5G network operators are constantly under pressure from regulatory agencies who restrict the deployment of base stations (gNBs) close to incumbent services, such as radio astronomy services (RAS), to avoid interfering with them. Recent works for coexistence with RAS employ limited channel modeling approaches and use explicit out-of-band communication between the gNB and RAS. However, the strict latency requirements of 5G make explicit communications less desirable due to their overheads. Deploying gNBs close to RAS is also not yet supported. In this paper, we propose a proactive open-loop beamforming and interference nullification technique in which a gNB nullifies its downlink signal at a nearby RAS telescope while beamforming to its users (UEs). We estimate the gNB-RAS channel using raytracing on open-source terrain maps. We formulate a problem that maximizes the minimum rate for UEs under the constraint of maximum allowable interference power at the RAS and show that its time complexity scales cubically with the number of gNB antennas. Hence, we propose a heuristic solution that achieves 4 orders better latency and 100 dBW lower interference power than the max-min rate solution with similar sum rate on users. Our proposed solution consistently achieves less than -310 dBW interference power, even when the gNB-RAS distance is less than 1 km, satisfying international regulations, and is robust against moving users that vary in location and elevation.

The rapid increase in the number of satellites increases the amount of signal interference at ground stations, which are typically equipped with large aperture antennas (LAAs). In this work, we propose an interference mitigation solution for satellite communications by placing a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) at the first focal point of the LAA exploiting the existing structure and power supply. The goal is to use the STAR-RIS to improve the signal-to-interference-plus-noise ratio (SINR) of the desired satellite in the presence of multiple interferes. We turn the SINR maximization into fractional programming for which we use Dinkelbach's method to find the STAR-RIS coefficients. Our numerical results reveal the effectiveness of our approach in enhancing the SINR at the ground station.

The physical layer security of a non-orthogonal multiple access (NOMA) system is investigated. In order to maximize the security level of each NOMA user in the system, a multiobjective optimization (MOO) problem is proposed for handling the relationship among conflicting objectives. A deep neural network-based framework is designed for solving the associated MOO problem and for estimating the Pareto front. The framework shows that the estimated Pareto front is very close to the true one, thus allowing designers to select Pareto optimal solutions for striking the most appropriate compromise for all users, even on dynamically time-variant basis. Numerical results are provided for illustrating the associated trade-offs.

Physical-layer (PHY) attacks increasingly pose a threat to location-based services. To secure the localization mechanism against PHY attacks, we propose a novel framework based on localization and user-and-attacker detection, with the help of unsupervised machine learning (ML) algorithms and multiple signal classification (MUSIC) spectra. Our proposed framework consists of two stages: i) uplink localization and detection; and ii) downlink secure transmission. Noticeably, in the proposed framework, a reciprocal relationship between the localization mechanism and the user/attacker detection is developed, where the localization supports the detection and vice versa. This reciprocal relationship allows wireless systems to detect localization attacks and further localize the attacker. Through simulation, we show the efficacy of combining localization and detection in the uplink. We then demonstrate the benefit of employing both localization and movable-antenna arrays for secure downlink transmission.

Security threats of an IEEE 802.11ax (WiFi 6) system can occur throughout the layers of the protocol stack. Looking at the security aspect of the physical layer and from an attacker's perspective, we present how a spoofing attack can cause an access point to steer the beam dedicated to a legitimate user toward the attacker. More particularly, we propose the BeamSteal attack that takes advantage of the vulnerability of the beamforming feedback (BFF) mechanism. The purpose of the BeamSteal attack is to cause the access point to receive the wrong BFF -information-bearing bits and end up steering the beam toward the attacker instead of the legitimate user. To contrast the harmful effect of the proposed attack, we compare it with two benchmarks, namely the no attack case and the jamming attack case. Our numerical results show that the BeamSteal attack not only degrades the performance of the legitimate user, but also helps the attacker improve its performance significantly.

The physical-layer security (PLS) of a space–ground communication system is examined. To improve the security performance, a pair of reconfigurable intelligent surfaces (RISs) is integrated into the system and benchmarked against a scheme, where there is only a single RIS close to the ground station. As for the double-RIS scenario, we formulate a secrecy rate maximization problem, and then propose an alternating optimization (AO) algorithm for jointly optimizing three vectors, namely, the beamformer of the ground station and the reflecting vectors of two different RISs. Similarly, as for the single-RIS case, we also propose another AO algorithm for optimizing a pair of vectors, namely, the beamformer of the ground station and the reflecting vector of the single RIS. Both the double-RIS and the single-RIS AO algorithms are developed on the basis of the first-order Taylor expansion and Dinkelbach’s method, which allow us to approximate nonconvex optimization problems by convex ones. Our results demonstrate that the proposed double-RIS scheme outperforms the single-RIS benchmark scheme in terms of its security.

