Secure and Green RSMA-Assisted Heterogeneous ISAC

TL;DR

This paper proposes a novel RSMA-assisted ISAC framework that enhances security and energy efficiency by joint beamforming optimization, addressing interference and security challenges in heterogeneous networks.

Contribution

It introduces a joint optimization approach for beamforming and interference management in RSMA-assisted ISAC, improving security, energy efficiency, and robustness.

Findings

The proposed scheme outperforms state-of-the-art methods in efficiency and robustness.

Joint beamforming optimization significantly improves security rate and reduces power consumption.

Simulation results demonstrate enhanced sensing and communication performance.

Abstract

This paper investigates sensing, communication, security, and energy efficiency of the heterogeneous integrated sensing and communication networks under challenging operational conditions. We focus on scenarios in which communication performance, security, and sensing accuracy are degraded by interference, eavesdropping, and imperfect channel state information. To this end, we analyze communication and sensing signals within ISAC framework as well as the communication signals of a multicast network based on \emph{rate-splitting multiple access} (RSMA). Then, sensing signal-to-cluster-plus-noise ratio, communication rate, security rate, and \emph{security energy efficiency} (SEE) are evaluated. To simultaneously enhance these system performances, we propose a targeted optimization framework aimed at maximizing SEE. This framework characterizes the sensing-security trade-off by jointly optimizing the transmit \emph{beamforming} (BF) vectors and the echo BF vector to construct green interference using the echo signal, as well as common and private streams generated by RSMA. Particularly, the joint design improves the security rate and reduces power consumption, thereby enabling a higher SEE. Given the non-convex nature of the optimization problem, we present an alternative approach that leverages Taylor series expansion, majorization-minimization, semi-definite programming, and successive convex approximation techniques. Specifically, we decompose the original non-convex and intractable optimization problem into three simplified sub-optimization problems, which are iteratively solved using an alternating optimization strategy. Simulations provide comparisons with state-of-the-art schemes, highlighting the superior efficiency, robustness, and scalability of the proposed joint multi-BF optimization scheme based on RSMA and green interference in improving system performances.