Multi-Objective Optimization-based Transmit Beamforming for Multi-Target and Multi-User MIMO-ISAC Systems

TL;DR

This paper develops a multi-objective optimization framework for transmit beamforming in MIMO-ISAC systems, balancing multi-user communication and multi-target sensing performance, validated through simulations.

Contribution

It introduces a novel Pareto boundary-based optimization approach that jointly considers communication and sensing metrics in MIMO-ISAC systems.

Findings

Proposed method achieves improved tradeoff between communication and sensing.

Using sensing MI upper bound enhances multi-target resolution.

Simulation confirms better performance compared to existing methods.

Abstract

Integrated sensing and communication (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this paper, we investigate transmit beamforming design for multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and communication users. A general form of multi-target sensing mutual information (MI) is derived, along with its upper bound, which can be interpreted as the sum of individual single-target sensing MI. Additionally, this upper bound can be achieved by suppressing the cross-correlation among reflected signals from different targets, which aligns with the principles of adaptive MIMO radar. Then, we propose a multi-objective optimization framework based on the signal-to-interference-plus-noise ratio of each user and the tight upper bound of sensing MI, introducing the Pareto boundary to characterize the achievable communication-sensing performance boundary of the proposed ISAC system. To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between communication users and radar targets. Subsequently, the bisection search method is employed to find a specific Pareto optimal solution by solving a series of convex feasible problems. Finally, simulation results validate that the proposed method achieves a better tradeoff between multi-user communication and multi-target sensing performance. Additionally, utilizing the tight upper bound of sensing MI as a performance metric can enhance the multi-target resolution capability and angle estimation accuracy.