This paper investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink (dl) communications with a primary focus on maximizing information secrecy by considering the channel state information (CSI) error. Acquiring perfect CSI is particularly challenging due to the unavailability of radio frequency chains at the STAR-RIS, the inherent impact of noise and interference on the CSI estimation, as well as non-collaborative nature of the eavesdroppers. In particular, we tackle the worst-case robust beamforming design problem to maximize the sum secrecy rate of the system while considering transmit power limitations, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. To tackle the resulting non-convex problem, we employ the S-procedure as an initial step to approximate semi-infinite inequality constraints. Subsequently, we leverage the alternating optimization with a line search framework to update the precoder and phase shift matrix iteratively. Furthermore, we extend our solution to address the non-convexity by leveraging a deep reinforcement learning (DRL) multi-agent (MA) framework based on Markov decision process. We also analyze practical phase shifts and the effect of direct links to showcase the practicality of our approach. Simulation results confirm STAR-RIS’s significant performance edge, exhibiting approximately 27.1% higher secrecy in conventional optimization and around 35.4% in the MA-DRL context compared over the conventional RIS. Moreover, our proposed MA-DRL approach surpasses single-agent schemes by about 8.6% in the case of proximal policy optimization and 19.9% in the case of deep deterministic policy gradient, emphasizing the benefits of the MA framework with STAR-RIS.

This paper explores simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink communications, focusing on maximizing information secrecy despite channel state information (CSI) errors. Perfect CSI is hard to achieve due to limited radio frequency chains at the STAR-RIS, noise, interference, and non-collaborative eavesdroppers. The study addresses the worst-case robust beamforming design problem to maximize the sum secrecy rate, considering transmit power limits, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. The S-procedure is used to estimate semi-infinite inequality constraints, followed by alternating optimization with a line search to iteratively update the precoder and phase shift matrix. Simulation results highlight STAR-RIS’s superior secrecy performance over conventional RIS and the algorithm’s efficiency across various scenarios.

The low-resolution reality of the hardware elements associated with massive mmWave antenna or reflector arrays is associated with the performance degradation of the wireless link when it is not properly controlled. In particular, the unintended angular radiations of the transmission or reflection arrays (e.g., transmission in non-intended directions) would invalidate the usual assumptions of information secrecy, even with perfect channel state information (CSI) knowledge at the transmitter, in the presence of low-resolution hardware. In this paper, we study a hybrid beamforming design for reconfigurable intelligent surface (RIS)-assisted multi-user multiple-input multiple-output (MU-MIMO) downlink (DL) communication, from the prospect of information secrecy maximization, wherein the array element phase rotations belong to the known discrete space. To address the NP-hard and non-convex nature of the problem at hand, we propose an iterative procedure by re-structuring the obtained discrete-domain problem into a tractable form which solves the problem numerically and guarantees the convergence to a stationary point. Further, we confirm the accuracy of the proposed optimization algorithm by an exhaustive search method based on graphical simulations. The minimal performance disparity that exists between the proposed algorithm and the considered digital beamforming (DBF) scheme as the upper bound validates the hybrid beamforming design. Moreover, the proposed work highlights the superiority of discrete-aware design over various existing baseline schemes, demonstrating the significant gains attainable by adopting discrete space design from the outset. Additionally, the proposed solution discusses the improvement in secrecy system performance by deploying RIS with an increased number of reflecting elements and thereby restricting the effect of eavesdroppers on secure communication.

In this paper, we explore a novel approach of using a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) to aid a multi-user (MU) multi-input multi-output (MIMO) system for simultaneous wireless information and power transfer (SWIPT) in the presence of statistical channel estimation errors (CEE). Our focus is on minimizing the power required for the SWIPT system through joint beamforming design at both the base station (BS) and STAR-RIS, while ensuring that the minimum rate and minimum energy harvesting requirements are met for information and energy receivers, respectively. Due to the non-convex and NP-hard nature of the problem, we utilize a minimum mean square error (MMSE) approach to simplify the problem and then use an alternating optimization framework to solve the beamforming design problems at the BS and STAR-RIS iteratively using general approximations. Simulation results show that the proposed algorithm provides a significant beamforming gain for STAR-RIS-aided SWIPT system over conventional RIS system while satisfying the given quality of service (QoS) constraints for SWIPT systems under the CEE.

