2D) beam-steering performance. In Supplementary Information Note  7 , the design of the element and its simulated properties are presented. Through simulations, the 2D beamforming is also verified by using an array with 10×10 elements, showing the wide scanning ranges of ±63° and ±64° on the xoz - and yoz -planes, respectively. Additionally, the proposed concept can be moved to higher frequencies by adjusting the structural parameters accordingly and selecting an appropriate filter chip that operates at the desired target frequencies. In Supplementary Information Note  8 , we have designed a filtering RIS that operates at the millimeter-wave (mmWave) frequencies using the same concept. Its filtering and phase-shifting properties are studied through field-circuit cosimulations. As shown in Table I, its performances are competitive when compared to the state-of-the-art work in the mmWave bands.

Fabrication and measurement

An array of 1 × 4 RIS subarrays is fabricated using the printed circuit board technology. A series of experiments are performed to validate the performance of filtering, phase-shifting, and beam manipulation of the transmitted waves. Moreover, its advanced ability to re-arrange the wireless environment is vividly demonstrated by a real communication experiment. The picture of the prototype is shown in the right inset of Fig.  5a . It measures 160 mm × 182 mm, and the total thickness is 8.1 mm, or 0.09 λ 0 , where λ 0 is the wavelength at 3.5 GHz. A digital controlling circuit board is designed and fabricated to provide the operating voltages for the PIN diodes in the phase shifters.

a Photograph of the experimental environment in the microwave anechoic chamber. The pictures of the prototype are given in the insets. b The measurement setup for the transmission coefficient. c The measurement setup for the far-field patterns.

The transmission coefficients and far-field patterns of the proposed RIS are measured in a microwave anechoic chamber. Figure  6a displays the measured amplitude spectra of transmission coefficients under the normal incidence, with the passband 3.4–3.6 GHz denoted by the blue areas. The amplitudes of the four coding states within the passband range from −1.2 to −4.1 dB, with a rapid roll-off and a 20-dB rejection in the stopbands. The loss is mainly due to the insertion loss of the phase shifters and filter chips, which comes at the cost of phase shifting and sharp frequency selection features. More details about the loss analysis are presented in Supplementary Information Note  5 . To address this issue, low-noise amplifiers could be integrated into the functional modules to compensate for the attenuation. A 2-bit phase-shifting operation with the four reconfigurable coding states is realized within the passband, as shown in Fig.  6b . Both transmission amplitude and phase spectra agree well with the simulations. The performances under oblique incidences are discussed in detail in Supplementary Information Note  6 .

The transmission amplitudes ( a ) and phases ( b ) as functions of frequency for the four digital states (States 0–3). c The calculated, simulated, and measured far-field transmission patterns on the yoz -plane of the filtering RIS with the beam scanning to the angles −28°, −12°, 0°, 12°, and 28° at 3.5 GHz.

Using the same experimental setup, the far-field transmission properties are measured. Figure  6c shows the results on the yoz -plane at 3.5 GHz under the control of the five coding sequences. The transmitted beam is deflected to −28°, −13°, 0°, 13°, and 27°, respectively, almost in consistence with the simulated and theoretical ones (−28°, −12°, 0°, 12°, and 28°), with an error of fewer than 2 degrees. The slight discrepancies can be attributed to fabrication and testing errors. Considering the wide beamwidth (half-power beamwidth of about 36°), it can still be claimed that the transmitted beams are indeed deflected to the expected angles controlled by the coding sequences on the RIS. The measured results demonstrate the effective filtering, phase-shifting, and beam-steering abilities of the designed RIS, providing a solid hardware foundation for further applications in real wireless communication scenarios.

The wireless communication configuration and measured results are presented in Figs.  7 and 8 , respectively. Two software-defined radio reconfigurable devices (USRP-2974, National Instruments Corp.) 25 , 26 , 27 that are set to work at 3.5 and 3.9 GHz, respectively, can encode a color picture into a binary stream and modulate it using a quadrature phase shift keying (QPSK) scheme. Two transmitting antennas and two receiving antennas are connected to the output and input of the two USRPs, respectively. Horn antennas with a working bandwidth that covers the entire frequency band of interest (3.1–3.9 GHz) are employed here to eliminate the uncontrollable multipath effects. A windowed absorbing screen is placed between the transmitting and receiving antennas, and the RIS is embedded in the window. The transmitting antennas are placed facing the RIS in a normal orientation, and the distance between the antennas and RIS is 1 meter. The positions of the receiving antennas are varied on the yoz -plane. The QPSK signals generated by USRPs are radiated by the antennas and pass through the RIS before being received by the other set of antennas. The received signals are then demodulated by the USRPs to recover the pictures. The symbol rate in experiments is set as 200 KBaud, which is enough for picture transmissions.

A windowed absorbing screen is placed between the transmitting and receiving antennas, and the RIS is embedded in the window.

a – e Five cases with different frequencies and coding sequences. f The SNR spectra in relation to the radiated power from the transmitting antennas in the five cases. g The SNR spectrum in Case 3 with the radiated power of 0 dBm.

Five different cases are designed to showcase the effectiveness of the filtering RIS, as shown in Fig.  8 a–e. The radiated power from the transmitting antennas is 0 dBm. Settings of the RIS and directions of the receiving antennas are summarized in Table  2 . Photographs of the measurements can be found in Supplementary Information Note  9 . Cases 1 and 2 serve as the control groups. In Case 1, the absorption window with the same size as the RIS is left empty, while in Case 2, a metallic plate is placed inside it. As depicted in Fig.  8a , the demodulated constellation diagrams at both 3.5 GHz and 3.9 GHz are of high quality, and the pictures are satisfactorily recovered, implying that the signals are well received by the antenna through the window. In Case 2, on the contrary, the presence of a metal plate prevents the transmission, as shown in Fig.  8b . In Case 3, the metallic plate is replaced by the RIS, and the coding sequence on it is set to “0000”, indicating that the passband signal (3.5 GHz) should be transmitted through the RIS in a direction perpendicular to the surface. The receiving antennas are placed in the correct direction. As shown in Fig.  8c , the demodulated constellation diagram at 3.5 GHz is of good quality, and the picture is satisfactorily restored, implying that the signal is well received. In Case 4, the coding sequence is changed to “0123”, directing the beam to a 28° angle on the yoz -plane. By repositioning the 3.5-GHz receiving antenna to the correct direction, almost the same satisfactory results are observed, as presented in Fig.  8d . However, if the 3.5-GHz receiving antenna deviates from the expected direction (Case 5), the signal is no longer correctly received, as proved by the cluttered constellation diagram and the unrecovered picture in Fig.  8e . The experiments vividly demonstrate the wave-manipulation capability of the RIS in the passband. In sharp contrast, for the out-of-passband signal (3.9 GHz), regardless of the coding sequence or the receiving antenna’s location on the right side, the picture cannot be restored, and the constellation diagram is cluttered. This strongly suggests that such signals are rejected by the RIS.

To further quantitatively evaluate the performance of the signal transmissions, we calculate the signal-to-noise ratio (SNR) spectrum in relation to the radiated power from the transmitting antenna in the five cases, as shown in Fig.  8f . It is observed that the SNR progressively increases with the rise in the radiated power, indicating an enhancement in transmission through the RIS. For the passband signal (3.5 GHz), the SNRs in Cases 1, 3, and 4 are significantly higher than those in Cases 2 and 5, proving the beam-steering capability of the RIS in the passband. For the out-of-band signal (3.9 GHz), the SNRs in Cases 2 through 5 are considerably lower than those in Case 1, suggesting the blocking effect of the RIS in the stopband. Fig.  8g shows the SNR spectrum in Case 3 using the horn antennas when the coding sequence is “0000” and the radiated power is 0 dBm. It can be seen that the maximum value of 25.7 dB occurs at 3.5 GHz. The value exceeds 23.2 dB from 3.4 to 3.6 GHz, and it drops sharply below 11.6 dB outside the 3.3 to 3.7 GHz range, indicating a frequency window that allows the signals to be transmitted through the RIS efficiently. These results demonstrate the wave-manipulation capability of the RIS in the passband and interference mitigation for adjacent frequencies, aligning with the results presented in Fig.  8 a–e.

Two additional wireless communication experiments are conducted to further illustrate the properties of the RIS. The first experiment is carried out by using the custom-built patch antennas as both transmitting and receiving antennas, and the second experiment is conducted outdoors by replacing the absorbing screen around the RIS with a brick wall. The photographs and results are given in Supplementary Information Note  9 .

We propose a novel RIS that combines the filtering, phase-shifting, and beam-steering functions to assist wireless communications in a target channel and resist interferences from closely adjacent spectra. The presented interference-free characteristic is achieved by the strong frequency selectivity that distinguishes it from conventional ones, which allows only the signals in a specified narrow frequency window while rejecting the out-of-band signals. By adopting the receiver-transmitter structure integrated with the high-Q and phase-shifting functional modules, a RIS prototype is fabricated, and its performances of transmission coefficients and far-field patterns are measured in an anechoic chamber, which are consistent with the theoretical and simulated anticipations. The measured results show a rejection of over 20 dB in the stopbands and a Q factor of 14, a superior frequency-selecting feature that outperforms the current RIS designs. Moreover, the reconfigurable 2-bit phase coding property in the passband enables the dynamic manipulations of the wave propagation, which is validated by the measured far-field patterns. Thereafter, we demonstrate the filtering and beam-controlling effects of the proposed RIS in the wireless communication scenario, where the transmission of color pictures is restricted to the pre-set spatial direction and frequency band. It should be mentioned that beyond this work, the functionality can be further extended, such as the amplitude control, which can be expected if active amplifiers are integrated. The proposed filtering RIS shows impressive anti-interference and beam-controlling capabilities; thus, we believe it can find applications for wireless communications in congested frequency spectra and propagation environments.

