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IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation

Lingyi Wang, Wei Wu, Fuhui Zhou, Zhijin Qin, Qihui Wu

TL;DR

In this paper, intelligent reflective surface (IRS)-enhanced secure semantic communication (IRS-SSC) is proposed to guarantee the physical layer security from a task-oriented semantic perspective and a novel semantic context awared state space (SCA-SS) is proposed to solve the dimensional catastrophe problem.

Abstract

Learning-task oriented semantic communication is pivotal in optimizing transmission efficiency by extracting and conveying essential semantics tailored to specific tasks, such as image reconstruction and classification. Nevertheless, the challenge of eavesdropping poses a formidable threat to semantic privacy due to the open nature of wireless communications. In this paper, intelligent reflective surface (IRS)-enhanced secure semantic communication (IRS-SSC) is proposed to guarantee the physical layer security from a task-oriented semantic perspective. Specifically, a multi-layer codebook is exploited to discretize continuous semantic features and describe semantics with different numbers of bits, thereby meeting the need for hierarchical semantic representation and further enhancing the transmission efficiency. Novel semantic security metrics, i.e., secure semantic rate (S-SR) and secure semantic spectrum efficiency (S-SSE), are defined to map the task-oriented security requirements at the application layer into the physical layer. To achieve artificial intelligence (AI)-native secure communication, we propose a noise disturbance enhanced hybrid deep reinforcement learning (NdeHDRL)-based resource allocation scheme. This scheme dynamically maximizes the S-SSE by jointly optimizing the bits for semantic representations, reflective coefficients of the IRS, and the subchannel assignment. Moreover, we propose a novel semantic context awared state space (SCA-SS) to fusion the high-dimensional semantic space and the observable system state space, which enables the agent to perceive semantic context and solves the dimensional catastrophe problem. Simulation results demonstrate the efficiency of our proposed schemes in both enhancing the security performance and the S-SSE compared to several benchmark schemes.

IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation

TL;DR

In this paper, intelligent reflective surface (IRS)-enhanced secure semantic communication (IRS-SSC) is proposed to guarantee the physical layer security from a task-oriented semantic perspective and a novel semantic context awared state space (SCA-SS) is proposed to solve the dimensional catastrophe problem.

Abstract

Learning-task oriented semantic communication is pivotal in optimizing transmission efficiency by extracting and conveying essential semantics tailored to specific tasks, such as image reconstruction and classification. Nevertheless, the challenge of eavesdropping poses a formidable threat to semantic privacy due to the open nature of wireless communications. In this paper, intelligent reflective surface (IRS)-enhanced secure semantic communication (IRS-SSC) is proposed to guarantee the physical layer security from a task-oriented semantic perspective. Specifically, a multi-layer codebook is exploited to discretize continuous semantic features and describe semantics with different numbers of bits, thereby meeting the need for hierarchical semantic representation and further enhancing the transmission efficiency. Novel semantic security metrics, i.e., secure semantic rate (S-SR) and secure semantic spectrum efficiency (S-SSE), are defined to map the task-oriented security requirements at the application layer into the physical layer. To achieve artificial intelligence (AI)-native secure communication, we propose a noise disturbance enhanced hybrid deep reinforcement learning (NdeHDRL)-based resource allocation scheme. This scheme dynamically maximizes the S-SSE by jointly optimizing the bits for semantic representations, reflective coefficients of the IRS, and the subchannel assignment. Moreover, we propose a novel semantic context awared state space (SCA-SS) to fusion the high-dimensional semantic space and the observable system state space, which enables the agent to perceive semantic context and solves the dimensional catastrophe problem. Simulation results demonstrate the efficiency of our proposed schemes in both enhancing the security performance and the S-SSE compared to several benchmark schemes.

Paper Structure

This paper contains 27 sections, 22 equations, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. IRS-Enhanced Secure Semantic Communication
  3. The Secure Semantic Communication Framework
  4. IRS-Enhanced Secure Semantic Communications
  5. Semantic Performance Metric for Semantic Image Reconstruction
  6. Semantic Coding Metrics
  7. Semantic Security Performance
  8. Cross-Layer Semantic Security Metrics
  9. Resource Allocation Problem Formulation
  10. The Proposed Intelligent Semantic Resource Allocation Scheme
  11. Pre-Training Semantic Coding and Multi-Layer Codebook
  12. Semantic-Awared MDP Problem
  13. Intelligent Resource Allocation Scheme Based on NdeHDRL
  14. Factorised Gaussian Noise Based Perturbation
  15. NdeHDRL-Dueling DQN for Adaptive Bit Allocation and Subchannel Assignment
  16. ...and 12 more sections

Figures (11)

  • Figure 1: An IRS-enhanced secure semantic communication network.
  • Figure 2: Our proposed intelligent resource allocation scheme for IRS-SSC networks.
  • Figure 3: The semantic context awared state for the MDP-based semantic resource allocation problem.
  • Figure 4: Pre-Training Semantic Coding Network
  • Figure 5: The SCA-SS Construction
  • ...and 6 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2