Reconfigurable intelligent surface (RIS) is an emerging next-generation technology that can improve the energy efficiency using multiple low-power passive metasurfaces which reflect the desired signal to the users. In this work, we consider a RIS-assisted underlay multiuser multiple-input multiple-output cognitive radio network and formulate a weighted energy efficiency maximization problem in order to jointly optimize the active precoding matrix (APM) at the secondary transmitter and passive precoding matrix (PPM) at the RIS subject to the constraints of available transmission power at the secondary transmitter and maximum allowable interference towards the primary user/receiver. However, due to the coupling of APM and PPM variables, the problem becomes non-convex, and conventional optimization methods cannot be used to solve it. Therefore, by adopting the weighted minimum mean-square error method we first transform the non-convex objective function into a convex one. Next, based on the block coordinate descent method, we propose an iterative algorithm that determines the optimal APM and PPM using Lagrange dual decomposition method and inner approximation method, respectively. Finally, the optimality and efficacy of the proposed algorithm are validated using numerical simulations. The impact of the channel state information (CSI) error has been studied via numerical simulations on the proposed network and it shows that the proposed design is relatively robust against the CSI imperfections, especially at low SNR conditions.

In this work, we study a full-duplex (FD) cloud radio access network (C-RAN) from the aspects of infrastructure sharing and information secrecy, where the central unit utilizes FD remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. Furthermore, the communication takes place in the presence of untrusted external receivers, i.e., eavesdropper nodes. The communicated uplink (UL) and downlink (DL) waveforms are quantized in order to comply with the limited capacity of the fronthaul links. In order to provide information secrecy, we propose a novel utilization of the quantization noise shaping in the DL, such that it is simultaneously used to comply with the limited capacity of the fronthaul links, as well as to degrade decoding capability of the individual eavesdropper and the untrusted RUs for both the UL and DL communications. In this regard, expressions describing the achievable secrecy rates are obtained. An optimization problem for jointly designing the DL and UL quantization and precoding strategies are then formulated, with the purpose of maximizing the overall system weighted sum secrecy rate. Due to the intractability of the formulated problem, an iterative solution is proposed, following the successive inner approximation and semi-definite relaxation frameworks, with convergence to a stationary point. Numerical evaluations indicate a promising gain of the proposed approaches for providing information secrecy against the untrusted infrastructure nodes and/or external eavesdroppers in the context of FD C-RAN communications.

In this paper, we study the robust secure unmanned aerial vehicle (UAV) communication system, where a UAV with full-duplex (FD) capability simultaneously receives the information signal from a ground unit (GU) and transmits jamming signal to degrade the wiretap capability of potential multiple ground eavesdroppers (Eves). With the consideration of estimation error of Eves' locations, we aim to maximize the average worst secrecy rate inside a certain flight period of the UAV by jointly optimizing the transmit power of the GU and UAV as well as the trajectory of the UAV. The resulting problem is intractable due to its non-convex nature and strongly coupled variables. Furthermore, the estimation error of Eves' locations results in an infinite number of constraints, which makes the problem even more difficult. To tackle this difficulty, we first propose an iterative algorithm based on the Schur complement lemma and successive inner approximation method to efficiently solve the problem suboptimally under the estimated Eves' locations. Then, in order to cope with the of Eves' location errors, we develop a cutting-set method, which solves the problem by alternating between optimal power-trajectory design and worst-case Eves' locations analysis. Via simulation, we show the improvement of the proposed algorithm compared to other benchmark algorithms under high FD self-interference cancellation levels.