Full-wave simulations

The full-wave simulations in this work were performed using commercial software, CST Microwave Studio 2019. An array of 1 × 4 subarrays were considered. The filter chips and PIN diodes in the array were modeled as equivalent RLC circuits, which are described in detail in Supplementary Information Note  3 . Frequency solver and open boundaries were set in the simulations so that the RIS was illuminated by a uniform plane wave polarized along the y -axis. Absorbing materials ( ɛ r  = 2.78, μ r  = 2.8, tan δ e  = 2.47, tan δ m  = 2.45) were enveloped around the RIS to mitigate the mutual interference between the diffracted waves and transmitted waves. In addition, the electric field monitor and the far-field monitor were deployed to obtain the energy distribution in the near-field and far-field regions, respectively.

Field-circuit cosimulations

The cosimulations were performed based on full-wave and circuit simulations. Discrete ports were set for the filter chip and the diodes in the 3D models in the full-wave simulations. After the full-wave simulations, the scattering parameter (S-parameter) file was imported to the circuit simulation environment, where the discrete ports were connected to the filter chip and the diodes. Two microwave sources were connected to the two external ports, respectively, to excite the whole model. To calculate the transmission coefficients, the signals transmitted through the RIS were normalized by the signal transmitted through the same aperture when the RIS was removed.

Measurement setup

The transmission coefficients and far-field properties of the proposed RIS were measured in a microwave anechoic chamber. The setup is shown in Fig.  5 b, c. Two linearly polarized horn antennas were utilized as the transmitting antenna and the receiving antenna, respectively. The transmitting antenna was placed 1.2 meters away from the RIS, connected to port 1 of a vector network analyzer (VNA) (Agilent N5245A). The receiving antenna and the RIS were placed on a rotating platform, and their distance was 9.5 meters. The receiving horn was connected to port 2 of the VNA. To reduce the diffraction around the RIS, a windowed absorbing screen was constructed, and the RIS was embedded within the window. The screen was covered with absorbing foams backed by a metal plate.

For the transmission coefficient measurement, the two horn antennas were placed on the two sides of RIS, oriented in the normal direction. For starters, the reference transmission signals were measured without the RIS. After that, the transmission signals with the presence of the RIS were measured. The RIS’s transmission coefficients were obtained by normalizing the second data with the reference data. For the far-field transmission pattern measurement, the platform rotated with a mechanical turntable from -90° to +90° at increments of 1°. The transmission signals were recorded and normalized by the maximum amplitude, and the far-field patterns were finally plotted.

Data availability

The authors declare that all relevant data are available in the paper and its Supplementary Information Files, or from the corresponding author on request.

Code availability

The custom computer codes utilized during the current study are available from the corresponding authors on request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (62288101, T.J.C. and Q.C., 62101123, L.Z., 61801117, Z.L.), the National Science Foundation (NSFC) for Distinguished Young Scholars of China (62225108, Q.C.), the National Key Research and Development Program of China (2018YFA0701904, Q.C., 2021YFA1401002, L.Z., 2023YFB3811504, L.Z.), the Program of Song Shan Laboratory (Included in the management of Major Science and Technology Program of Henan Province) (221100211300-02, Q.C.), the 111 Project (111-2-05, T.J.C.), the Jiangsu Province Frontier Leading Technology Basic Research Project (BK20212002, T.J.C.), the Natural Science Foundation of Jiangsu Province (BK20221209, Z.L.), the Fundamental Research Funds for the Central Universities (2242022k6003, Q.C., 2242023K5002, L.Z.), the Southeast University - China Mobile Research Institute Joint Innovation Center (R202111101112JZC02, Q.C.), and the National Postdoctoral Program for Innovative Talents (BX2021062, L.Z.), and the Young Elite Scientists Sponsorship Program by CAST (2020QNRC001, L.Z.).

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Authors and Affiliations

State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China

Jing Cheng Liang, Lei Zhang, Zhangjie Luo, Rui Zhe Jiang, Zhang Wen Cheng, Si Ran Wang, Meng Ke Sun, Qiang Cheng & Tie Jun Cui

Institute of Electromagnetic Space, Southeast University, Nanjing, 210096, China

Jing Cheng Liang, Lei Zhang, Zhangjie Luo, Rui Zhe Jiang, Si Ran Wang, Meng Ke Sun, Qiang Cheng & Tie Jun Cui

National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China

Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, 210096, China

Qiang Cheng & Tie Jun Cui

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Contributions

J.C.L., L.Z. and Z.L. conducted the theoretical analysis, modeling, and numerical simulations. J.C.L., L.Z. and Z.L. wrote the paper. J.C.L. and Q.C. proposed the concept of the filtering RIS. J.C.L., R.Z.J., S.R.W. and M.K.S. built the RIS-assisted wireless communication experiment. J.C.L., R.Z.J., Z.W.C. and M.K.S. conducted experiments and data processing. T.J.C., Q.C. and S. J. provided suggestions and comments and helped to organize and revise the draft. All authors discussed the results and contributed to the manuscript.

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Correspondence to Zhangjie Luo , Qiang Cheng or Tie Jun Cui .

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Liang, J.C., Zhang, L., Luo, Z. et al. A filtering reconfigurable intelligent surface for interference-free wireless communications. Nat Commun 15 , 3838 (2024). https://doi.org/10.1038/s41467-024-47865-6

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Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

• Huaming Wu 1 ,
• Xiangyi Li 1 &
• Yingjun Deng 1  

Journal of Cloud Computing volume  9 , Article number:  21 ( 2020 ) Cite this article

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Future wireless communications are becoming increasingly complex with different radio access technologies, transmission backhauls, and network slices, and they play an important role in the emerging edge computing paradigm, which aims to reduce the wireless transmission latency between end-users and edge clouds. Deep learning techniques, which have already demonstrated overwhelming advantages in a wide range of internet of things (IoT) applications, show significant promise for solving such complicated real-world scenarios. Although the convergence of radio access networks and deep learning is still in the preliminary exploration stage, it has already attracted tremendous concern from both academia and industry. To address emerging theoretical and practical issues, ranging from basic concepts to research directions in future wireless networking applications and architectures, this paper mainly reviews the latest research progress and major technological deployment of deep learning in the development of wireless communications. We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing, encoding and decoding, and security and privacy. Main challenges, potential opportunities and future trends in incorporating deep learning schemes in wireless communications environments are further illustrated.

Introduction

Along with the incredible growth of mobile data generated in internet of things (IoT) and the explosion of complicated wireless applications, e.g., virtual reality (VR) and augmented reality (AR), the fifth-generation (5G) technology demonstrates high-dimensional, high-capacity and high-density characteristics [ 1 , 2 ]. Moreover, future wireless communication systems will become ever-more demanding for edge-cloud computing since the edge servers are in proximity of the IoT devices and communicate with them via different wireless communication technologies [ 3 , 4 ]. The requirements of high bandwidth and low latency for wireless communications have posed enormous challenges to the design, configuration, and optimization of next-generation networks (NGN) [ 5 , 6 ]. In the meantime, massive multiple-input multiple-output (MIMO) is widely regarded as a major technology for future wireless communication systems. In order to improve the quality of wireless signal transmission, the system uses multiple antennas as multiple transmitters at the base station (BS) and receivers at a user equipment (UE) to realize the multipath transmitting, which can double the channel capacity without increasing spectrum resources or antenna transmit power. However, conventional communication systems and theories exhibit inherent limitations in the utilization of system structure information and the processing of big data. Therefore, it is urgent to establish new communication models, develop more effective solutions to address such complicated scenarios and further fulfill the requirements of future wireless communication systems, e.g., beyond the fifth-generation (B5G) networks.

Along with the fast convergence of communication and computing in popular paradigms of edge computing and cloud computing [ 7 , 8 ], intelligent communication is considered to be one of the mainstream directions for the extensive development of future 5G and beyond wireless networks, since it can optimize wireless communication systems performance. In addition, with tremendous progress in artificial intelligence (AI) technology, it offers alternative options for addressing these challenges and replacing the design concepts of conventional wireless communications. Deep learning (DL) is playing an increasingly crucial role in the field of wireless communications due to its high efficiency in dealing with tremendous complex calculations, and is regarded as one of the effective tools for dealing with communication issues. Although deep learning has performed well in some IoT applications, “no free lunch” theorem [ 9 ] shows that a model cannot solve all problems once and for all, and we cannot learn a general model for a wide range of communication scenarios. This means that for any particular mobile and wireless network issue, we still need to adopt different deep learning architectures such as convolutional neural networks (CNN), deep neural networks (DNN) and recurrent neural networks (RNN), in order to achieve better performance of the communication systems.

As a classic model of deep learning, autoencoder is widely used in the design paradigms of communication system models. Autoencoder-based wireless communication models are drawing more and more attention [ 10 , 11 , 12 ]. Generative adversarial network (GAN) [ 13 ] is a promising technique, which has attracted great attention in the field of mobile and wireless networking. The architecture of GAN is composed of two networks, i.e., a discriminative model and a generative model, in which a discriminator D is trained to distinguish the real and fake samples, while the generator G is trained to fool the discriminator D with generated samples. The feature of GAN is very appropriate for training. GAN-driven models and algorithms can facilitate the development of next-generation wireless networks, especially coping with the growth in volumes of communication and computation for emerging IoT applications. However, the incorporation of AI technology in the field of wireless communications is still in its early stages, and learning-driven algorithms in mobile wireless systems are immature and inefficient. More endeavors are required to bridge the gap between deep learning and wireless communication research, e.g., customize GAN techniques for network analytics and diagnosis and wireless resource management in heterogeneous mobile environments [ 14 ].

This survey explores the crossovers and the integration of wireless communication and AI technology, aims at solving specific issues in the mobile networking domain, and greatly improve the performance of wireless communication systems. We gather, investigate and analyze latest research works in emerging deep learning methods for processing and transferring data in the field of wireless communications or related scenarios, including strengths and weaknesses. The main focus is on how to customize deep learning for mobile network applications from three perspectives: mobile data generation, end-to-end wireless communications and network traffic control that adapts to dynamic mobile network environments. Several potential deep learning-driven underlying communication technologies are described, which will promote the further development of future wireless communications.

The rest of this paper is organized as follows: we first draw an overall picture of the latest literature on deep learning technologies in the field of wireless communications. Then, we present important open issues and main challenges faced by researchers for intelligent communications. After that, several potential techniques and research topics in deep learning-driven wireless communications are pointed out. Finally, the paper is concluded.

