The automotive industry has witnessed flourishing development in the last few years with the hope of achieving fully autonomous driving. In parallel, dual-function radar-communication (DFRC) systems are being developed to tackle the spectrum scarcity challenge and integrate radar and communication transceivers into a unified system. However, with a DFRC system, critical information may be intercepted by unauthorized people in the target vehicle. Therefore, the physical layer security of the DFRC system needs to be given considerable attention. To improve the security of information transmission, artificial noise (AN) is used to interfere with eavesdroppers. The main objective of this work is to maximize the secrecy rate of vehicles subject to both radar signal-to-interference-plus-noise ratio (SINR) and communication transmit power constraints. To overcome the difficulty caused by the existence of the nonconvex Shannon capacity formula in the formulated problem, we convert the secrecy rate optimization to an equivalent bi-convex optimization problem. Furthermore, due to the nonconvexity of the formulated design problem, we exploit an inexact block coordinate descent (IBCD) algorithm based on semi-definite Programming (SDP) to obtain the optimal solution. Moreover, to reduce the complexity of the SDP-based IBCD algorithm, a bi-search-based IBCD algorithm is employed for single target scenarios. Finally, numerical simulations are used to demonstrate the superiority of using the AN approach to enhance the performance of the DFRC system in terms of the secrecy rate.

This article presents accurate approximation expressions for the outage and secrecy outage probabilities of relay-assisted free-space optical (FSO) communication links utilizing unmanned aerial vehicles (UAVs). We consider the effects of weather attenuation and random fluctuations of UAVs’ orientations and positions. The obtained expressions are applicable to different types of intensity modulation direct detection techniques. We utilized$L$-ary multipulse pulse-position modulation$(L$MPPM) scheme and optimized link performance by adjusting the$L$MPPM settings using our derived expressions. We conduct numerical analyses to evaluate the impact of system settings, including beam divergence and modulation settings, on outage and secrecy outage probabilities. Our results demonstrate that adaptive$L$MPPM can improve average spectral efficiency, particularly in time-varying UAV-based FSO channels, while maintaining the same outage and secrecy outage probabilities as ordinary multipulse pulse-position modulation. Moreover, we investigate the effect of an eavesdropper’s location on the probability of a secure channel between legitimate users. We validate the accuracy of our expressions by comparing them with Monte Carlo (MC) simulation results under different channel conditions. Derived expressions provide efficient and accurate evaluation results to optimize the performance of UAV-based relay-assisted FSO communication systems.

This paper addresses the problem of co-existence between a radar and a communication system that share the same frequency band. In particular, we investigate the role of a reconfigurable intelligent surface (RIS) in improving the performance of both systems and facilitating their co-existence. We consider the optimization problem of maximizing the radar signal to interference plus noise ratio (SINR) with respect to the active transmit beamformer at the radar, the passive RIS reflection coefficients, and the transmit covariance matrices of the communication system, subject to communication outage as well as radar and communication power constraints. This problem is solved through an alternating maximization procedure, first in the ideal scenario of perfect channel state information (CSI), and then in the case of incomplete CSI, using a statistical model of the CSI uncertainty. Numerical results demonstrate the effectiveness of our approach and quantify the beneficial effect of an RIS on the co-existence of a radar and a communication system.

Vehicular communications have witnessed remarkable achievements throughout the last few years on the way of providing reliable, safe, and affordable travel experience. Such achievements have not only skyrocketed the demand to the current services of vehicular communications, but also exploited many new services. To meet different Quality-of-Service (QoS) requirements of vehicular communications services, we assume the adoption of the hybrid OMA/NOMA-enabled access scheme as well as deploying intelligent reflecting surface (IRS) in such highly dynamic environments. We focus on maximizing the total system sum rate via jointly optimizing the transmit power allocation, the IRS’s phase shift, and the vehicle active beamforming under different QoS levels, in terms of reliability and rate constraints of vehicles. To tackle such an optimization problem, we divide the original problem into three subproblems, and propose an efficient iterative suboptimal algorithm. Specifically, the successive convex approximation (SCA) approach is applied to turn this original nonconvex problem into separate power allocation, IRS phase shift, and active transmit beamforming optimization subproblems. Simulation results demonstrate that the proposed algorithm converges fast, and the proposed design can enhance the total system sum rate through enhancing the rate within cell-edge zones regardless of the vehicle speeds.

Active reconfigurable intelligent surfaces (RISs) are a novel and promising technology that allows controlling the radio propagation environment while compensating for the product path loss along the RIS-assisted path. In this letter, we consider the classical radar detection problem and propose to use an active RIS to get a second independent look at a prospective target illuminated by the radar transmitter. At the design stage, we select the power emitted by the radar, the number of RIS elements, and their amplification factor in order to maximize the detection probability for a fixed probability of false alarm and a common (among radar and RIS) power budget. An illustrative example is provided to assess the achievable detection performance, also in comparison with that of a radar operating alone or with the help of a passive RIS.

One of the most challenging issues in non-orthogonal multiple access (NOMA) based cognitive radio networks (CRNs) is the power allocation (PA) problem. Moreover, the quality of service (QoS) constraints of both the primary and secondary users can not be neglected when dealing with the PA problem. In this paper, a two-tier PA algorithm is proposed for NOMA-based CRNs to make the best use of the inherited characteristics of power domain based NOMA technique. Specifically, the proposed algorithm is able to solve the PA problem in NOMA-based CRNs under primary user (PU) QoS constraints through setting up an overall power constraint for the secondary users (SUs), this represents the first tier. Additionally, the PA among the SUs, representing the second tier, is accomplished based on their channel gains. The performance of the proposed PA approach with NOMA-based CRNs is compared with the fractional transmit power control technique which is implemented typically in conventional orthogonal multiple access based CRNs, in terms of the number of admitted SUs and the outage probability. The simulation results have confirmed the superiority of NOMA-based networks compared to their OMA counterparts due to the much better PA in NOMA-based CRNs compared to OMA-based ones.

Multiple-input multiple-output (MIMO) radar systems have a wide range of applications not only in military fields but also in civilian areas. This wide applicability of MIMO radars is due to their improved spatial resolution and flexibility of designing their transmit beampatterns. Motivated by this and the increasing interest in obtaining the optimum beampattern through designing the covariance matrix of the transmit waveform, this paper presents two new algorithms for designing the beampattern of MIMO radar systems through optimizing the covariance matrix for the transmitting waveform. The first algorithm is based on the iteratively reweighted least squares of the deviation errors between the designed and desired beampatterns. The second one depends on the least absolute shrinkage selection operator (LASSO), which shrinks some deviation errors elements and sets others to zero, hence achieving a good trade-off between the sparsity and errors of the devised beampattern. Numerical simulations are carried out to prove the superiority of our proposed algorithms in case of both symmetric and non-symmetric beampatterns.

Interference alignment (IA) emerged on the communication scene as a solution to the interference problem in all interference-limited networks, including heterogeneous cellular systems. However, the performance of IA is greatly related to the accuracy of the channel state information at transmitters (CSIT), namely the number of feedback bits. Accordingly, in order to improve the performance of IA, it would be useful to analyze the number of feedback bits with respect to the sum rate loss. Motivated by that, this paper studies a limited feedback-based IA scheme suitable for two tier macrocell-femtocell heterogeneous networks. First, an approximate analytical expression for the upper bound on the total sum rate loss due to limited feedback in the studied IA system, is derived. Then, a simulation based evaluation of the sum-rate loss due to the implementation of limited feedback IA in heterogeneous networks is obtained. Simulation results confirmed the severe effect of quantization of CSI on the interference alignment performance.