Full-Duplex (FD) Amplify-and-Forward (AF) Multiple-Input Multiple-Output (MIMO) relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with the inherent processing simplicity. However, when the impact of hardware distortions is considered, such relays suffer from a distortion-amplification loop, due to the inter-dependent nature of the relay transmit signal covariance and the residual self-interference covariance. The aforementioned behavior leads to a significant performance degradation for a system with a low or medium hardware accuracy. In this work, we analyse the relay transfer function as well as the Mean Squared- Error (MSE) performance of an FD-AF MIMO relay-assisted communication, under the consideration of collective sources of additive and multiplicative transmit and receive impairments. An optimization problem is then devised over the linear transmit and receive strategies to minimize the communication MSE and solved by employing the recently proposed Penalty Dual Decomposition (PDD) method. The proposed solution converges to a stationary point of the original problem via a sequence of quadratic convex programs. Numerical simulations verify the significance of the proposed distortion-aware design compared to the common simplified approaches, as the hardware accuracy degrades.

Spectrum sharing and full duplexing are two promising technologies for alleviating the severe spectrum crunch that has threatened to blight the progress of future wireless communication systems. In this paper, we consider a two tier coexistence framework involving a collocated multiple-input-multiple-output (MIMO) radar system (RS) and a full-duplex MIMO cellular system (CS). Considering imperfect channel state information and hardware impairments at the CS, we focus on a spectrum sharing environment to improve the quality of service (QoS) for cellular users by designing i) precoders at CS via the minimization of sum mean-squared-errors, subject to the constraints of transmit powers of the CS and probability of detection (PoD) of the RS, and ii) precoders at the RS to mitigate the interference from RS towards CS. While the monotonically increasing relationship between PoD and its non-centrality parameter is exploited to resolve the PoD in terms of interference threshold towards the RS, a generalized likelihood ratio test for target detection is used to derive detector statistics of the precoded radar waveforms. Numerical results demonstrate the feasibility of the proposed spectrum sharing algorithms, albeit with certain tradeoffs in RS transmit power, PoD and QoS of cellular users.

In this paper we investigate the co-existence between a multi-cell multi-user (MU) full-duplex (FD) multiple-input multiple-output (MIMO) cellular network and a MIMO radar. While a joint beam-forming design technique at the cellular base-stations and users is proposed to maximize the detection probability of the MIMO radar subject to constraints of data rate per user in each cell and transmit power, null-space based waveform projection is used to mitigate the interference from the radar towards the cellular network. In particular, the proposed technique optimizes the performance of detection probability by maximizing its lower bound, which is obtained by exploiting the monotonically increasing relationship of detection probability and its non-centrality parameter. Numerical results show the feasibility of spectrum sharing between both systems.

In this work we study the secrecy energy efficiency (SEE) maximization problem for a multi-carrier and multiple-input-multiple-output (MIMO) multiple eavesdroppers communication system. By utilizing the full-duplex (FD) operation, the system is simultaneously capable of bidirectional communication and jamming to the potential eavesdroppers. In particular, we opportunistically utilize different communication and jamming channel conditions at different subcarriers, raised due to multipath fading or large bandwidth, in order to improve the SEE. Due to the non-convex and non-smooth nature of the resulting optimization problem, we propose an iterative solution with a guaranteed convergence to a stationary point based on the successive inner approximation and Dinkelbach's algorithm. The numerical evaluations show a considerable improvement of the system SEE under the condition that the self-interference of the FD transceivers can be efficiently mitigated.

Full-duplex (FD) jamming transceivers recently have been shown to enhance the information security of wireless communication systems by simultaneously transmitting artificial noise (AN) while receiving information. In this paper, we investigate whether FD jamming can also improve the system's secrecy energy efficiency (SEE) in terms of securely communicated bits per Joule when considering the additional power used for jamming and self-interference (SI) cancellation. Moreover, the degrading effect of the residual SI is also taken into account. In this regard, we formulate a set of SEE maximization problems for a FD multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, considering both cases where exact or statistical channel state information (CSI) is available. Due to the intractable problem structure, we propose iterative solutions in each case with a proven convergence to a stationary point. Numerical simulations indicate only a marginal SEE gain, through the utilization of FD jamming, for a wide range of system conditions. However, when SI can efficiently be mitigated, the observed gain is considerable for scenarios with a small distance between the FD node and the eavesdropper, a high signal-to-noise ratio (SNR), or for a bidirectional FD communication setup.

