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Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO approach

Yangbo Guo, Jianhui Fan, Ruichen Zhang, Baofang Chang, Derrick Wing Kwan Ng, Dusit Niyato, Dong In Kim

TL;DR

Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches in terms of achievable SEE and significantly improves convergence speed compared to the original PPO approach.

Abstract

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. {In these networks,} an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem. In the formulated problem, beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices are jointly optimized subject to constraints on total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. {The approach leverages} dual sample strategies and generalized advantage estimation (GAE). In addition, to further simplify the design, we adopt the maximum ratio transmission (MRT) and zero-forcing (ZF) as beamforming vectors in the action space. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches in terms of achievable SEE and significantly improves convergence speed compared to the original PPO approach. Moreover, implementing the RSMA scheme and IRS contributes to overall system performance, {achieving approximately $19.67\%$ improvement over traditional multiple access schemes and $25.74\%$ improvement over networks without IRS deployment.

Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO approach

TL;DR

Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches in terms of achievable SEE and significantly improves convergence speed compared to the original PPO approach.

Abstract

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. {In these networks,} an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem. In the formulated problem, beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices are jointly optimized subject to constraints on total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. {The approach leverages} dual sample strategies and generalized advantage estimation (GAE). In addition, to further simplify the design, we adopt the maximum ratio transmission (MRT) and zero-forcing (ZF) as beamforming vectors in the action space. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches in terms of achievable SEE and significantly improves convergence speed compared to the original PPO approach. Moreover, implementing the RSMA scheme and IRS contributes to overall system performance, {achieving approximately improvement over traditional multiple access schemes and improvement over networks without IRS deployment.

Paper Structure

This paper contains 31 sections, 41 equations, 10 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work
  4. Motivation and Contributions
  5. System Model
  6. VLC Network
  7. VLC Channel Model
  8. LoS Channels
  9. NLoS Channels
  10. IRS-Assisted Channel
  11. Problem Formulation
  12. Information Transmission
  13. Optimization Problem
  14. Proposed DRL-based Approach
  15. Optimization Problem Reformulation
  16. ...and 16 more sections

Figures (10)

  • Figure 1: Illustration of the IRS-assisted VLC network with RSMA. The IRS reflects LED signals to enhance communication with user devices (i.e., all LUs) while mitigating interference from an Eve. RSMA is employed to split and encode user messages, improving data transmission and interference management across the VLC MISO channel.
  • Figure 2: Framework of the proposed DS-PPO Approach. The IRS-assisted VLC MISO network utilizes dual sampling to generate on-policy and off-policy samples, which are processed through actor-critic networks to optimize actions via gradient descent.
  • Figure 3: Convergence behaviors of the DS-PPO approach.
  • Figure 4: Convergence performances of different learning rates.
  • Figure 5: SEE achieved with different approaches.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2