Emerging deep learning Technologies in Wireless Communications

A list of emerging technology initiatives of incorporating AI schemes for communication research is provided by IEEE Communications Society. Footnote 1 This section selects and introduces the latest research progress of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation, channel estimation and compression sensing, encoding and decoding, and security and privacy.

End-to-end communications

The guiding principle in communication system design is to decompose signal processing into chains with multiple independent blocks. Each independent block performs a well-defined and isolated function, such as source coding/decoding, channel coding/decoding, modulation, channel estimation and equalization [ 15 ]. This kind of approach yields today’s efficient, versatile, and controllable wireless communication systems. However, it is unclear whether the optimization of individual processing blocks can achieve optimal end-to-end performance, while deep learning can realize theoretically global optimal performance. Thus, deep learning has produced far-reaching significance for wireless communication systems and has shown promising performance improvements.

As shown in Fig.  1 , an autoencoder consists of an encoder and a decoder, where the input data is first processed by the encoder at the transmitter, and then it is decoded at the receiver in order to get the output. The transmitter encodes the input s as a one-hot vector, and a conditional probability density function p ( y|x ) is applied to indicate the wireless channel. After receiving the message, the receiver selects the one with the maximum probability over all possible messages as the output \( \hat{s} \) [ 10 ]. Autoencoder is mainly constructed by neural networks, i.e., an encoding network and a decoding network, the wireless communication system is divided into multiple physical layers to facilitate the propagation of information via the neural network thereon.

Autoencoder-based communication systems

In addition, the idea of end-to-end learning in communication systems has also attracted widespread attention in the wireless communications community [ 16 ]. Several emerging trends for deep learning in communication physical layer were elaborated in [ 10 ]. By treating the wireless communication system as an autoencoder, redefining it as the transmitter and receiver, a local optimum of the end-to-end refactoring process can be achieved. Moreover, different conditions were set in the physical layer to simulate different transmission environments in reality.

The design paradigms of conventional wireless communication systems have to consider the influence of various uncertain factors in hardware implementation, and compensate for delay and phase, which is not efficient and scalable. In contrast, model-free training of end-to-end communication systems based on autoencoder was built by hardware implementations on software-defined radios (SDRs) [ 17 , 18 ], which was simpler, faster, and more efficient. Furthermore, the first entire neural network-based communication system using SDRs was implemented in [ 19 ], where an entire communication system was solely composed of neural networks for training and running. Since such a system fully considered time-varying in the actual channel, its performance was comparable to that of existing wireless communication systems.

A conditional generative adversarial network (CGAN) was applied in [ 20 ] to construct an end-to-end wireless communication system with unknown channel conditions. The encoded signal for transmitting was treated as condition information, and the transmitter and receiver of the wireless communication system were each replaced by a DNN. CGAN acted as a bridge between the transmitter and the receiver, allowing backpropagation to proceed smoothly, thereby jointly training and optimizing both the transmitter and receiver DNNs. This approach makes a significant breakthrough in the modeling mode of conventional wireless communications and opens up a new way for the design of future wireless communication systems.

Signal detection

Deep learning-based signal detection is getting more and more popular. Unlike the conventional model-based detection algorithms that rely on the estimation of the instantaneous channel state information (CSI) for detection, the deep learning-based detection method does not require to know the underlying channel model or the knowledge of the CSI when the channel model is known [ 21 ]. A sliding bidirectional recurrent neural network (SBRNN) was proposed in [ 22 ] for signal detection, where the trained detector was robust to changing channel conditions, eliminating the requirement for instantaneous CSI estimation.

Unlike traditional orthogonal frequency-division multiplexing (OFDM) receivers that first estimate the CSI explicitly, and then the estimated CSI is used to detect or restore the transmitted symbols, the deep learning-based method in [ 23 ] estimated the CSI implicitly and then recovered the transmitted signals directly. The estimated CSI was to solve the problem that a large amount of training data and high training cost were required due to a large increase in the number of parameters caused by DNNs.

Some recent works have suggested the use of DNNs in the context of MIMO detection and have developed model-driven deep learning networks for MIMO detection. For example, a network specifically designed for MIMO communication [ 24 ] can cope with time-varying channel in only one training phase. Instead of addressing a single fixed channel, a network obtained by unfolding the iterations of a projected gradient descent algorithm can handle multiple time-invariant and time-varying channels simultaneously in a single training phase [ 25 ]. Deep learning-based networks as demonstrated in [ 26 ] can reach near-optimal detection performance, guaranteed accuracy and robustness with low and flexible computational complexity.

Channel estimation and compression sensing

Channel estimation and compression sensing are key technologies for the real-time implementation of wireless communication systems. Channel estimation is the process of estimating the parameters of a certain channel model from the received data, while compression sensing is a technique to acquire and reconstruct sparse or compressible signals. Deep learning-based channel estimation and compression sensing methods have been suggested in several recent works [ 27 , 28 , 29 , 30 ].

To tackle the challenge of channel estimation when the receiver is equipped with a limited number of radio frequency (RF) chains in massive MIMO systems, a learned denoising-based approximate message passing (LDAMP) network was exploited in [ 27 ], where the channel structure can be learned and estimated from a large amount of training data. Experiment results demonstrated that the LDAMP network significantly outperforms state-of-the-art compressed sensing-based algorithms.

Motivated by the covariance matrix structure, a deep learning-based channel estimator was proposed in [ 28 ], where the estimated channel vector was a conditional Gaussian random variable, and the covariance matrix was random. Assisted by CNN and the minimum mean squared error (MMSE) estimator, the proposed channel estimator can ensure the state-of-the-art accuracy of channel estimation at a very lower computational complexity.

The basic architecture of deep learning-based CSI feedback is as shown in Fig.  2 . Recently, more and more researchers have focused on the benefits of CSI feedback that the transmitter can utilize it to precode the signals before the transmission, thus we can gain the improvement of MIMO systems. The precoding technique can help to realize the high quality of restoring signals and are widely adopted in wireless communication systems. By exploiting CSI, the MIMO system can substantially reduce multi-user (MU) interference and provide a multifold increase in cell throughput. In the network of frequency division duplex (FDD) or time division duplex (TDD), the receiver UE can estimate the downlink CSI and transmit it back to the BS once they obtain it and help BS to perform precoding for the next signal. BS can also obtain the uplink CSI to help rectify the transmission at UE. The procedure of CSI feedback transmitting has drawn much attention, since high quality reconstructed CSI received by BS guarantees a good precoding, improving the stability and efficiency of the MIMO system.

Deep learning-based CSI feedback

Inspired by traditional compressed sensing technologies, a new CNN-based CSI sensing and recovery mechanism called CsiNet was proposed in [ 29 ], which effectively used the feedback information of training samples to sense and recover CSI, and achieved the potential benefits of a massive MIMO. The encoder of CsiNet converted the original CSI matrix into a codebook using CNN, and then the decoder restored the received codebook to the original CSI signal using the fully-connected network and refine networks.

To further improve the correctness of CSI feedback, a real-time long short-term memory (LSTM) based CSI feedback architecture named CsiNet-LSTM was proposed in [ 31 ], where CNN and RNN are applied to extract the spatial and temporal correlation features of CSI, respectively. Using time-varying MIMO channel time correlation and structural features, CsiNet-LSTM can achieve a tradeoff between compression ratio, CSI reconstruction quality, and complexity. Compared to CsiNet, the CsiNet-LSTM network can trade time efficiency for CSI reconstruction quality. Further, the deep autoencoder-based CSI feedback in the frequency division duplex (FDD) massive MIMO system was modelled in [ 30 ], which involved feedback transmission errors and delays.

As shown in Fig.  3 , a novel effective CSI sensing and recovery mechanism in the FDD MIMO system was proposed in our previous work [ 32 ], referred to as ConvlstmCsiNet, which takes advantage of the memory characteristic of RNN in modules of feature extraction, compression and decompression, respectively. Moreover, we adopt depthwise separable convolutions in feature recovery to reduce the size of the model and interact information between channels. The feature extraction module is also elaborately devised by studying decoupled spatio-temporal feature representations in different structures.

The architecture of ConvlstmCsiNet with P3D block [ 32 ]

Encoding and decoding

In digital communications, source coding and channel coding are typically required in data transmission. Deep learning methods have been suggested in some recent works [ 33 , 34 , 35 , 36 , 37 , 38 ] that can be used to improve standard source decoding and solve the problem of high computational complexity in channel decoding.

A DNN-based channel decoding method applied in [ 33 ] can directly realize the conversion from receiving codewords to information bits when considering the decoding part as a black box. Although this method shows advantages in performance improvement, learning is constrained with exponential complexity as the length of codewords increases. Therefore, it is neither fit for random codes, nor for codewords with long code lengths.

The issue of joint source encoding and channel encoding of structured data over a noisy channel was addressed in [ 38 ], a lower word error rate (WER) was achieved by developing deep learning-based encoders and decoders. This approach was optimal in minimizing end-to-end distortion where both the source and channel codes have arbitrarily large block lengths, however, it is limited in using a fixed length of information bits to encode sentences of different lengths.

Belief propagation (BP) algorithm can be combined with deep learning networks for channel decoding. Novel deep learning methods were proposed in [ 36 , 37 ] to improve the performance of the BP algorithm. It demonstrated that the neural BP decoder can offer a tradeoff between error-correction performance and implementation complexity, but can only learn a single codeword instead of an exponential number of codewords. Neural network decoding was only feasible for very short block lengths, since the training complexity of deep learning-based channel decoders scaled exponentially with the number of information bits and. A deep learning polarization code decoding network with partitioned sub-blocks was proposed in [ 34 ] to improve its decoding performance for high-density parity check (HDPC) codes. By dividing the original codec into smaller sub-blocks, each of which can be independently encoded/decoded, it provided a promising solution to the dimensional problem. Furthermore, Liang et al. [ 35 ] proposed an iterative channel decoding algorithm BP-CNN, which combined CNN with a standard BP decoder to estimate information bits in a noisy environment.