A whole suite of innovative technologies and architectures have emerged in response to the rapid growth of wireless traffic. This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite for enhancing the network’s spectral efficiency.As for our analytical contributions, upon coherently combing the signals received by the central processing unit (CPU) from the users through the space and terrestrial links, we first mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. Our generic achievable throughput expression is applicable for arbitrary received signal detection techniques employed at the APs and the satellite under realistic imperfect channel estimates. A closed-form expression is then obtained for the ergodic UL data throughput, when maximum ratio combining is utilized for detecting the desired signals.As for our resource allocation contributions, we formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics for performing power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated relying on a closed-form expression. Our integrated hybrid network concept allows users to be served that may not otherwise be accommodated due to the excessive data demands. The algorithms proposed allow us to address the congestion issues appearing when at least one user is served at a rate below his/her target. The mathematical analysis is also illustrated with the aid of our numerical results that show the added benefits of considering the space links in terms of improving the ergodic data throughput. Furthermore, the proposed algorithms smoothly circumvent any potential congestion, especially in face of high rate requirements and weak channel conditions.

Millimeter-wave (mmWave) carriers are an essential building block of fifth-generation (5G) systems. Satisfactory performance of the communications over the mmWave spectrum requires an alignment between the signal beam of the transmitter and receiver, achieved via beam training protocols. Nevertheless, beam training is vulnerable to jamming attacks, where the attacker intends to send jamming signals over different spatial directions to confuse legitimate nodes. This paper focuses on defending against this attack in smart factories where a moving Automated Guided Vehicle (AGV) communicates with a base station via a mmWave carrier. We introduce a defensive strategy to cope with jamming attacks, including two stages: jamming detection and jamming mitigation. Developed based on autoencoders, both algorithms can learn the characteristics/features of the received signals at the AGV. They can be employed consecutively before performing the downlink data transmission. In particular, once a jamming attack is identified, the jamming mitigation can be utilized to retrieve the corrupted received signal strength vector, allowing a better decision during the beam training operation. In addition, the proposed algorithm is straightforward and fully compliant with the existing beam training protocols in 5G New Radio. The numerical results show that not only the proposed defensive strategy can capture more than 80% of attack events, but it also improves the average signal-to-interference-plus-noise-ratio significantly, i.e., up to 15 dB.

This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite. By coherently combing the signals received by the central processing unit from the users through the space and terrestrial links, we mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. A closed-form expression is obtained when maximum ratio combining is employed to detect the desired signals. We formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics to perform power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated based on a closed-form expression. The mathematical analysis is validated with numerical results showing the added benefits of considering a satellite link in terms of improving the ergodic data throughput.

This article presents a general system framework that lays the foundation for reconfigurable intelligent surface (RIS)-enhanced broadcast communications in Industrial Internet of Things (IIoTs). In our system model, we consider multiple sensor clusters co-existing in a smart factory where the direct links between these clusters and a central base station (BS) are blocked completely. In this context, a RIS is utilized to reflect signals broadcast from BS toward cluster heads (CHs) which act as a representative of clusters, where BS only has access to the statistical distribution of the channel state information (CSI). An analytical upper bound of the total ergodic spectral efficiency (SE) and an approximation of outage probability are derived. Based on these analytical results, two algorithms are introduced to control the phase shifts at RIS, which are the Riemannian conjugate gradient (RCG) method and the deep neural network (DNN) method. While the RCG algorithm operates based on the conventional iterative method, and the DNN technique relies on unsupervised deep learning (DL). Our numerical results show that both algorithms achieve satisfactory performance based on only statistical CSI. In addition, compared to the RCG scheme, using DL reduces the computational latency by more than ten times with an almost identical total ergodic SE achieved. These numerical results reveal that while using the conventional RCG method may provide unsatisfactory latency, and the DNN technique shows much promise for enabling RIS in ultrareliable and low-latency communications (URLLC) in the context of IIoTs.

This article proposes a novel Industrial Internet of Things framework to monitor the machine health conditions (MHCs) in a smart factory. The framework utilizes the reconfigurable intelligent surface (RIS) to address propagation blockages while employing a novel power mapping scheme and an autoencoder to facilitate the transmission and classification of the MHCs. Analytical and numerical analyses are then performed to study the ergodic capacity (primary information) and the MHC accuracy (secondary information) in terms of the RIS size ($K$) and the transmit power ($P$). We observe that the accuracy of detecting MHCs does not change significantly with$K$and$P$, implying that the MHC alerts can be efficiently conveyed in parallel with the primary information. In contrast, a careful choice of different power mapping levels is necessary in order to achieve the two main goals: 1) reasonably high data rate for primary transmission and 2) high accuracy for secondary MHC information.

Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as O-OFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation.

We consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as “clean” or “normal”, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contrast, the received signal is considered as “spurious” or “abnormal” in the face of a spoofing signal. An autoencoder (AE) is trained to learn the characteristics/features from a training dataset, which contains only normal samples associated with no spoofing attacks. The AE takes original samples as its input samples and reconstructs them at its output. Based on the trained AE, we define the detection thresholds of our spoofing discovery algorithm. To be more specific, contrasting the output of the AE against its input will provide us with a measure of geometric waveform similarity/dissimilarity in terms of the peaks of curves. To quantify the similarity betweenunknowntesting samples and thegiventraining samples (including normal samples), we first propose a so-calleddeviation-based algorithm. Furthermore, we estimate the angle of arrival (AoA) from each legitimate aeroplane and propose a so-calledAoA-based algorithm. Then based on a sophisticated amalgamation of these two algorithms, we form our final detection algorithm for distinguishing the spurious abnormal samples from normal samples under a strict testing condition. In conclusion, our numerical results show that the AE improves the trade-off between the correct spoofing detection rate and the false alarm rate as long as the detection thresholds are carefully selected.

We consider a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in which a source node, Alice, communicates with an energy-harvesting destination node, Bob, in the presence of a passive eavesdropper. To secure the wireless transmission, Alice generates a hybrid artificial noise (AN) in both frequency and time domains. Moreover, in order to collect more energy, Bob splits the received signal power of the cyclic prefix of each OFDM block. We then propose two non-convex optimization problems to balance both the need for security and the need for harvesting energy at Bob. While one considers maximizing the secrecy rate, the other approach aims at maximizing the harvested energy. Path-following algorithms of low computational complexity are developed and evaluated. Our numerical results show the gain of our proposed scheme and the effectiveness of our proposed algorithms.

A multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in the presence of a passive eavesdropper is considered. The deployment of radio frequency power transfer at the receiver and the use of hybrid artificial noise at the transmitter are simultaneously taken into account. At the legal receiver, the cyclic prefix of each OFDM block is used for the purpose of harvesting energy. In parallel, the power-splitting SWIPT technique is additionally used. We then propose a trade-off problem to maximize the secrecy rate of the network while keeping the harvested energy above a given threshold. Throughout the numerical results, the performance of our proposed secure scheme is evaluated.

This paper studies the security aspect of a recently introduced “cell-free massive MIMO” network under a pilot spoofing attack. First, a simple method to recognize the presence of this type of an active eavesdropping attack to a particular user is shown. In order to deal with this attack, we consider the problem of maximizing the achievable data rate of the attacked user or its achievable secrecy rate. The corresponding problems of minimizing the power consumption subject to security constraints are also considered in parallel. Path-following algorithms are developed to solve the posed optimization problems under different power allocation to access points (APs). Under equip-power allocation to APs, these optimization problems admit closed-form solutions. Numerical results show their efficiency.

We consider a cooperative relay network in the presence of many eavesdroppers whose locations are distributed according to a homogeneous Poisson point process. The relay, which operates in amplify-and-forward protocol, has very large transmit and receive antenna arrays, while other nodes are equipped with a single antenna. We assume that the relay exploits maximum ratio combing (MRC) in the uplink and maximum ration transmission in the downlink, while all eavesdroppers are able to exploit MRC to maximize the received signals. In addition, there is no perfect channel state information of any eavesdroppers since all eavesdroppers in practice tend to hide from the legitimate users. Furthermore, we suppose that there are direct links between source and eavesdroppers, while a direct link between source and destination does not exist. Under such assumptions, which are totally biased towards eavesdroppers, we examine the security performance of the proposed system throughout secrecy outage probability and connection outage probability.

We consider a diffusion-based nano-network with N spatially distributed transmitters and one receiver. While the transmitters are linked in a unified entity to perform one transmission, the receiver is large enough to be viewed as a plane. Messages are encoded into the number of nano-scale molecules. Based on these assumptions, we analyze the signal-to-interference ratio, which is based on the average numbers of absorbed molecules. Moreover, we present an approach to interference alignment for molecular communications.