In this paper, spectrum sharing between a multiple-input-multiple-output (MIMO) radar and a full duplex (FD) multi-user MIMO (MU-MIMO) cellular system is studied. While a joint transceiver design technique at a hardware impaired FD cellular system considering imperfect channel state information is developed to maximize the detection probability of the MIMO radar, null-space based waveform projection is proposed to mitigate the interference from the radar towards the cellular system. The proposed technique exploits the monotonically increasing relationship of detection probability and its non-centrality parameter and accordingly optimizes the performance of detection probability of the radar, while also providing the cellular users with specific quality-of-service (QoS). Numerical results demonstrate the feasibleness of spectrum sharing between both systems, albeit certain trade-offs in radar transmit power, detection probability and QoS of users in the cellular system.

We propose a novel transceiver design technique to facilitate flexible spectrum sharing between a multiple-input multiple-output (MIMO) radar and a full-duplex (FD) MIMO cellular system. The optimization problem for maximizing the rate of the cellular system is formulated, subject to the constraints of individual power at the uplink users, total power at the base station, and interference power towards the MIMO radar from the cellular system so that the detection probability of the radar is not hindered. We show that the above problem can be cast as a second-order cone programming problem and the joint design of transceiver matrices can be obtained through an iterative algorithm. Numerical results show that using the spectrum shared by the radar, the FD cellular system can achieve sum rate of up to 25-30 bits/sec/Hz for a reasonable self-interference cancellation of around -70 dB. However, to facilitate this, while also maintaining a detection probability of around 0.9, the radar needs to spend an extra power of around 2-3 dB.

In this work, joint design of fronthaul compression and precoding in cloud radio access networks (C-RANs) is studied for full-duplex (FD) multiple-input multiple-output (MIMO) underlay cognitive radio networks. In this system, multiple secondary uplink (UL) and downlink (DL) users equipped with multiple antennas communicate with a control unit (CU) in the “cloud” through a set of multi-antenna secondary FD radio units (RU) which are connected to the CU through limited capacity links. We address the sum-rate maximization problem subject to UL and DL fronthaul rate constraints at each RU, power constraints at each RU and UL users, and maximum allowed interference at the primary users. Casting this non-convex problem as a difference of convex (DC) problem, an iterative algorithm based on the Majorization Minimization (MM) approach that guarantees convergence to a stationary point is proposed. Numerical results demonstrate the advantage of the proposed algorithm.

In this work we study the secrecy energy efficiency (SEE) of a multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, in terms of the securely communicated bits-per-Joule, where the legitimate receiver is equipped with full-duplex (FD) capability. In particular, we seek answer to the question: if and how the application of an FD jammer can enhance the system SEE, considering the additional power consumption used for jamming and self-interference cancellation, as well as the degrading effect of residual self-interference. In this regard, an SEE maximization problem is formulated. Due to the intractable problem structure, an iterative solution is provided with a guaranteed convergence to a local optimum. Moreover, the proposed solution is extended for a system with a bidirectional communication, where both legitimate nodes are equipped with FD capability. Numerical simulations indicate a marginal SEE gain, via the utilization of FD jamming, for a wide range of system conditions. However, the observed gain is significant for the scenarios with a small distance between the FD node and the eavesdropper, a high signal-to-noise ratio (SNR) condition or for a bidirectional FD communication setup, under the condition that the self-interference can be effectively and efficiently mitigated.

In this work we address the joint design of the wireless backhauling network topology, as well as the frequency/power allocation on the wireless links, where the nodes are capable of full-duplex (FD) operation. The proposed joint design enables the coexistence of multiple access/backhaul links at the same channel, resulting in an enhanced spectral efficiency. Moreover, it enables the usage of FD capability when/where it is gainful. In this regard, a mixed-integer-linear-program (MILP) design framework is proposed, aiming at a minimum cost design for the wireless backhaul network, considering the required rate demand at each base station, as well as the various operational limits. The numerical simulations show a reduction in the network cost via the utilization of the proposed designs, thanks to the coexistence of multiple links on the same channel due to the FD capability.

