Information and sensing beamforming optimization for multi-user multi-target MIMO ISAC systems

Abstract

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.

School Mathematics Textbooks in China

“…Furthermore, we evaluate the performance of an existing stateof-the-art algorithm [24] for solving the original nonconvex problem (7), which is called WMMSE-SDR. However, the WMMSE-SDR algorithm is only guaranteed to find a stationary point of problem (7).…”

“…2. Comparison of the proposed B&B algorithm with the WMMSE-SDR algorithm [24] in terms of the objective value.…”

“…As described in [19], the numerical efficiency as well as the convergence speed of the B&B algorithm heavily relies on the quality of upper and lower bounds for the optimal value [16,21,22,23]. Recently, the work in [24] utilized a combination of the weighted mean square error minimization (WMMSE) and the SDR techniques to achieve a Karush-Kuhn-Tucker point for the problem. But this algorithm is not guaranteed to find the globally optimal solution of the problem.…”

Simultaneous Beam Sweeping for Multi-Beam Integrated Sensing and Communication

“…Regarding the performance metrics, it is popular to use the Fisher information [18], [19], and its reciprocal, i.e., the Cramér-Rao bound (CRB) [13]- [15], to evaluate the performance of sensing. In contrast, the data rate is widely considered for communications [13], [14]. Signal-to-noise ratio (SNR) is another common metric for sensing [8]- [10] and communications [20]- [22].…”