Joint Transceiver Optimization for MmWave/THz MU-MIMO ISAC Systems

TL;DR

This paper proposes a low-complexity joint transceiver design for mmWave/THz MU-MIMO ISAC systems, balancing communication and sensing performance with analytical solutions and hybrid beamforming.

Contribution

It introduces a novel low-dimensional subspace approach and analytical solutions for joint transceiver optimization in mmWave/THz ISAC systems, demonstrating hybrid beamforming's effectiveness.

Findings

Hybrid beamforming achieves similar performance to fully digital precoding.

Proposed algorithms balance communication and sensing tasks effectively.

Simulation shows mild performance loss when optimizing for both tasks.

Abstract

In this paper, we consider the problem of joint transceiver design for millimeter wave (mmWave)/Terahertz (THz) multi-user MIMO integrated sensing and communication (ISAC) systems. Such a problem is formulated into a nonconvex optimization problem, with the objective of maximizing a weighted sum of communication users' rates and the passive radar's signal-to-clutter-and-noise-ratio (SCNR). By exploring a low-dimensional subspace property of the optimal precoder, a low-complexity block-coordinate-descent (BCD)-based algorithm is proposed. Our analysis reveals that the hybrid analog/digital beamforming structure can attain the same performance as that of a fully digital precoder, provided that the number of radio frequency (RF) chains is no less than the number of resolvable signal paths. Also, through expressing the precoder as a sum of a communication-precoder and a sensing-precoder, we develop an analytical solution to the joint transceiver design problem by generalizing the idea of block-diagonalization (BD) to the ISAC system. Simulation results show that with a proper tradeoff parameter, the proposed methods can achieve a decent compromise between communication and sensing, where the performance of each communication/sensing task experiences only a mild performance loss as compared with the performance attained by optimizing exclusively for a single task.