A Physical Layer Security Framework for IRS-Assisted Integrated Sensing and Semantic Communication Systems

TL;DR

This paper introduces a physical layer security framework for IRS-assisted integrated sensing and semantic communication systems, optimizing beamforming and phase shifts to enhance secrecy and sensing accuracy simultaneously.

Contribution

It proposes a novel joint design of beamforming and IRS phase shifts using multi-objective optimization, combining artificial noise, dedicated sensing signals, and wiretap coding for improved security and sensing.

Findings

Achieves a better trade-off between semantic secrecy rate and localization accuracy.

Improves sensing accuracy by 5 dB over traditional MRT methods.

Demonstrates the effectiveness of the proposed joint optimization approach.

Abstract

In this paper, we propose a physical layer security (PLS) framework for an intelligent reflecting surface (IRS)-assisted integrated sensing and semantic communication (ISASC) system, where a multi-antenna dual-functional semantic base station (BS) serves multiple semantic communication users (SCUs) and monitors a potentially malicious sensing target (MST) in the presence of an eavesdropper (EVE). Both MST and EVE attempt to wiretap information from the signals transmitted to the SCUs. The deployment of the IRS not only enhances PLS by directing a strong beam towards the SCUs, but also improves the localization information for the target without disclosing information about the SCUs. To further strengthen PLS, we employ joint artificial noise (AN) and dedicated sensing signal (DSS), in addition to wiretap coding. To evaluate sensing accuracy, we derive the Cramer-Rao bound (CRB) for estimating the direction of arrival (DoA), and to assess the PLS level of the ISASC system, we determine a closed-form expression for the semantic secrecy rate (SSR). To achieve an optimal trade-off between these two competing objectives, we formulate a multi-objective optimization problem (MOOP) for the joint design of the BS's beamforming (BF) vectors and the IRS's phase shift vector. To tackle this MOOP problem, the $\epsilon$-constraint method is employed, followed by an alternating optimization (AO)-based algorithm that leverages the classical successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Simulation results demonstrate that the proposed scheme outperforms the baseline schemes, achieving a superior trade-off between SSR and CRB. Specifically, our proposed approach improves the sensing accuracy by 5 dB compared to the commonly adopted maximal ratio transmission (MRT) approach.