Multi-Objective Optimization-based Transmit Beamforming for Multi-Target and Multi-User MIMO-ISAC Systems
Chunwei Meng, Zhiqing Wei, Dingyou Ma, Wanli Ni, Liyan Su, and Zhiyong, Feng

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…
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