In this paper, the feasibility of spectrum sharing between a multiple-input multiple-output (MIMO) radar system (RS) and a MIMO cellular system (CS), comprising of a full-duplex (FD) base station (BS) serving multiple downlink and uplink users at the same time and frequency is investigated. While a joint transceiver design technique at the CS's BS and users is proposed to maximize the probability of detection (PoD) of the MIMO RS, subject to constraints of quality of service (QoS) of users and transmit power at the CS, null-space based waveform projection is used to mitigate the interference from RS toward CS. In particular, the proposed technique optimizes the performance of PoD of RS by maximising its lower bound, which is obtained by exploiting the monotonically increasing relationship of PoD and its non-centrality parameter. The numerical results show the utility of the proposed spectrum sharing framework, but with certain tradeoffs in performance corresponding to RS's transmit power, RS's PoD, CS's residual self-interference power at the FDBS and QoS of users.

In this paper, we consider a full-duplex (FD) and amplify-and-forward (AF) relay with multiple antennas, where hardware impairments of the FD relay are taken into account. Due to the inter-dependence of the transmit relay power and the residual self-interference in an FD-AF relay, we observe a distortion loop that degrades the system performance when relay dynamic range is not high. In this paper, we analyze the relay function, and an optimization problem is formulated to maximize the signal to distortion-plus-noise ratio under relay and source transmit power constraints. Due to the problem complexity, we propose a gradient-projection-based (GP) algorithm to obtain an optimal solution. Moreover, a non-alternating sub-optimal solution is proposed by assuming a rank-1 relay amplification matrix, and separating the design of the relay process into multiple stages (MuStR1). The proposed MuStR1 method is then enhanced by introducing an alternating update over the optimization variables, denoted as AltMuStR1 algorithm. Numerical simulations show that compared to GP, the proposed (Alt)MuStR1 algorithms significantly reduce the required computational complexity at the expense of a slight performance degradation. Moreover, as the hardware impairments increase, or for a system with a high transmit power, the impact of applying a distortion-aware design is significant.

In this paper we address a worst-case weighted sum rate maximization problem for a full-duplex (FD) and point-to-point (P2P) system. The effects of channel-state information (CSI) error, as well as the signal distortion due to hardware impairments are jointly taken into account. Due to the intractable structure of the resulting problem, a weighted minimum mean squared error (WMMSE) method is applied to cast the rate maximization into a separately convex optimization problem, which can be iteratively solved with a guaranteed convergence. The provided rate maximization framework is also shown to provide a converging minimum mean squared error (MMSE) design as a special case. Moreover, a methodology to obtain the least favorable error matrices is proposed by casting the resulting non-convex quadratic optimization into a convex problem. The achievable guaranteed (worst-case) rate is then numerically studied, over different levels of CSI error intensity, transceiver accuracy, and available transmit power.

In this paper we address the linear precoding and decoding design problem for a bidirectional orthogonal-frequency-division-multiplexing (OFDM) communication system, between two multiple-input-multiple-output (MIMO) full-duplex (FD) nodes. The effects of hardware distortions, leading to residual self-interference and inter-carrier leakage, are taken into account. In the first step, the operation of a FD MIMO OFDM transceiver is modeled under the impact of known hardware impairments. An alternating quadratic convex program (AltQCP) is then provided to obtain a minimum-mean-squared-error (MMSE) design for the defined system. The proposed design is then extended to maximize the system sum rate, applying the weighted-MMSE (WMMSE) method. The proposed AltQCP methods result in a monotonic improvement, leading to a necessary convergence to a stationary point. Finally, the performance of the defined system is evaluated under various system conditions, and in comparison to the other approaches in the literature. A significant gain is observed via the application of the proposed method as the hardware inaccuracy, and consequently inter-carrier leakage, increases.