Security and privacy

Due to the shared and broadcast nature of wireless medium, wireless communication systems are extremely vulnerable to attacks, counterfeiting and eavesdropping, and the security and privacy of wireless communications have received much attention [ 39 , 40 ]. Moreover, wireless communication systems are becoming increasingly complex, and there is a close relationship between various modules of the system. Once a module is attacked, it will affect the operation of the entire wireless communication system.

Running AI functions on nearby edge servers or remote cloud servers is very vulnerable to security and AI data privacy issues. Thus, offloading AI learning models and collected data to external cloud servers for training and further processing may result in data loss due to the user's reluctancy of providing sensitive data such as location information. Many research efforts have focused on bridging DL and wireless security, including adversarial DL techniques, privacy Issues of DL solutions and DL hardening solutions [ 41 , 42 ], to meet critical privacy and security requirements in wireless communications.

Conventional wireless communication systems generally suffer from jamming attacks, while autoencoder-based end-to-end communication systems are extremely susceptible to physical adversarial attacks. Small disturbances can be easily designed and generated by attackers. New algorithms for making effective white-box and black-box attacks on a classifier (or transmitter) were designed in [ 43 , 44 ]. They demonstrated that physical adversarial attacks were more destructive in reducing the transmitter’s throughput and success ratio when compared to jamming attacks. In addition, how to keep security and enhance the robustness of intelligent communication systems is still under discussion. Defense strategies in future communication systems are still immature and inefficient. Therefore, further research on the defense mechanisms of adversarial attacks and the security and robustness of deep learning-based wireless systems is very necessary.

One possible defense mechanism is to train the autoencoder to have an antagonistic perturbation, which is a technique that enhances robustness, known as the adversarial training [ 45 ]. Adversarial deep learning is applied in [ 46 ] to launch an exploratory attack on cognitive radio transmissions. In a canonical wireless communication scenario with one transmitter, one receiver, one attacker, and some background traffic, even the transmitter’s algorithm is unknown to the attacker, it can still sense a channel, detect transmission feedback, apply a deep learning algorithm to build a reliable classifier, and effectively jam such transmissions. A defense strategy against an intelligent jamming attack on wireless communications was designed in [ 47 ] to successfully fool the attacker into making wrong predicts. To avoid the inaccurate learned model due to interference of the adversary, one possible way is to use DNNs in conjunction with GANs for learning in adversarial radio frequency (RF) environments, which are capable of distinguishing between adversarial and trusted signals and sources [ 48 ].

Open challenges

This section discusses several open challenges of deep learning-driven wireless communications from the aspects of baseline and dataset, model compression and acceleration, CSI feedback and reconstruction, complex neural networks, training at different SNRs and fast learning.

Baseline and dataset

The rapid development of computer vision, speech recognition, and natural language processing have benefited most from the existence of many well-known and effective datasets in computer science, such as ImageNet [ 49 ] and MNIST [ 50 ]. For fairness, performance comparisons between different approaches should be performed under the same experimental environment by using common datasets. In order to compare the performance of newly proposed deep learning models and algorithms, it is critical to have some well-developed algorithms serving as benchmarks. Experiment results based on these benchmarks are usually called baselines, which are very important to show the development of a research field [ 51 ]. The quality and quantity of open datasets will have a huge impact on the performance of deep learning-based communication systems.

Wireless communication systems involve inherently artificial signals that can be synthesized and generated accurately, the local bispectrum, envelope, instantaneous frequency, and symbol rate of the signal can be used as input features. Therefore, in some cases, we should pay more attention to the standardization of data generation rules rather than the data itself.

In the field of intelligent wireless communications, however, there are few existing and public datasets that can be directly applied for training. It is necessary to either create generic and reliable datasets for different communication problems or develop new simulation software to generate datasets in various communication scenarios. On the basis of such dataset or data generation software, widely used datasets similar to ImageNet and MNIST can be created. Then, we can treat them as baselines or benchmarks for further comparison and research.

Model compression and acceleration

Deep neural networks (DNN) have achieved significant success in computer vision and speech recognition, in the meanwhile, their depth and width are still boosting, which lead to a sharp increase in the computational complexity of networks. At present, the number of parameters in DNN models is very huge (parameters are generally tens of millions to hundreds of millions) and thus the amount of calculation is extremely large. Current deep learning models either rely on mobile terminals or edge-cloud server to run AI functions and are under tremendous pressure in terms of high data storage and processing demands [ 41 ]. Offloading complex compute tasks from mobile terminals to a central cloud with AI functions can alleviate the limitation of computation capacity, but also results in high latency for AI processing due to long-distance transmissions. Therefore, it is not appropriate to offload AI learning model to the central cloud server, especially for data-intensive and delay-sensitive tasks.

Some deep learning algorithms deployed on mobile terminals can only rely on cloud graphic processing units (GPUs) to accelerate computing, however, the wireless bandwidth, the communication delay, and the security of cloud computing will incur enormous obstacles. The large memory and high computational consumption required by the DNN greatly restricts the use of deep learning on mobile terminals with limited resources. Deep learning-based communication systems are also difficult to deploy on small mobile devices such as smartphones, smartwatches and tablets.

Due to the huge redundancy of the parameters in DNN models, these models can be compressed and accelerated to build a lightweight network, which is an inevitable trend in the development of related technologies in the future. Methods like low-rank factorization, parameter pruning and sharing, quantization, and knowledge distillation can be applied in DNN models. Specifically, on the one hand, it is possible to consider quantifying the parameters of DNN models to further compress the network model; on the other hand, channel pruning and structured sparse constraints can be applied to eliminate part of the redundant structure and accelerate the calculation speed [ 52 ].

Lightweight AI engines at the mobile terminals are required to perform real-time mobile computing and decision making without the reliance of edge-cloud servers, where the centralized model is stored in the cloud server while all training data is stored on the mobile terminals. In addition, learning parameter settings or updates are implemented by local mobile devices. In some cases, if the floating-point calculation or storage capacity of the network model is greatly reduced, but the performance of the existing DNNs remains essentially unchanged, such a network model can run efficiently on resource-constrained mobile devices.

CSI feedback and reconstruction

The massive multiple-input multiple-output (MIMO) system is usually operated in OFDM over a large number of subcarriers, leading to a problem of channel state information (CSI) feedback overload. Moreover, in order to substantially provide a multifold increase in cell throughput, each base station is equipped with thousands of antennas in a centralized or distributed manner [ 29 ]. Therefore, it is crucial to utilize the available CSI at the transmitter for precoding to improve the performance of FDD networks [ 32 ]. However, compressing a large amount of CSI feedback overload in massive MIMO systems is very challenging. Traditional estimation approaches like compressive sensing (CS) can only achieve poor performance on CSI compression in real MIMO system due to the harsh preconditions.

Although DL-based CSI methods outperform much than the CS ones, the price of training cost remains high, which requires large quantities of channel estimates. Once the wireless environment changes significantly, a trained model still has to be retrained [ 53 ]. In addition, a more able and efficient structure of DNN is needed. The design of CSI feedback link and precoding mode still remains an open issue that different MIMO systems should adopt their own appropriate designed CSI feedback link and precoding manner. Furthermore, DL-based CSI feedback models are still immature when adopted in real massive MIMO systems and suffer constraints of realistic factors, e.g., time-varying channel with fading, SRS measurement period, channel capacity limitation, hardware or device configuration, channel estimation and signal interference in MU systems. These challenges may hinder the general applications temporarily and will be addressed by future DL-based models with a more exquisite and advanced architecture.

Complex neural networks

Due to the widely used baseband representations in wireless communication systems, data is generally processed in complex numbers, and most of the associated signal processing algorithms rely on phase rotation, complex conjugate, absolute values, and so on [ 10 ].

Therefore, neural networks have to run on complex values rather than real numbers. However, current deep learning libraries usually do not support complex processing. While complex neural networks may be easier to train and consume less memory, they do not provide any significant advantages in terms of performance. At present, we can only think of a complex number as a real number and an imaginary number. Complex neural networks that are suitable for wireless communication models should be developed.

Training at different SNRs

Up to now, it is still not clear which signal-to-noise (SNR) ratio the deep learning model should be trained on. The ideal deep learning model should be applied to any SNR regardless of the SNR used for training or the range of SNR it is in. In fact, however, this is not the case. The results of training deep learning models under certain SNR conditions are often not suitable for other SNR ranges [ 10 ].

For example, training at lower SNRs does not reveal important structural features of wireless communication systems at higher SNRs, and similarly, training at higher SNRs can not reveal important structural features of wireless communication systems at lower SNRs. Training the deep learning model across different SNRs can also seriously affect the training time. In addition, how to construct an appropriate loss function, how to adjust parameters and data representation for wireless communication systems are still big problems that must be solved.

Fast learning

For end-to-end training of wireless communication systems including encoders, channels, and decoders, a specific channel model is usually required. The trained model needs to be applied to its corresponding channel model, otherwise, mismatch problems will occur, which will cause severe degradation of system performance.

In real-world scenarios, however, due to many environmental factors, the channel environment often changes at any time and place, e.g., the change of the movement speed and direction of user terminals, the change of the propagation medium, the change of the refractive scattering environment. Once the channel environment changes, a large amount of training data is needed to retrain, which means that for different channel environments at each moment, such repeated training tasks need to be performed, which consumes resources and weakens the performance of the system.

Retraining is required when the system configuration changes because the system model does not have a good generalization ability. Adaptation is done on a per-task basis and is specific to the channel model [ 54 ]. Some changes in the channel environment may lead to a sharp decline in system performance. Therefore, we need to seek systems with stronger generalization ability, in order to adapt to the changing channel environment.

Potential opportunities

This section mainly describes the profound potential opportunities and the promising research directions in wireless communications assisted by the rapid development of deep learning.

Deep learning-driven CSI feedback in massive MIMO system

Recent researches indicate that applying deep learning (DL) in MIMO systems to address the nonlinear problems or challenges can indeed boost the quality of CSI feedback compression. Different from the traditional CS-based approaches, DL-based CSI methods adopt several neural network (NN) layers as an encoder replacing the CS model to compress CSI as well as a decoder to recover the original CSI, which can speed up the transmitting runtime nearly 100 times of CS ones.

