Secure and Green Rate-Splitting Multiple Access Integrated Sensing and Communications

2025-02-19

AI Summary

In cognitive radio-enabled integrated sensing and communication (ISAC) systems, dual threats from interference and eavesdropping degrade communication quality, sensing accuracy, and security. To address this, this paper proposes a multi-beam joint optimization framework based on rate-splitting multiple access (RSMA). Innovatively, echo signals are leveraged to construct “green interference,” simultaneously suppressing eavesdropping and improving energy efficiency. This work is the first to deeply integrate the RSMA common/private stream splitting mechanism with four-dimensional co-optimization—sensing, communication, security, and energy efficiency. The non-convex problem is tackled via a combination of Taylor series expansion, majorization-minimization (MM), semidefinite programming (SDP), and successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme significantly enhances secure energy efficiency (SEE) and outperforms existing approaches across all key metrics: secure rate, target sensing accuracy, and communication quality.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Applications of cryptography
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

In this paper, we investigate the sensing, communication, security, and energy efficiency of integrated sensing and communication (ISAC)-enabled cognitive radio networks (CRNs) in a challenging scenario where communication quality, security, and sensing accuracy are affected by interference and eavesdropping. Specifically, we analyze the communication and sensing signals of ISAC as well as the communication signal consisting of common and private streams, based on rate-splitting multiple access (RSMA) of multicast network. Then, the sensing signal-tocluster-plus-noise ratio, the security rate, the communication rate, and the security energy efficiency (SEE) are derived, respectively. To simultaneously enhance the aforementioned performance metrics, we formulate a targeted optimization framework that aims to maximizing SEE by jointly optimizing the transmit signal beamforming (BF) vectors and the echo signal BF vector to construct green interference using the echo signal, as well as common and private streams split by RSMA to refine security rate and suppress power consumption, i.e., achieving 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 superiority of the proposed joint multi-BF optimization scheme based on RSMA and constructed green interference in improving system performances.

Problem

• Enhance security and energy efficiency in ISAC-enabled CRNs.
• Optimize signal beamforming for green interference and security.
• Solve non-convex optimization via decomposition and iterative strategies.

Innovation

• RSMA for secure communication
• Green interference with echo signals
• Alternating optimization strategy used