Model-Based Machine Learning for Max-Min Fairness Beamforming Design in JCAS Systems

TL;DR

This paper presents a novel model-based machine learning approach for beamforming in JCAS systems, optimizing fairness between communication and sensing, and demonstrating improved scalability and performance over traditional methods.

Contribution

It introduces a machine learning algorithm based on alternating optimization for fair beamforming in multi-user JCAS systems, addressing non-convex challenges.

Findings

The proposed algorithm scales well with the number of users.

It achieves better performance with shorter run time.

It effectively balances communication and sensing objectives.

Abstract

Joint communications and sensing (JCAS) is expected to be a crucial technology for future wireless systems. This paper investigates beamforming design for a multi-user multi-target JCAS system to ensure fairness and balance between communications and sensing performance. We jointly optimize the transmit and receive beamformers to maximize the weighted sum of the minimum communications rate and sensing mutual information. The formulated problem is highly challenging due to its non-smooth and non-convex nature. To overcome the challenges, we reformulate the problem into an equivalent but more tractable form. We first solve this problem by alternating optimization (AO) and then propose a machine learning algorithm based on the AO approach. Numerical results show that our algorithm scales effectively with the number of the communications users and provides better performance with shorter run time compared to conventional optimization approaches.