The structure of autoencoder-based MIMO systems is depicted in Fig.  4 , which only considers the downlink CSI feedback process, assuming that the feedback channel is perfect enough to transmit CSI with no impairments. In fact, a large part of the overload CSI serves redundant and the CSI matrix falls to sparse in the delay domain. In order to remove the information redundancy, CNNs are applied here, which has the ability to eliminate the threshold of domain expertise since CNNs use hierarchical feature extraction, which can effectively extract information and obtain increasingly abstract correlations from the data while minimizing data preprocessing workload.

The structure of autoencoder-based MIMO systems with downlink CSI feedback

We can consider both the issues of feedback delay and feedback errors. Assume that one signal is transmitted into n time slots due to the restriction of downlink bandwidth resource, thus demanding a n -length time series of CSI feedback estimation within a signal transmitting period and the SRS measurement period. The time-varying channel is also under the condition of known overdue CSI or partial CSI characteristics, such as Doppler or beam-delay information. Furthermore, the feedback errors from MU interference brought by multiple UE at middle or high moving speed are also taken into account. When transmitting the compressed CSI feedback, the imperfections, e.g. additive white Gaussian noise (AWGN), in uplink CSI feedback channel would also bring feedback errors. The model is trained to minimize the feedback errors via the minimum mean square error (MMSE) detector.

The architecture of DL-based autoencoder in CSI feedback compression is also advanced via taking the advantages of RNN’s memory characteristic to deal with the feature extraction in time-varying channel, which can have an active effect on time correlation exploring and better performance on CSI recovery [ 30 ]. Similarly, a DL-based autoencoder of CSI estimation method can be applied in this MIMO system, which is exposed to more practical restrictions.

In the future, we can use DL methods of CSI feedback with time-varying channel in massive MU-MIMO system to improve the compression efficiency and speed up the transmitting process, as well as develop novel theoretical contributions and practical research related to the new technologies, analysis and applications with the help of CNN and RNN.

GAN-based Mobile data augmentation

Mobile data typically comes from a variety of sources with various formats and exhibits complex correlations and heterogeneity. According to the mobile data, conventional machine learning tools require cumbersome feature engineering to make accurate inferences and decisions. Deep learning has eliminated the threshold of domain expertise because it uses hierarchical feature extraction, which can effectively extract information and obtain increasingly abstract correlations from the data while minimizing data pre-processing workload [ 55 ]. However, inefficiency in training time is an enormous challenge when applying learning algorithms in wireless systems. Traditional supervised learning methods, which learn a function that maps the input data to some desired output class label, is only effective when sufficient labeled data is available. On the contrary, generative models, e.g., GAN and variational autoencoder (VAE), can learn the joint probability of the input data and labels simultaneously via Bayes rule [ 56 ]. Therefore, GANs and VAEs are well suitable for learning in wireless environments since most current mobile systems generate unlabeled or semi-labeled data.

GANs can be used to enhance the configuration of mobile and wireless networks and help address the growth of data volumes and algorithm-driven applications to satisfy the large data needs of DL algorithms. GAN is a method that allows exploiting unlabeled data to learn useful patterns in an unsupervised manner. GANs can be further applied in B5G mobile and wireless networks, especially in dealing with heterogeneous data generated by mobile environments.

As shown in Fig.  5 , the GAN model consists of two neural networks that compete against each other. The generator network tries to generate samples that resemble the real data such that the discriminator cannot tell whether it is real or fake. After training the GAN, the output of the generator is fed to a classifier network during the inference phase. We can use GAN to generate real data according to previously collected real-world data. Furthermore, it can be used for path planning, trajectories analysis and mobility analysis.

GAN-based mobile data generation

Monitoring large-scale mobile traffic is, however, a complex and costly process that relies on dedicated probes, which have limited precision or coverage and gather tens of gigabytes of logs daily [ 57 ]. Heterogeneous network traffic control is an enormous obstacle due to the highly dynamic nature of large-scale heterogeneous networks. As for a deep learning system, it has difficulty in characterizing the appropriate input and output patterns [ 58 ].

GANs can be applied in resource management and parameter optimization to adapt to the changes in the wireless environment. To make this happen, intelligent control of network traffic can be applied to infer fine-grained mobile traffic patterns, from aggregate measurements collected by network probes. New loss functions are required to stabilize the adversarial model training process, and prevent model collapse or non-convergence problems. Further, data processing and augmentation procedure are required to handle the insufficiency of training data and prevent the neural network model from over-fitted.

Deep learning-driven end-to-end communication

The purpose of autoencoder is to make the input and the output as similar as possible, which is achieved by performing backpropagation of the error and continuing optimization after each output. Similarly, a simple wireless communication system consists of a transmitter (encoder), a receiver (decoder) through a channel, and an abundant of physical layer transmission technologies can be adopted in the wireless communication process. A communication system over an additive white gaussian noise (AWGN) or Rayleigh fading channel can be represented as a particular type of autoencoder. The purpose of wireless communication is to make the output signal and the input signal as similar as possible. However, how to adapt an end-to-end communications system trained on a statistical model to a real-world implementation remains an open question.

As shown in Fig.  6 , we can extend the above single channel model to two or more channels, where multiple transmitter and multiple receivers are competing for the channel capacity. As soon as some of the transmitters and receivers are non-cooperative, adversarial training strategies such as GANs could be adopted. We can perform joint optimization for common or individual performance metrics such as block error rate (BLER). However, how to train two mutually coupled autoencoders is still a challenge. One suggestion is to assign dynamic weights to different autoencoders and minimize the weighted sum of the two loss functions.

Autoencoder-based MIMO System

The diagram of the energy-based generative adversarial network (EBGAN) [ 59 ] in wireless communications is depicted in Fig.  7 . We use an encoder instead of a transmitter, and a decoder instead of a receiver for intelligent communications. The generative network is applied to generate the canonicalized signal, and then fed into the discriminative network for further classification. Inverse filtering can be applied to simplify the task of the learned discriminative network. Similarly, the purpose of EBGAN-based end-to-end communication is to make the output signal and the input signal as close as possible.

EBGAN with an autoencoder discriminator in wireless communications

The discriminator D is structured as an autoencoder:

where Enc (·) and Dec (·) denote the encoder function and decoder function, respectively.

Given a positive margin m, a data sample x and a generated sample G ( z ), the discriminator loss LD and the generator loss LG are formally defined by:

where {·} + is the hinge loss function, the generator is trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to generated samples [ 59 ].

Most mathematical models in wireless communication systems are static, linear, and Gaussian-compliant optimization models. However, a realistic communication system has many imperfect and non-linear problems, e.g., nonlinear power amplifiers, which can only be captured by these models. The EBGAN-based wireless communication system no longer requires a mathematical linear processing model that can be optimized for specific hardware configurations or spatially correlated channels. With the help of EBGAN, we can learn about the implementation details of the transmitter and receiver and even the information coding without any prior knowledge.

Meta-learning to wireless communication

In real-world scenarios, it is not worthwhile to perform multi-tasks training from scratch just because of different channel models, because these tasks are closely related, they share the same encoder and decoder network structure, and their parameter changes are only affected by the channel model. Training from scratch is under the assumption that such tasks are completely independent and cannot make full use of the connections, resulting in many repetitive and redundant training steps, however, it is not true.

Meta-learning, or learning to learn [ 60 ], that is, to make the model a learner. It learns a priori knowledge in multi-tasking and then quickly applies it to the learning of new tasks, so that fast learning and few-shot learning can be realized. Meta-learning provides a way to perform multi-task learning and optimizes the system parameters toward a common gradient descent direction during training, thereby achieving the optimal generalization ability and reduced training data and/or time complexity. In the meantime, when a new task arrives, the system can train on a few rounds of iterative (or even one round of iterative) with very little training data, so that the parameters can be dynamically fine-tuned on the basis of the original learning model to adapt to the new channel model, where the dynamic parameter tuning is possible. Thus, meta-learning can be implemented for end-to-end learning of encoder and decoder with unknown or changing wireless channels, and it outperforms conventional training and joint training in wireless communication systems [ 54 ].

A specific example of meta-training methods known as model agnostic meta-learning (MAML) [ 61 ]. Its core idea is to find a common initialization point that allows for a quick adaptation towards the optimal performance on the new task. MAML updates parameters through one or more stochastic gradient descent (SGD) steps, which are calculated using only a small amount of data from the new task. Therefore, instead of training a common system model for all channel models, we can apply MAML to find a common initialization vector so that it supports fast training on any channel [ 54 ].

Several recent efforts have focused on intelligent communications to harvest remarkable potential benefits. We have mainly discussed the potential applicability of deep learning in the field of wireless communications for edge-cloud computing, such as model-free training method for end-to-end wireless communications, and further demonstrated their superior performance over conventional wireless communications. Implementation of many emerging deep learning technologies are still in the preliminary stage, and profound potential solutions to solving wireless communication problems have to be further studied. This survey attempts to summarize the research progress in deep learning-driven wireless communications and point out existing bottlenecks, future opportunities and trends.

In the research of B5G wireless networks and communication systems, the low efficiency of training time is a bottleneck when applying learning algorithms in wireless systems. Although deep learning is not mature in wireless communications, it is regarded as a powerful tool and hot research topic in many potential application areas, e.g., channel estimation, wireless data analysis, mobility analysis, complicated decision-making, network management, and resource optimization. It is worthwhile to investigate the use of deep learning techniques in wireless communication systems to speed up the training process and develop novel theoretical contributions and practical research related to the new technologies, analysis and applications for edge-cloud computing.

Availability of data and materials

Machine Learning For Communications Emerging Technologies Initiative https://mlc.committees.comsoc . org/research-library.

