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Integrated sensing and communication (ISAC) has been envisioned as a key enabler in the next-generation wireless networks. In this paper, we consider the joint information and sensing beamforming design in a multi-user and multi-target multi-input multi-output ISAC system, where a transmit BS and a sensing BS collaborate to sense targets, and the transmit BS sends information streams to communication users at the same time. To optimize the sensing performance and guarantee the communication throughput, we formulate a joint beamforming design problem to minimize the trace of the weighted Cramer–Rao bound of target parameters subject to the sum-rate constraint. The problem is challenging to solve due to the intricate non-convex objective function and constraints. We firstly exploit the weighted mean square error minimization (WMMSE) and semidefinite relaxation (SDR) techniques to devise a WMMSE–SDR algorithm that can achieve a KKT point of the problem. The SDR can be shown to be tight for a subproblem in the WMMSE–SDR algorithm, which implies zero duality for the subproblem. Based on this property and fractional programming techniques, we further reformulate the beamforming problem as a min–max form with simple constraints which then can be efficiently solved by first-order min–max optimization algorithms. Finally, the proposed algorithms are evaluated extensively in simulations. Numerical results show that both proposed algorithms can achieve promising performance in sensing and communication, and the low-complexity algorithm has a significantly reduced computation time. [ABSTRACT FROM AUTHOR]

As more challenging demands and potential applications increase in the sixth generation (6G) wireless networks, the future of mobile communications is exciting. Recently proposed intelligent reflecting surface (IRS) is a promising technology for improving the communication performance and building a programmable wireless propagation environment. However, high-precision configuration or perfect phase estimation of the IRS is unfeasible. In this paper, we study the IRS-aided millimeter wave (mmWave) multi-user multi-input multi-output (MIMO) systems with phase errors. Our objective is to maximize the achievable sum rate by joint optimizing the finited-resolution passive beamforming at the IRS, combiner, precoder, and power allocation. We first propose an iteratively sequential update (ISU) algorithm which can significantly reduce the complexity compared with the traditional exhaustive search algorithm (ESA) and cross-entropy (CE) method. Then we propose to use the genetic algorithm (GA) to further enhance the performance. The proposed algorithms can meet the requirements of low complexity and high performance respectively. Simulation results show that the IRS-aided systems can improve communication performance and coverage significantly compared with the conventional system. In addition, we observe that the system performance will not increase continuously with the number of the IRS elements when the total area of the IRS is fixed. [ABSTRACT FROM AUTHOR]

This paper describes a multiuser scenario with several terminals acceding simultaneously to the same frequency channel. The objective is to design an optimal multiuser system that may be used as a comparative framework when evaluating other suboptimal solutions and to contribute to the already published works on this topic. The present work assumes that a centralized manager knows perfectly all the channel responses between all the terminals. According to this, the transmitters and receivers, using antenna arrays and leading to the so-called multiple-input-multiple-output (MIMO) channels, are designed in a joint beamforming approach, attempting to minimize the total transmit power subject to quality of service (QoS) constraints. Since this optimization problem is not convex, the use of the simulated annealing (SA) technique is proposed to find the optimum solution.