We study a full duplex (FD) multiple-input multiple-output (MIMO) cognitive cellular network, in which a secondary base-station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) secondary users (SUs) operating in half-duplex (HD) mode simultaneously. The spectrum is shared between secondary and primary networks, and thus uplink SUs and secondary BS generate interference on primary users (PUs). We assume that the channel state information (CSI) available at the transmitters is imperfect, and the errors of the CSI are assumed to be norm bounded. Under the impact of channel uncertainty, we address the robust minimization of the sum of mean-squared-errors (MSE) of all estimated symbols subject to power constraints at the uplink SUs and secondary BS, and interference constraints projected to each PU. We show that this problem can be cast as a Semidefinite programming (SDP), and joint design of transceiver matrices can be obtained through an iterative algorithm. Numerical results are presented to show the effectiveness and robustness of the proposed robust algorithm.

Energy consumption in wireless communication systems is exponentially increasing due to growing wireless traffic. Combating adverse effects of excessive energy consumption calls for energy- aware system design, leading to a new research paradigm, green communications. To achieve energy awareness in a full-duplex (FD) bi-directional multiple antenna system, in this paper, we study a Quality-of-Service (QoS) problem, where the total transmit power is minimized subject to minimum rate constraints at each node, and propose two algorithms. We first apply a penalty-based method in order to obtain an efficient optimization strategy. Afterwards, in the second algorithm, we generalize the well-known relationship between Weighted-Sum-Rate (WSR) andWeightedMinimum-Mean- Squared-Error (WMMSE) problems, originally used to solve the sum-rate maximization problem, to tackle the sum-power minimization problem.

We consider a single cell multiple-input multiple-output (MIMO) system, where a base-station (BS) operating in full-duplex (FD) mode serves multiple uplink (UL) and downlink (DL) mobile users operating in half-duplex (HD) mode, simultaneously. The self-interference at the BS, and co-channel interference (CCI) from UL users to DL users are both taken into account. We consider the transmit and receive filter design for sum- rate maximization subject to a sum-power constraint at the BS and individual power constraints at each UL user of the system under the limited dynamic range considerations at the transmitters and receivers. By reformulating this non-convex problem as an equivalent component-wise convex optimization problem with the addition of two auxiliary variables, we propose a low complexity algorithm which converges to a stationary point.

In this paper, we address the energy efficiency maximization problem for a distributed passive radar system, with application in signal classification. Two energy efficiency maximization strategies are studied. Firstly, the total power consumption of the network is minimized, while maintaining a required classification quality. We show that the resulting optimization problem has a similar solution structure to the famous water-filling algorithm and can be obtained analytically. Secondly, the energy efficiency of the network is viewed as the ratio of the observed useful information to the total energy consumption of the network for each estimation process. The optimal solution for the latter case is achieved by converting the original problem into an iterative convex feasibility check with a guaranteed convergence to optimality. Finally, the optimal behavior of the defined system in terms of energy efficiency is examined with respect to different system parameters and design approaches by performing extensive numerical simulations.

We investigate the power allocation problem in distributed sensor networks that are used for target object classification. In the classification process, the absence, the presence, or the type of a target object is observed by the sensor nodes independently. Since these local observations are noisy and thus unreliable, they are fused together as a single reliable observation at a fusion center. The fusion center uses the best linear unbiased estimator to accurately estimate the reflection coefficient of target objects. We utilize the average deviation between the estimated and the actual reflection coefficient as a metric for defining the objective function. First, we demonstrate that the corresponding optimization of the power allocation leads to a signomial program which is in general quite hard to solve. Nonetheless, by using the proposed system model, fusion rule and objective function, we are able to optimize the power allocation analytically and can hence present a closed-form solution. Since the power consumption of the entire network may be limited in various aspects, three different cases of power constraints are discussed and compared with each other. In addition, a sensitivity analysis of the optimal power allocation with respect to perfect and imperfect parameter knowledge is worked out.