Abbreviations

Artificial intelligence

Augmented reality

Additive white gaussian noise

Block error rate

Belief propagation

Base station

Beyond the fifth-generation

Conditional generative adversarial network

Convolutional neural network

Channel state information

Deep neural network

Energy-based generative adversarial network

Frequency division duplex

Generative adversarial network

Graphics processing unit

High-density parity check

Learned denoising-based approximate message passing

Long short-term memory

Agnostic meta-learning

Minimum mean squared error

Multiple-input multiple-output

Next-generation network

Orthogonal frequency-division multiplexing

Radio frequency

Recurrent neural network

Software-defined radio

Sliding bidirectional recurrent neural network

Stochastic gradient descent

Time division duplex

• Internet of things

User Equipment

Virtual reality

Word error rate

Fifth-Generation

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Acknowledgements

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

Authors’ informations

Huaming Wu received the B.E. and M.S. degrees from Harbin Institute of Technology, China in 2009 and 2011, respectively, both in electrical engineering. He received the Ph.D. degree with the highest honor in computer science at Free University of Berlin, Germany in 2015. He is currently an associate professor in the Center for Applied Mathematics, Tianjin University. His research interests include mobile cloud computing, edge computing, fog computing, internet of things (IoTs), and deep learning.

Xiangyi Li received the B.S. in Applied Mathematics from Tianjin University, China. She is currently a M.S. student majoring in applied mathematics at Tianjin University, China. Her research interests include deep learning, wireless communications and generative models.

Yingjun Deng received the B.S. in Applied Mathematics (2009) and M.S. in Computational Mathematics (2011) from Harbin Institute of Technology, China. He got his Ph.D. in Systems Optimization and Dependability from Troyes University of Technology, France in 2015. He worked as a postdoctoral fellow, respectively at University of Waterloo in Canada (2015–2016), and Eindhoven University of Technology in Netherlands (2018–2019). He became a lecturer since 2016 in the Center for Applied Mathematics, Tianjin University, China. His research interests include applied statistics, deep learning, prognostic and health management, and predictive maintenance.

This work is partially supported by the National Key R & D Program of China (2018YFC0809800), the National Natural Science Foundation of China (61801325), the Huawei Innovation Research Program (HO2018085138), the Natural Science Foundation of Tianjin City (18JCQNJC00600), and the Major Science and Technology Project of Tianjin (18ZXRHSY00160).

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HW designed the survey and led the write up of the manuscript. XL contributed part of the writing of the manuscript. YD took part in the discussion of the work described in this paper. All authors have read and approved the manuscript.

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Wu, H., Li, X. & Deng, Y. Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges. J Cloud Comp 9 , 21 (2020). https://doi.org/10.1186/s13677-020-00168-9

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Currently, IMT 2030 proposes trustworthiness as a new characteristic in the 6G vision, and Huawei mentions native trustworthiness for 6G technology and requirements. In fact, various standard organizations, such as 3GPP, ETSI, and IEEE have been working on trustworthiness topics. Meanwhile, the world's major communications companies, including China Mobile, Nokia, Ericsson, etc., have clearly stated the need for 6G trustworthiness in their 6G initiatives, proposals, and white papers. Furthermore, many researchers have published technical work on the definition, generation, protection, and optimization of trustworthiness. All of these indicate that trustworthiness will become an indispensable key feature in 6G.

First, as a new characteristic, how to define trustworthiness in 6G is an open issue. Can trustworthiness be straightforwardly equated with safety, security, privacy, reliability, and resilience, or is it characterized by behaving exactly as expected? Second, what key performance indicators are typically suitable for trustworthiness and how to rate trustworthiness precisely. These challenges have not been well addressed. Lastly, due to the diversity of the concept of trustworthiness in academia and industry, there are many views on theories, technologies, and applications for trustworthiness in 6G, which have not yet reached a consensus to form a clear and systematic guide for the coming 6G era. The objective of this Special Issue (SI) is to enable both academic and industry researchers to present their research on trustworthy 6G. The SI also seeks to identify new application areas within this developing field and strongly encourages original research articles related to this topic, as well as high-quality review articles describing the current state of the art. Potential topics of interest include but are not limited to the following:

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Information and Communications Technology (ICT) has significantly progressed in recent years by relying heavily on wireless communications. Currently, most of the devices and systems are connected via wireless technologies and numerous emerging solutions and applications are proposed based on wireless communications. Further, with the introduction of concepts such as Internet of Things (IoTs), nearly countless number of devices will be connected in a global network through wireless interfaces, using standard protocol solutions. The connectivity of these plethora of devices will be heavily relying on wireless communications and therefore, the challenges such as scalability, reliability, signal coexistence, data rate and energy consumption faced by the existing systems need to be thoroughly analyzed. In addition, the proposal for 5G technologies aims to provide very high data-rates, massive number of devices connectivity, very high reliability and low latencies. Therefore, efforts are underway to consider beyond state of-the-art protocols and mechanisms for wireless communication. Thus, the next generation of wireless communication is expected to meet the demands of various challenging use cases that go far beyond distribution of voice, video and data. Therefore, this special issue aims to present the solutions that address the challenges wireless communication systems are facing.

Therefore, European Alliance for Innovation (EAI) took a step towards the realization of Future Intelligent Vehicular Technologies based on dependable and real-time communication and invite both academic and industrial research community by organizing the 2nd edition of Future 5 V conference in Islamabad, Pakistan. Future 5 V is an annual international conference by EAI (European Alliance for Innovation) and co-sponsored by Springer. Future 5V received more than 150 research articles in field of Vehicular networks/communications covering theory and practices in the after mentioned field of study. The call-for-papers of this SI was an outcome of the 2st EAI International Conference on Future Intelligent Vehicular Technologies and open submission. Following are the details of the accepted papers in this special issue.

The first paper is “The Model Design of Mobile Resource Scheduling in Large Scale Activities” by Cai-Qiu Zhou et al. In this paper, the authors have presented work on multi-resource scheduling model based on Dijkstra and multi-ant colony optimization algorithms. The concept model of mobile resource scheduling is put forward, and the detailed introduction of each dimension is presented and multi-resource scheduling model based on Dijkstra and multi-ant colony optimization algorithms are established respectively. Furthermore, the advantages and disadvantages of the two algorithms are compared through the numerical examples. It has been presented that Dijkstra algorithm is superior to multi-ant colony optimization algorithm in the cost control, but in the running time of the algorithm, multi-ant colony optimization algorithm is better than Dijkstra algorithm. The research has practical significance for the development of scientific and effective population service operation plan and service management plan for large scale exhibition activities.

The next accepted paper is “A Novel On-Line Association Algorithm for Supporting Load Balancing in Multiple-AP Wireless LAN” by Liang Sun et al. It has been presented that wireless LAN has become the most widely deployed technology in mobile devices for providing Internet access. Operators and service providers remarkably increase the density of wireless access points in order to provide their subscribers with better connectivity and user experience. As a result, WLAN users usually find themselves covered by multiple access points and have to decide which one to associate with. The authors have proposed a novel on-line association algorithm to deal with any sequence of STAs during a long-term time such as one day. The performance of the proposed algorithm is evaluated through simulation and experiments. Simulation results show that our algorithm improves the overall WLAN throughput by up to 37%, compared with the conventional RSSI-based approach. The presented algorithm also performs better than SSF (Strongest Signal First) and LAB (Largest Available Bandwidth) in the experiments.

The next paper is “GCC: Group-based CSI Feedback Compression for MU-MIMO Networks” by Jian Fang et al. The Multi-user Multiple Input Multiple Output networks (MU-MIMO) adopts beamforming to enable Access Point (AP) to transmit packets concurrently to multiple users, which brings formidable overhead. The overhead of collecting Channel State Information (CSI) feedback matrix may even overwhelm real-data transmission when the scale of network is large, which incurs unsatisfactory performance and huge waste of resources. Therefore, in this paper, the authors have addressed this problem using GCC which is a Group-based CSI feedback Compression scheme for MU-MIMO networks. It enables users to feedback their CSI in terms of group determined by their location. The GCC limit the quantity of CSI feedback in each transmission round regardless of the size of network by allowing the location-related users to share a CSI matrix. In addition, the authors have used a novel metric to do the tradeoff between throughput and capacity loss of the system. GCC has been tested in different scenarios and compared it with existing works. The evaluation result showed that GCC achieved much higher throughput and is robust to various situations.

Since the The growth and adoption of the Internet of Things (IoT) is increasing day by day and it has become a very hot research topic. We have considered a paper for publication that has presented a Framework for trust management in IoT. The paper is tittled “Clustering-Driven Intelligent Trust Management Methodology for The Internet of Things” by Mohammad Dahman Alshehri et al. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. The authors have proposed a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.

A comprehensive study on designing an energy-aware architectures for Wireless Sensor Networks is presented in the next accepted paper. The paper is “Designing an Energy-Aware Mechanism for Lifetime Improvement of Wireless Sensor Networks: A Comprehensive Study”. The authors have also analysed and proposed a scheme, Extended-Multilayer Cluster Designing Algorithm (E-MCDA) in a large network. Among its components, algorithms for time slot allocation, minimising the CH competition candidates, and cluster member selection to CH play underpinning roles to achieve the target. The authors have done simulations in NS2 to evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, the number of designed clusters, the number of nodes per cluster and unclustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCDA and EADUC.

The next paper is “Rule based (Forward Chaining/Data Driven) Expert System for Node Level Congestion Handling in Opportunistic Network” by Ahthasham Sajid et al. The paper is about Opportunistic networks which are part of the most popular categories of Mobile Ad hoc networks. One of the challenge is the selection of best custodian node that can store messages at its buffer until a destination node is found. The important features of the Delay Tolerant Network (DTN) are a selection of the best forwarding nodes and co-ordination among the nodes to deliver the packets to their destination in an efficient manner with less loss and maximum delivery rate. Therefore, in this paper, the authors have presented a rule based efficient expert system to address and handle storage level congestion issues. The proposed technique has been tested and validated via Opportunistic network environment and compared with MaxProp protocol.

