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Energy-Efficient Probabilistic Semantic Communication Over Visible Light Networks With Rate Splitting

Zhouxiang Zhao, Zhaohui Yang, Mingzhe Chen, Chen Zhu, Xin Tong, Zhaoyang Zhang

TL;DR

The paper addresses energy-efficient optimization for a VLC-based probabilistic semantic communication (PSCom) system that uses rate splitting (RSMA) to simultaneously deliver knowledge updates and semantic data. It introduces a joint optimization framework over beamforming, DC bias, common rate allocation, and semantic compression ratios, while accounting for computational overhead via a probabilistic-graph knowledge base and AoI constraints. An alternating optimization algorithm is developed, combining successive convex approximation (SCA) for beamforming and Dinkelbach/DCA techniques for the semantic-compression/DC-bias subproblem, ensuring convergence and tractable computation. Simulation results with a 4-LED, 2-user indoor scenario substantiate that PSCom-RSMA outperforms SDMA, NOMA, and non-semantic RSMA in energy efficiency under realistic VLC constraints. The work demonstrates the practical viability of joint communication-and-computation optimization for energy-constrained semantic communications in VLC networks.

Abstract

Visible light communication (VLC) is emerging as a key technology for future wireless communication systems due to its unique physical-layer advantages over traditional radio-frequency (RF)-based systems. However, its integration with higher-layer techniques, such as semantic communication, remains underexplored. This paper investigates the energy efficiency maximization problem in a resource-constrained VLC-based probabilistic semantic communication (PSCom) system. In the considered model, light-emitting diode (LED) transmitters perform semantic compression to reduce data size, which incurs additional computation overhead. The compressed semantic information is transmitted to the users for semantic inference using a shared knowledge base that requires periodic updates to ensure synchronization. In the PSCom system, the knowledge base is represented by probabilistic graphs. To enable simultaneous transmission of both knowledge and information data, rate splitting multiple access (RSMA) is employed. The optimization problem focuses on maximizing energy efficiency by jointly optimizing transmit beamforming, direct current (DC) bias, common rate allocation, and semantic compression ratio, while accounting for both communication and computation costs. To solve this problem, an alternating optimization algorithm based on successive convex approximation (SCA) and Dinkelbach method is developed. Simulation results demonstrate the effectiveness of the proposed approach.

Energy-Efficient Probabilistic Semantic Communication Over Visible Light Networks With Rate Splitting

TL;DR

The paper addresses energy-efficient optimization for a VLC-based probabilistic semantic communication (PSCom) system that uses rate splitting (RSMA) to simultaneously deliver knowledge updates and semantic data. It introduces a joint optimization framework over beamforming, DC bias, common rate allocation, and semantic compression ratios, while accounting for computational overhead via a probabilistic-graph knowledge base and AoI constraints. An alternating optimization algorithm is developed, combining successive convex approximation (SCA) for beamforming and Dinkelbach/DCA techniques for the semantic-compression/DC-bias subproblem, ensuring convergence and tractable computation. Simulation results with a 4-LED, 2-user indoor scenario substantiate that PSCom-RSMA outperforms SDMA, NOMA, and non-semantic RSMA in energy efficiency under realistic VLC constraints. The work demonstrates the practical viability of joint communication-and-computation optimization for energy-constrained semantic communications in VLC networks.

Abstract

Visible light communication (VLC) is emerging as a key technology for future wireless communication systems due to its unique physical-layer advantages over traditional radio-frequency (RF)-based systems. However, its integration with higher-layer techniques, such as semantic communication, remains underexplored. This paper investigates the energy efficiency maximization problem in a resource-constrained VLC-based probabilistic semantic communication (PSCom) system. In the considered model, light-emitting diode (LED) transmitters perform semantic compression to reduce data size, which incurs additional computation overhead. The compressed semantic information is transmitted to the users for semantic inference using a shared knowledge base that requires periodic updates to ensure synchronization. In the PSCom system, the knowledge base is represented by probabilistic graphs. To enable simultaneous transmission of both knowledge and information data, rate splitting multiple access (RSMA) is employed. The optimization problem focuses on maximizing energy efficiency by jointly optimizing transmit beamforming, direct current (DC) bias, common rate allocation, and semantic compression ratio, while accounting for both communication and computation costs. To solve this problem, an alternating optimization algorithm based on successive convex approximation (SCA) and Dinkelbach method is developed. Simulation results demonstrate the effectiveness of the proposed approach.
Paper Structure (24 sections, 1 theorem, 41 equations, 12 figures, 3 tables, 3 algorithms)

This paper contains 24 sections, 1 theorem, 41 equations, 12 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. VLC
  4. Semantic Communication
  5. VLC and Semantic Communication
  6. Motivation and Contributions
  7. System Model and Problem Formulation
  8. PSCom Model
  9. Knowledge Update
  10. Computational Overhead
  11. VLC Channel Model
  12. RSMA for VLC-Based PSCom
  13. Power Consumption Model
  14. Computation Power
  15. Communication Power
  16. ...and 9 more sections

Key Result

Theorem 1

The optimal DC bias in problem eq.scradcbd is

Figures (12)

  • Figure 1: An indoor VLC-based PSCom network with multiple LEDs and users.
  • Figure 2: An example of the semantic compression and semantic recovery procedures with the aid of knowledge characterized by probabilistic graph.
  • Figure 3: An illustration of the segmented linear relationship between semantic compression ratio and computation overhead in the PSCom system.
  • Figure 4: An illustration of the Lambertian radiation model.
  • Figure 5: The mechanism of the RSUM scheme.
  • ...and 7 more figures

Theorems & Definitions (2)

  • Theorem 1
  • Proof