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

TL;DR

This paper proposes an energy-efficient semantic communication framework over visible light networks using rate splitting, optimizing transmission and compression to enhance performance while managing computation costs.

Contribution

It introduces a novel VLC-based probabilistic semantic communication system with joint optimization of beamforming, bias, rate, and compression, employing an alternating optimization algorithm.

Findings

Enhanced energy efficiency through joint optimization.

Effective use of rate splitting multiple access in VLC.

Simulation confirms improved system performance.

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.