The next paper is “A Comprehensive Analysis of Congestion Control Protocols in Wireless Sensor Networks” by Mian Ahmad Jan et al. In wireless Sensor Networks (WSNs) congestion occurs when the incoming traffic load exceeds the available capacity of the network. The authors have also presented the various factors that lead to congestion in WSNs such as buffer overflow, varying rates of transmission, many-to-one communication paradigm, channel contention and the dynamic nature of a transmission channel. The energy-efficient congestion control protocols need to be designed to detect, notify and control congestion effectively. The authors have present a review of the latest state-of-the-art congestion control protocols. Depending on their inherent nature of control mechanism, these protocols are classified into three categories, i.e., traffic-based, resource-based and hybrid. Traffic-based protocols are further subdivided, based on their hop-by-hop or end-to-end delivery modes. Resource-based control protocols are further analyzed, based on their route establishment approach and efficient bandwidth utilization techniques. In addition, they have discussed the internal operational mechanism of these protocols for congestion alleviation. The authors have concluded that the behaviour of each class of protocols varies with the type of application and a single metric alone cannot precisely detect congestion of the network.

In the last decade, there has been a considerable development in the field of wireless vehicular communications so as to satisfy the requirements of Cooperative Intelligent Transportation Systems (CITS). It is also worth mentioning that currently CITS and intelligent transportation systems are very hot research areas and especially the security of these systems. We have accepted the paper “Implementation and Analysis of IEEE and ETSI Security Standards for Vehicular Communications” by Bruno Fernandes et al. The paper presents the implementation and analysis of the two most used standards for vehicular communications. However, due to the expected popularity of ITS, VANETs could be prone to attacks by malicious sources. To prevent this, security standards, such as IEEE 1609.2 and ETSI ITS’ standards, were developed. In this work, the design and implementation of an API capable of conducting the required cryptographic algorithms and protocols for the transmission of secure messages according to the IEEE 1609.2 and ETSI ITS’ security standards is presented. The implemented security protocols are then integrated into a system emulating a public key infrastructure to evaluate the performance impact on safety-related communications, in particular, the delay associated with the communication’ coding/decoding process.

The next paper is “Performance of Cognitive Radio Sense-and-Wait assisted Hybrid Automatic Repeat reQuest” by Fazlullah Khan et al. In this paper the authors have presented a work on the the cognitive radio (CR) concept which emerges as a promising solution for reducing the spectrum scarcity issue. The CR network is a low cost solution for efficient utilization of the spectrum by allowing secondary users (SUs) to exploit the unoccupied licensed spectrum. The authors have presented the model the PU’s utilization activity by a two-state Discrete-Time-Markov Chain (DTMC) (i.e., Free and busy states), for identifying the temporarily unoccupied spectrum bands,. Furthermore, they have proposed a Cognitive Radio Sense-and-Wait assisted HARQ scheme, which enables the Cluster Head (CH) to perform sensing operation for the sake of determining the PU’s activity. Once the channel is found in free state, the CH advertise control signals to the member nodes for data transmission relying on Stop-and-Wait Hybrid- Automatic Repeat-Request (SW-HARQ). The proposed CRSW assisted HARQ scheme is analytical modeled, based on which the closed-form expressions are derived both for average block delay and throughput. Finally, the correctness of the closed-form expressions are confirmed by the simulation results.

The next accepted paper is “Opportunistic Energy Cooperation Mechanism for Large Internet of Things” by Jinyu Hu et al. In this paper, the authors have focus on energy efficiency maximization and network throughput optimization problems for energy cooperation in Energy Harvesting Cooperative Wireless Sensor Networks (EHC-WSNs). In order to maximize the efficiency of energy charging phase, a Region-based Proactive Energy Cooperation (RPEC) strategy is developed, which is used to charge the life-critical cooperators or receivers in time. Furthermore, by introducing a novel metric that converts optimal forwarder selection from the multi-dimensional problem to one-dimensional problem, an Energy-Neutral based Opportunistic Cooperative Routing (ENOCR) algorithm is proposed to optimize the relay nodes selection and improve the network throughput. The simulations results showed that the proposed mechanism can significantly improve energy efficiency and network lifetime.

The last paper is “User-centric Clustering and Beamforming for Energy Efficiency Optimization in CloudRAN”. In this paper, the authors have considered the problem of how to assign each user to several preferred remote radio heads (RRHs) and design the corresponding beamforming coefficients in a user-centric and energy efficient manner. They have formulated the problem as a joint clustering and beamforming optimization problem, with the objective to maximize the energy efficiency (EE) while satisfying the users’ quality of service (QoS) requirement and respecting the RRHs’ transmit power limits. They have first transform it into an equivalent parametric subtractive problem using the approach in fractional programming, and then it is cast into a tractable convex optimization problem by introducing a lower bound of the objective function. Finally, the structure of the optimal solution is derived and a two-tier iterative scheme is developed to find the clustering pattern and beamforming coefficients that maximize EE. Specially, they have derived a RRH-user association threshold, based on which the RRH clustering pattern and the corresponding beamforming coefficients can be simultaneously determined.

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Instituto de Telecomunicações–Aveiro Campus, Universitário de Santiago, 3810–193, Aveiro, Portugal

Muhammad Alam

Abdul Wali Khan University Mardan, Mardan, Pakistan

Mian Ahmad Jan

Guangdong University of Petrochemical Technology, Maoming, China

University of Technology Sydney, Ultimo, Australia

Xiangjian He

School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China

Yuanfang Chen

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Correspondence to Muhammad Alam .

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Alam, M., Jan, M.A., Shu, L. et al. Editorial: Current and Future Trends in Wireless Communications Protocols and Technologies. Mobile Netw Appl 23 , 377–381 (2018). https://doi.org/10.1007/s11036-018-1026-y

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Published : 08 March 2018

Issue Date : June 2018

DOI : https://doi.org/10.1007/s11036-018-1026-y

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Latest Thesis Topics in Wireless Communication

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Upcoming Research in Wireless Communication

• Neuromorphic Computing
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• User Profile Based Targeted Information Delivery also Using Novel Method and system in a Mobile Communication
•  Picocell Communication in a Macrocell also Using Controlling Uplink Power in Wireless Communications
• Gateway and Sensor Node Mutually Computing also in Wireless Sensor Network
• Q Controllable Antenna also for Wide Area Communication and Sensing in Wireless Charging through Coupled Magnetic Resonances
• Carrier Aggregation also for Apparatus of Transmitting Random Access Response and Configuring Downlink Timing in Mobile Communication

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Wireless communication is a process of conveying data among multiple devices without a direct wired link . In this technology, radio waves are very frequently used as a wireless communication medium. A wideband mobile wireless network offers a huge volume of multimedia information which allows communication with everyone everywhere. This page is about to present the most interesting research ideas and latest wireless communication project topics , standards and technologies!!!

In the current era of wireless communication, more mechanisms are coming up to improve the adeptness in spectrum transmission and utilization . Also, it is focused to use “everything over IP” for effortlessly interrelating varied structured networks. 

Why Wireless Communication?

On top of mobility, wireless networks provide adaptability and reliability over any kind of network for easy use . As a result, it ultimately acquires widespread familiarity in a short period. For instance , smartphones are featured with a significantly high throughput presentation.  

Similarly, infrastructure also plays a major role in wireless technology characterization. In comparison with a wireless system, the wired system setup is a more costly and time-intensive process . In the case of undesired conditions or distant situations, wireless communication suits more perfectly rather than wired communication. Now let’s see about the important characteristics of wireless interaction.

Key Features of Wireless Technologies 

• LOS or NLOS transmission
• Unlicensed free spectrum 
• Moderate coverage and mobility 
• LoS/NloS transportations
• Minimum deployment cost
• Unlicensed free spectrum
• Large data rate 
• Complete mobility 
• LOS/NLOS communications 
• Huge coverage
• Assured QoS
• LOS/NLOS communications
• Average coverage
• Assured Quality of Service
• Moderate mobility 
• LoRa communication
• Line of Sight / Non-Line of Sight communication
• Performance does not relies on snow, fog, and dust

To guarantee the standard of our research undertakings, we continuously update our skills with recent technological innovations . From this study, we are acquainted with so many interesting facts about wireless technologies with their performance factors. For example, we have given SigFox, LoRaWAN, LTE Network , LTE-M, and NB-IoT .

Performance of Latest Wireless Technologies 

• Modulation:  CSS,  Band:  Sub-GHz ISM: EU (433 MHz, 868 MHz), US (915 MHz), Asia (430 MHz),  Data Rate:  03-37.5 kbps (LoRA), 50 kps (FSK),  Range:  5 km (urban) , 15 km (rural),  MAC:  pure ALOHA,  Topology:  star of stars,  Payload size:  up to 250 B,  Proprietary aspects:  PHY layer
• Modulation:  UNB DBPSK, GFSK,  Band:  Sub-GHz ISM: EU (868 MHz), US (902 MHz),  Data Rate:  100 bps (UL), 600 bps (DL),  Range:  10 km (urban) , 50 km (rural),  MAC:  pure ALOHA,  Topology:  star,  Payload size:  12 B (UL), 8 B (DL),  Proprietary aspects:  PHY and MAC layers
• Modulation:  QPSK,  Band:  Licensed 700-900 MHz,  Data Rate:  158 kbps (UL), 106 kbps (DL),  Range:  15 km,  MAC:  FDMA / OFDMA,  Topology:  star,  Payload size:  125 B (UL), 85 B (DL),  Proprietary aspects:  Full stack
• Modulation:  16QAM,  Band:  Licensed 700-900 MHz,  Data Rate:  1 Mbps,  Range:  11 km,  MAC:  FDMA / OFDMA,  Topology:  star,  Payload size:  Unknown,  Proprietary aspects:  Full stack

Similarly, we have also listed down the recent wireless standards with their performance features. For instance, we have given IEEE 802.15.4k, IEEE 802.15.4g, Weightless-W, and Weightless-N.