A cooperative relay network operating in the presence of eavesdroppers, whose locations are distributed according to a homogeneous Poisson point process, is considered. The relay is equipped with a very large antenna array and can exploit maximal ratio combing in the uplink and maximal ratio transmission in the downlink. A realistic model in which the channel state information of every eavesdropper is not known is considered, as eavesdroppers tend to hide themselves in practice. The destination is thus in a much weaker position than all the eavesdroppers because it only receives the retransmitted signal from the relay. Under this setting, the security performance is investigated for two relaying protocols: amplify-and-forward and decode-and-forward. The secrecy outage probability, the connection outage probability, and the tradeoff between them, which is controlled by the source power allocation, are examined. Finally, suitable solutions for the source power (such that once the transmission occurs with high reliability, the secure risk is below a given threshold) are proposed for a tradeoff between security and reliability.

This paper studies the capacity and energy efficiency of non‐coherent Rayleigh fading channels with Gaussian mixture noise where neither the transmitter nor the receiver has the knowledge of channel state information. The channel under consideration is suited for cellular networks having multi‐tier heterogeneous architectures in which the channel conditions change rapidly. In the first part of the paper, we characterize the structure of a capacity‐achieving input signal. Specifically, we establish an integrable upper bound on the integrand in the output entropy and demonstrate that there exists a unique optimal input. By formulating the Kuhn‐Tucker condition and establishing a diverging lower bound on it, we show that the optimal input is discrete having a finite number of mass points. Using this result, we investigate the capacity and energy efficiency of the considered channel in the second part of the paper. In particular, we first develop a numerical method to evaluate the optimal input and compute the capacity. The energy efficiency, which is related to the capacity and optimal input in low‐power regimes, is examined by calculating the minimum bit energy and wideband slope of the spectral‐efficiency curve. We also analytically show the optimality of an on‐off signal in this regime.

A generalised selection transmission (GST) and generalised selection combining (GSC) scheme is proposed for two‐way relay networks where two multi‐antenna sources exchange information via a single‐antenna relay. New exact and asymptotic expressions are derived for the outage probability and symbol error rate (SER) in Rayleigh fading. Moreover, a tight upper bound on the ergodic sum‐rate is presented. These results are used to demonstrate that the proposed GST/GSC scheme preserves the full diversity order, which equals the minimum number of antennas at the two sources. It is also shown that the impact of the number of selected antennas lies in the array gain only. Furthermore, the GST/GSC scheme significantly improves the performance relative to single‐antenna selection, and only incurs a negligible reduction in performance relative to all‐antenna beamforming. Finally, the optimal relay location that minimises the SER is determined analytically. It is observed that the optimal relay location shifts towards one source when the number of selected or available antennas at the other source increases.

A cooperative network in which confidential messages are conveyed from a source to a legitimate destination with the help of decode-and-forward relays in the presence of a malicious eavesdropper is considered. Tight upper bounds on the ergodic secrecy rate are derived in the cases of i) cooperative beamforming and ii) multi-user selection. Further, a new concept of cooperative diversity gain, namely, adapted cooperative diversity gain (ACDG), is investigated. It is shown that the ACDG can be seen as an effective metric to evaluate the security level of a cooperative wireless network in the presence of eavesdroppers. Also, the ACDG obtained in the cooperative beamforming scenario is equal to the traditional cooperative diversity gain of traditional multiple-input single-output networks, while the ACDG obtained in the multiuser scenario is equal to that of traditional single-input multiple-output networks.

A relay network in which a source wishes to convey a confidential message to a legitimate destination with the assistance of trusted relays is considered. In particular, cooperative beamforming and user selection techniques are applied to protect the confidential message. The secrecy rate (SR) and secrecy outage probability (SOP) of the network are investigated first, and a tight upper bound for the SR and an exact formula for the SOP are derived. Next, asymptotic approximations for the SR and SOP in the high signal-to-noise ratio (SNR) regime are derived for two different schemes: 1) cooperative beamforming and 2) multiuser selection. Furthermore, a new concept of cooperative diversity gain, namely, adapted cooperative diversity gain (ACDG), which can be used to evaluate the security level of a cooperative relaying network, is investigated. It is shown that the ACDG of cooperative beamforming is equal to the conventional cooperative diversity gain of traditional multiple-input single-output networks, while the ACDG of the multiuser scenario is equal to that of traditional single-input multiple-output networks.

In this study, the authors propose an equivocation scheme for wiretap channels, which is composed of bit‐extension mapping, coset coding and hybrid automatic repeat request (HARQ). The inner bit‐extension code and outer coset code are used for equivocation of a wiretapper channel, whereas the HARQ scheme is to mitigate noisy errors in a main legitimate channel. These concatenated codes and HARQ are effective and practical for various channel conditions. The average equivocation and the probability of causing imperfect secrecy are analysed for finite codeword lengths. As a function of channel conditions, they investigate the block error rate at the legitimate receiver and the information leakage to the wiretapper. From simulation results, they further determine the minimum requirements of code design for some target values of the ‘residual’ block error rate and information leakage at maximum retransmission.