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

Xudong Li, Rugui Yao, Theodoros A. Tsiftsis, Alexandros-Apostolos A. Boulogeorgos

TL;DR

This work tackles secure and energy-efficient integrated sensing and communications in cognitive radio networks by integrating RSMA-based multicast communications with ISAC signaling to create green interference. The authors formulate a non-convex SEE objective that couples the echo beamformer, BS1 transmit beams, and BS2 RSMA beams under power, rate, and security constraints, and they develop an alternating optimization framework using Taylor series expansion, majorization-minimization, semi-definite programming, and successive convex approximation to decompose the problem into three tractable subproblems. The echo-beamformer optimization yields a principal eigenvector solution, while the BS1 and BS2 beamformers are obtained via convexifications and LMIs/SOC representations, enabling iterative convergence to a (local) optimum. Numerical results show that RSMA with green interference consistently improves sensing SCNR, legitimate secrecy rate, and security energy efficiency compared with SDMA, NOMA, and OMA baselines, validating the proposed approach for secure and green ISAC-CRNs in interference- and eavesdropping-prone environments.

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.

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

TL;DR

This work tackles secure and energy-efficient integrated sensing and communications in cognitive radio networks by integrating RSMA-based multicast communications with ISAC signaling to create green interference. The authors formulate a non-convex SEE objective that couples the echo beamformer, BS1 transmit beams, and BS2 RSMA beams under power, rate, and security constraints, and they develop an alternating optimization framework using Taylor series expansion, majorization-minimization, semi-definite programming, and successive convex approximation to decompose the problem into three tractable subproblems. The echo-beamformer optimization yields a principal eigenvector solution, while the BS1 and BS2 beamformers are obtained via convexifications and LMIs/SOC representations, enabling iterative convergence to a (local) optimum. Numerical results show that RSMA with green interference consistently improves sensing SCNR, legitimate secrecy rate, and security energy efficiency compared with SDMA, NOMA, and OMA baselines, validating the proposed approach for secure and green ISAC-CRNs in interference- and eavesdropping-prone environments.

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.

Paper Structure

This paper contains 18 sections, 5 theorems, 72 equations, 12 figures, 4 algorithms.

Table of Contents

  1. Introduction
  2. System, Channel, and Signal Models
  3. System Model
  4. Channel Model
  5. Communication Signal Model
  6. Sensing Signal Model
  7. Optimization Formulation and Solution
  8. Optimization of the Echo Signal BF Vector at $\rm BS_1$
  9. Optimization of the Transmit BF Vectors at $\rm BS_1$
  10. Optimization of the Transmit BF Vector at $\rm BS_2$
  11. Complexity analysis of Algorithm \ref{['algorithm_4']}
  12. Numerical Results and Discussions
  13. Conclusion
  14. Proof of Proposition 1
  15. Proof of Proposition 2
  16. ...and 3 more sections

Key Result

Proposition 1

The constraint on the security rate in (P1d) can be converted to respectively, where $\tau = {2^{{I_{\rm{S}}}}}$.

Figures (12)

  • Figure 1: PR-ISAC CRNs architecture.
  • Figure 2: SEE v.s. iteration number for different $M_1$.
  • Figure 3: $\gamma_{\rm b1}$ v.s. $P_{\rm 1, max}$ for different optimization schemes.
  • Figure 4: $R_{\rm S}$ v.s. $P_{\rm 2, max}$ for different multiple access schemes.
  • Figure 5: $P_{\rm 2, max}$ v.s. $R_{\rm S}$ for different multiple access schemes.
  • ...and 7 more figures

Theorems & Definitions (10)

  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Proposition 3
  • proof
  • Proposition 4
  • proof
  • Proposition 5
  • proof