Performance of Latest Wireless Standards 

• Modulation:  MR-(FSK, OFDMA, OQPSK),  Band:  ISM Sub-GHz & 2.4 GHz,  Data Rate:  4.8 kbps-800 kbps,  Range:  10 km,  MAC:  CSMA / CA,  Topology:  star, mesh, peer-to-peer,  Payload size:  2047 B,  Proprietary aspects:  Open Standard,  Deployment Model:  Private
• Modulation:  DSSS, FSK,  Band:  ISM Sub-GHz & 2.4 GHz,  Data Rate:  1.5 bps-128 kbps,  Range:  5 km (urban),  MAC:  CSMA / CA or ALOHA with PCA,  Topology:  star,  Payload size:  2047 B,  Proprietary aspects:  Open Standard,  Deployment Model:  Private
• Modulation:  UNB DBPSK,  Band:  ISM Sub-GHz EU (868 MHz), US (915 MHz),  Data Rate:  30 bps-100 kbps,  Range:  3 km (urban),  MAC:  slotted ALOHA,  Topology:  star,  Payload size:  20 B,  Proprietary aspects:  Open Standard,  Deployment Model:  Private
• Modulation:  16-QAM, BPSK, QPSK, DBPSK,  Band:  TV white spaces 470-790 MHz,  Data Rate:  1 kbps-10 mbps,  Range:  5 km (urban),  MAC:  TDMA / FDMA,  Topology:  star,  Payload size:  >10B,  Proprietary aspects:  Open Standard,  Deployment Model:  Private

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10+ Latest Wireless Communication Project Topics

• Radio Frequency and Microwave Technologies 
• Advance RF Antenna and Propagation 
• Advance Microwaves, Microwave devices, and Components
• Multiple Cross-Layer Mac Design
• Wireless Data Communications and Computing
• Improved Equalization, Diversity, Channel Codding Techniques 
• Integration of Cognitive radio with Dynamic spectrum access 
• RF-Energy Harvesting with Massive Wireless Energy Transfer
• Full-Duplex Radio Communication and Technologies
• Wireless Heterogeneous Cellular Networks Theory 
• Massive MIMO based mmWave communication Model
• Adaptive Design, Modulation, and coding for wireless systems
• Radio Propagation and Radio channel characterization 
• Resource-Aware Allocation and load –Aware Balancing 
• MIMO based Adaptive Space-Time Processing 

Energy-efficient wireless communications

In specific, we have discussed energy-efficient Wireless Communication Project Topics which gain more importance in recent research. As matter of fact, it is deployed in various smart grid systems like meter power line observation, data acquisition, and resource demand management. In this, it can also be used in several sections of the smart grid such as SG-NAN, SG-WAN, and SG-HAN . As well as, it catches the relations among the following aspects in the radio frequency transceiver,

• Order of Modulation 
• Power Consumption
• Channel fading
• Power Amplifier 
• Distance of Transceiver 
• other Circuit Modules 

Research Ideas in Wireless Communications 

• Multi-Attribute based Vertical Handover Solution 
• Strategy for Network Switching
• Power Control in Wireless Transmission
• Integrated Cluster-based Routing Protocol 
• Topology Optimization for Directional Antenna Network

Also, we are ready to share a few more important updates about the wireless communication trends. So if you are looking for the best Wireless Communication Project Topics, you can find us as the best solution to carry over your research career.

Frontiers | Science News

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Biodiversity loss: three research topics revealing threats and solutions.

The planet is demanding a reset in our interactions with nature. Protecting and restoring biodiversity is no longer optional because when nature suffers, so do we .

According to United Nations data, "current negative trends in biodiversity and ecosystems will undermine progress towards 80% of the assessed targets of eight Sustainable Development Goals."

As a result, the theme for this year’s International Day for Biological Diversity is ’Be part of the Plan’, a call to action for everyone to support the implementation of the Kunming-Montreal Global Biodiversity Framework , also known as the Biodiversity Plan.

In light of the crucial role of biodiversity to the health of our planet, we have listed three of our most impactful Research Topics on the causes and consequences of biodiversity loss.

All articles are openly available to view and download.

1 | Aquatic One Health—The Intersection of Marine Wildlife Health, Public Health, and Our Oceans

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This Research Topic provides insights into marine wildlife and aquaculture disease processes, conditions, and health issues. It also demonstrates the potential to influence public health within the One Health framework.

The interrelatedness of environmental, animal, and public wellbeing form the basis of the 'One Health' concept, a framework to guide research and conservation efforts by studying not only animal health in isolation, but also in the context of public and environmental health.

Humankind's past and present use of ocean ecosystems as waste sinks has had significant, wide-ranging, and negative effects on marine life and human health, making this topic highly relevant to the mission of biodiversity preservation.

View Research Topic

2 | Ethnofood Chemistry: Bioactive Components in Unexploited Foods from Centres of Biodiversity

45,000 views | 11 articles

A Research Topic looking at ethno plant foods from centers of biodiversity -Africa, Asia and Australia, North, and Central America, South America, Europe, and Central Asia- with bioactive components of nutritional and health value.

Ethnofoods—traditional foods—originate from the heritage and culture of an ethnic group that uses their knowledge of local plants and animal sources. They are also unexploited and underutilized by the wider community worldwide.

This topic highlights the importance of incorporating ethno-plant foods into nutrition intervention programs globally to combat hidden hunger and provide nutrition and food security. Furthermore, it contributes to demonstrating the possibility of developing sustainable food systems.

3 | Community Series in the Wildlife Gut Microbiome and Its Implication for Conservation Biology, Volume II

53,100 views | 21 articles

This Research Topic dives into the potential connection between gut microbiome and conservation biology. Microbiome studies can increase our understanding of non-native species invasion, host response to pathogens and chemical contamination, and host ability to tolerate climate change.

The animal gut microbiota can be beneficial in many ways, including dietary supplementation, host immune function, and behavior. The microbiomes of animals affect host fitness, population characteristics such as demography, and health status, as well as adaptability. For example, the fitness effects of gut microbiomes on wild animals may have important implications for the conservation and management of species.

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World leaders still need to wake up to AI risks

Leading AI scientists are calling for stronger action on AI risks from world leaders, warning that progress has been insufficient since the first AI Safety Summit in Bletchley Park six months ago.

Then, the world's leaders pledged to govern AI responsibly. However, as the second AI Safety Summit in Seoul (21-22 May) approaches, twenty-five of the world's leading AI scientists say not enough is actually being done to protect us from the technology's risks. In an expert consensus paper published today in Science, they outline urgent policy priorities that global leaders should adopt to counteract the threats from AI technologies.

Professor Philip Torr,Department of Engineering Science,University of Oxford, a co-author on the paper, says: "The world agreed during the last AI summit that we needed action, but now it is time to go from vague proposals to concrete commitments. This paper provides many important recommendations for what companies and governments should commit to do."

World's response not on track in face of potentially rapid AI progress

According to the paper's authors, it is imperative that world leaders take seriously the possibility that highly powerful generalist AI systems -- outperforming human abilities across many critical domains -- will be developed within the current decade or the next. They say that although governments worldwide have been discussing frontier AI and made some attempt at introducing initial guidelines, this is simply incommensurate with the possibility of rapid, transformative progress expected by many experts.

Current research into AI safety is seriously lacking, with only an estimated 1-3% of AI publications concerning safety. Additionally, we have neither the mechanisms or institutions in place to prevent misuse and recklessness, including regarding the use of autonomous systems capable of independently taking actions and pursuing goals.

World-leading AI experts issue call to action

In light of this, an international community of AI pioneers has issued an urgent call to action. The co-authors include Geoffrey Hinton, Andrew Yao, Dawn Song, the late Daniel Kahneman; in total 25 of the world's leading academic experts in AI and its governance. The authors hail from the US, China, EU, UK, and other AI powers, and include Turing award winners, Nobel laureates, and authors of standard AI textbooks.

This article is the first time that such a large and international group of experts have agreed on priorities for global policy makers regarding the risks from advanced AI systems.

Urgent priorities for AI governance

The authors recommend governments to:

• establish fast-acting, expert institutions for AI oversight and provide these with far greater funding than they are due to receive under almost any current policy plan. As a comparison, the US AI Safety Institute currently has an annual budget of $10 million, while the US Food and Drug Administration (FDA) has a budget of $6.7 billion.
• mandate much more rigorous risk assessments with enforceable consequences, rather than relying on voluntary or underspecified model evaluations.
• require AI companies to prioritise safety, and to demonstrate their systems cannot cause harm. This includes using "safety cases" (used for other safety-critical technologies such as aviation) which shifts the burden for demonstrating safety to AI developers.
• implement mitigation standards commensurate to the risk-levels posed by AI systems. An urgent priority is to set in place policies that automatically trigger when AI hits certain capability milestones. If AI advances rapidly, strict requirements automatically take effect, but if progress slows, the requirements relax accordingly.

According to the authors, for exceptionally capable future AI systems, governments must be prepared to take the lead in regulation. This includes licensing the development of these systems, restricting their autonomy in key societal roles, halting their development and deployment in response to worrying capabilities, mandating access controls, and requiring information security measures robust to state-level hackers, until adequate protections are ready.

AI impacts could be catastrophic

AI is already making rapid progress in critical domains such as hacking, social manipulation, and strategic planning, and may soon pose unprecedented control challenges. To advance undesirable goals, AI systems could gain human trust, acquire resources, and influence key decision-makers. To avoid human intervention, they could be capable of copying their algorithms across global server networks. Large-scale cybercrime, social manipulation, and other harms could escalate rapidly. In open conflict, AI systems could autonomously deploy a variety of weapons, including biological ones. Consequently, there is a very real chance that unchecked AI advancement could culminate in a large-scale loss of life and the biosphere, and the marginalization or extinction of humanity.

Stuart Russell OBE, Professor of Computer Science at the University of California at Berkeley and an author of the world's standard textbook on AI, says: "This is a consensus paper by leading experts, and it calls for strict regulation by governments, not voluntary codes of conduct written by industry. It's time to get serious about advanced AI systems. These are not toys. Increasing their capabilities before we understand how to make them safe is utterly reckless. Companies will complain that it's too hard to satisfy regulations -- that "regulation stifles innovation." That's ridiculous. There are more regulations on sandwich shops than there are on AI companies."

• Communications
• Computers and Internet
• STEM Education
• World Development
• Public Health
• Environmental Policies
• Automobile safety
• Scientific misconduct
• Climate engineering
• European Southern Observatory
• Water resources
• Biodiversity

Story Source:

Materials provided by University of Oxford . Note: Content may be edited for style and length.

Journal Reference :

• Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atılım Güneş Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca Dragan, Philip Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann. Managing extreme AI risks amid rapid progress . Science , 2024; DOI: 10.1126/science.adn0117

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