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Adaptive Sensing Performance Design for Enhancing Secure Communication in Networked ISAC Systems

Yiming Xu, Dongfang Xu, Shenghui Song, Dusit Niyato

TL;DR

The paper tackles secure communication in a networked ISAC system by estimating the eavesdropper's CSI through sensing and by optimizing sensing performance adaptively within the information leakage rate. It presents both centralized (BCD-based) and decentralized (consensus ADMM-based) optimization frameworks that jointly design beamforming and radar covariance under rate and secrecy constraints, leveraging CRB-based sensing performance metrics. A key finding is that an optimal, intermediate sensing level maximizes system performance, and excessive sensing or fixed sensing requirements can degrade efficiency; adaptive sensing balances sensing and transmission to exploit synergy and reduce power. The work demonstrates practical viability of implicit sensing-performance design in PLS-enabled ISAC networks and shows substantial gains in power efficiency and security with reduced coordination overhead in the decentralized approach.

Abstract

The channel state information (CSI) of an eavesdropper is crucial for physical layer security (PLS) design, but it is difficult to obtain due to the passive and non-cooperative nature of the eavesdropper. To this end, integrated sensing and communication (ISAC) offers a novel solution by estimating the CSI of the eavesdropper based on sensing information. However, existing studies normally impose explicit and fixed sensing performance requirement without considering the varying communication conditions, which hinders the system from fully exploiting the synergy between sensing and communication. To address this issue, this paper proposes sensing-enhanced secure communication with adaptive sensing performance. Specifically, we formulate the sensing performance implicitly in the information leakage rate and adaptively optimize it for the minimization of the power consumption, offering enhanced flexibility and adaptability in sensing performance. We consider both centralized and decentralized designs to thoroughly investigate the impact of network structure on system performance and complexity. Specifically, we devise a block coordinate descent (BCD)-based method for centralized design. For decentralized design, we develop an optimization framework based on consensus alternating direction method of multipliers (ADMM) to reduce complexity and information exchange overhead. Experimental results demonstrate the advantage of the proposed implicit sensing performance requirement design due to its capability to adaptively adjust the sensing performance to enhance the system performance for varying system configurations.

Adaptive Sensing Performance Design for Enhancing Secure Communication in Networked ISAC Systems

TL;DR

The paper tackles secure communication in a networked ISAC system by estimating the eavesdropper's CSI through sensing and by optimizing sensing performance adaptively within the information leakage rate. It presents both centralized (BCD-based) and decentralized (consensus ADMM-based) optimization frameworks that jointly design beamforming and radar covariance under rate and secrecy constraints, leveraging CRB-based sensing performance metrics. A key finding is that an optimal, intermediate sensing level maximizes system performance, and excessive sensing or fixed sensing requirements can degrade efficiency; adaptive sensing balances sensing and transmission to exploit synergy and reduce power. The work demonstrates practical viability of implicit sensing-performance design in PLS-enabled ISAC networks and shows substantial gains in power efficiency and security with reduced coordination overhead in the decentralized approach.

Abstract

The channel state information (CSI) of an eavesdropper is crucial for physical layer security (PLS) design, but it is difficult to obtain due to the passive and non-cooperative nature of the eavesdropper. To this end, integrated sensing and communication (ISAC) offers a novel solution by estimating the CSI of the eavesdropper based on sensing information. However, existing studies normally impose explicit and fixed sensing performance requirement without considering the varying communication conditions, which hinders the system from fully exploiting the synergy between sensing and communication. To address this issue, this paper proposes sensing-enhanced secure communication with adaptive sensing performance. Specifically, we formulate the sensing performance implicitly in the information leakage rate and adaptively optimize it for the minimization of the power consumption, offering enhanced flexibility and adaptability in sensing performance. We consider both centralized and decentralized designs to thoroughly investigate the impact of network structure on system performance and complexity. Specifically, we devise a block coordinate descent (BCD)-based method for centralized design. For decentralized design, we develop an optimization framework based on consensus alternating direction method of multipliers (ADMM) to reduce complexity and information exchange overhead. Experimental results demonstrate the advantage of the proposed implicit sensing performance requirement design due to its capability to adaptively adjust the sensing performance to enhance the system performance for varying system configurations.
Paper Structure (26 sections, 1 theorem, 115 equations, 7 figures, 2 algorithms)

This paper contains 26 sections, 1 theorem, 115 equations, 7 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Two-stage protocol
  4. Centralized and Decentralized Design
  5. Signal Model
  6. Received Signals at Users
  7. Received Signals at the Eavesdropper
  8. CSI Uncertainty with Sensing Estimation Errors
  9. Problem Formulation
  10. Centralized Design
  11. Centralized Sensing
  12. Centralized Optimization for Solving Problem \ref{['main_prob']}
  13. Decentralized Design
  14. Decentralized Sensing
  15. Decentralized Optimization for Solving Problem \ref{['main_prob']}
  16. ...and 11 more sections

Key Result

Lemma 1

(S-Procedure Lemma boyd2004convex) Define two functions $f_i(\mathbf{t}): \mathbb{C}^{N\times 1}\to \mathbb{R}$, $i\in \left \{ 1,2 \right \}$ as where $\mathbf{A}_i\in \mathbb{H}^N$, $\mathbf{b}_i\in \mathbb{C}^{N\times 1}$, and $\mathrm{c}_i\in \mathbb{R}$. Then, the implication $f_1(\mathbf{t})\leq0 \Rightarrow f_2(\mathbf{t})\leq0$ holds if and only if there exists a variable $\kappa \geq 0$

Figures (7)

  • Figure 1: An overview of the sensing-enhanced secure communication in a networked ISAC system. Part A illustrates the architecture of the considered system, where multiple BSs perform collaborative sensing to localize the eavesdropper and serve the associated users. The BSs are connected to a central controller for information exchange and coordination. Part B presents the topological structure of the centralized design. The central controller collects the CSI and sensing echoes from all BSs and performs the centralized sensing and optimization. Part C presents the topological structure of the decentralized design. BSs perform decentralized sensing and optimization in parallel, with information shared with other BSs via the central controller.
  • Figure 2: An illustration of the two-stage protocol of the frame. Each frame is divided into two stages, each of which consists of multiple time slots. In the first stage, the BSs simultaneously conduct sensing and communication. In the second stage, the secure information transmission is carried out, utilizing the refined CSI obtained from the sensing results in the first stage.
  • Figure 3: Convergence behavior of Algorithm 1 (Left) and Algorithm 2 (Right).
  • Figure 4: Total power consumption (dBm) versus achievable rate requirement $R_{\text{info},m,k}$ (bits/s/Hz).
  • Figure 5: Total power consumption (dBm) versus available transmit power at each BS $P_{\text{max}}$ (dBm).
  • ...and 2 more figures

Theorems & Definitions (7)

  • Remark 1
  • Remark 2
  • Remark 3
  • Lemma 1
  • Remark 4
  • Remark 5
  • Remark 6