Dual-function radar andcommunication (DRC) system has been recently recognized as a promising approach to solve the spectrum scarcity problem. However, when the target exists within a crowded area where pathloss dominating, the performance of radar may be severely degraded. To tackle this issue, this article proposes for the first time the deployment of an intelligent reflecting surface (IRS) to help the DRC system to enhance the radar detection performance. The IRS can configure the environment around the radar by adaptively adjusting the phases of its reflecting units to strengthen the signal quality toward specific directions, mostly the target direction, and completely null-out transmissions in other directions, mostly the directions toward the communication system. Specifically, in this article, we investigate the joint optimization of the IRS passive phase-shift matrix (PSM) and precoding matrix of the radar-aided basestation for the DRC system. The optimization is carried-out through maximizing the signal-to-noise ratio (SNR) at the radar receiver under both sensing and communication constraints, which turns out to be a nonconvex problem. In order to circumvent this challenging problem, an alternation optimization approach is employed to decouple the optimization variables and split this intractable problem into two subproblems. However, it is still challenging to obtain the optimal PSM due to the high power of the objective function and the unit-modulus constraints. To solve this problem, a majorization–minimization algorithm is conceived to transform the nonconvex problem to an easy to solve quadratic constraint quadratic programming problem. Simulation resultsdemonstrate that the IRS can help improving the performance of the DRC system in terms of the received SNR, and the proposed algorithm shows fast convergence.

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.

Due to the flourishing development of vehicle-to-vehicle (V2V) communications and autonomous driving, interference between radar sensing and communication signals becomes a challenging issue. We propose a transmit beamforming based spectrum sharing scheme to achieve peaceful coexistence between automotive multiple-input multiple-out (MIMO) radar and communication systems. Our objective is to maximize the signal-to-interference-plus-noise ratio (SINR) of the automotive radar receiver subject to the communication capacity and the transmitted power budget constraints to optimize both the communication covariance matrix and the radar transmit precoder. The formulated optimization problem is non-convex, which is converted to convex by introducing a new slack variable and then solving it using the block coordinate descent, also called alternation optimization, approach. Additionally, the ellipsoid sub-gradient method is then employed to reduce the computational complexity. Simulation results demonstrate that our proposed scheme outperforms the conventional schemes.

Coexisting radar and communication (CRC) systems on the same spectrum band have been recently recognized as a promising approach to solve the spectrum scarcity problem. Additionally, with the new requirements of achieving higher energy efficiency and restoring the consumed power, an initiative has been exploited to embed both information and energy within the same signal. Motivated by that, this paper presents a unified model to implement the concept of simultaneous wireless information and power transfer (SWIPT) in the coexistence environment between radar and communication systems. Specifically, we propose a novel energy harvesting based CRC model (CRCEH), in which the signals transmitted from both the communication and radar transmitters are precoded towards their conventional targets, communication receiver and radar targets respectively, as well as to an energy harvesting receiver. This system is capable of solving the spectrum scarcity problem as well as supporting low power devices with their energy requirements from the energy harvested at the energy harvesting receiver. The design problem is formulated as maximizing the radar detection probability subject to communication capacity and energy harvesting constraints. However, directly processing the data from the radar receiver requires high computational complexity and hardware costs. In order to circumvent this issue, this paper also proposes to deploy lens antenna array (LNA) to CRCEH system to sharply direct the signals to the energy harvesting receiver together with their respective destinations. Since LNA is able to focus the signal power on a subset of antennas, we may only deal with the data from the selective antenna subset associated with the maximal power. Numerical results show that the proposed method is able to achieve improved performance in terms of radar detection under communication capacity constraints compared to the conventional approaches besides the great benefit of harvesting the energy to feed some limited energy devices.

Optimum performance of co‐existing radar and communication (CRC) system is a challenging task when target and user exist within a crowded area where path‐loss is dominant. Inspired by the application of intelligent reflecting surface (IRS) in reconstructing the wireless transmission environment, this paper investigates deploying IRS to the CRC system to pursue performance improvement. Particularly, we consider an IRS‐assisted CRC system where the IRS not only provides an indirect communication path but inevitably introduces additional interfering paths. Our goal is to maximize the radar signal‐to‐interference‐plus noise ratio (SINR) by jointly optimizing the transmit beamform and the phase of IRS while satisfying the user SINR, the total transmit power at the radar and base station, the restriction of IRS phaseshift. An efficient alternative optimization algorithm combining the second‐order cone programming and semidefinite programming optimization methods is exploited to solve the complicated non‐convex unit‐norm problem. Simulation results reveal the advantages of deploying IRS in the CRC system and the effectiveness of our proposed algorithm.

The achievement of optimum performance of a co-existing radar and communication (CRC) system is a challenging task when the channel state information (CSI) for the interference channels is imperfect, particularly when the power of the CRC system is restricted. To address this issue, this article proposes a robust CRC system design that considers CSI uncertainty. Specifically, the objective is to design a robust beamforming scheme to maximize the radar performance subject to the user signal-to-interference-plus-noise ratio （SINR）and the transmit power of the CRC system. Due to the infinite number of constraints that result from the CSI uncertainty of the interference channels, a loosely bound robust approach is developed, which employs the shrinkage method to convert the infinite number of constraints into a finite number of constraints. However, the performance of the loosely bound robust approach is not satisfactory. Therefore, this paper further proposes a sub-optimal robust approach that employs the S-procedure to transform the infinite number of inequality constraints into equivalent finite linear matrix inequalities (LMIs). The simulation results indicate that the design of the robust CRC system can significantly improve the system performance compared to the traditional design approach.