Beamforming Design for Max-Min Fairness Performance Balancing in ISAC Systems

TL;DR

This paper proposes novel beamforming strategies for ISAC systems that optimize fairness among users and balance sensing and communication performance, addressing complex non-convex optimization challenges.

Contribution

It introduces two efficient methods, including a fractional programming approach and a new technique for non-smooth optimization, to enhance beamforming design in ISAC systems.

Findings

Proposed methods improve fairness and performance balance.

Convex sub-problems enable efficient optimization.

Numerical results validate the effectiveness of the approaches.

Abstract

Integrated sensing and communications (ISAC) is envisioned as a key technology for future wireless communications. In this paper, we consider a downlink monostatic ISAC system wherein the base station serves multiple communications users and sensing targets at the same time in the presence of clutter. We aim at both guaranteeing fairness among the communications users while simultaneously balancing the performances of communications and sensing functionalities. Therefore, we optimize the transmit and receive beamformers to maximize the weighted minimum signal-to-interference and clutter-plus-noise ratios. The design problem is highly challenging due to the non-smooth and non-convex objective function and strongly coupled variables. We propose two efficient methods to solve the problem. First, we rely on fractional programming and transform the original problem into convex sub-problems, which can be solved with standard convex optimization tools. To further reduce the complexity and dependence on numerical tools, we develop a novel approach to address the inherent non-smoothness of the formulated problem. Finally, the efficiencies of the proposed designs are demonstrated by numerical results.