Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming

TL;DR

This paper introduces a fairness-aware secure integrated sensing and communications system that optimizes secrecy and fairness among users using fractional programming and quadratic transform techniques.

Contribution

It presents a novel optimization framework with entropy-regularized fairness and an efficient solution method for secure, fair ISAC systems.

Findings

Improved average secrecy rate and data rate

Enhanced beam gain performance

Robust security and fairness in resource allocation

Abstract

We propose a novel secure integrated sensing and communications (ISAC) system designed to serve multiple communication users (CUs) and targets. To that end, we formulate an optimization problem that maximizes the secrecy rate under constraints balancing both communication and sensing requirements. To enhance fairness among users, an entropy-regularized fairness metric is introduced within the problem framework. We then propose a solution employing an accelerated quadratic transform (QT) with a non-homogeneous bound to iteratively solve two subproblems, thereby effectively optimizing the overall objective. This approach ensures robust security and fairness in resource allocation for ISAC systems. Finally, simulation results verify the performance gains in terms of average secrecy rate, average data rate, and beam gain.