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.

翻译：可见光通信（VLC）因其相较于传统射频（RF）系统在物理层的独特优势，正成为未来无线通信系统的关键技术。然而，其与语义通信等更高层技术的融合仍待深入探索。本文研究了资源受限的、基于VLC的概率语义通信（PSCom）系统中的能量效率最大化问题。在所考虑的模型中，发光二极管（LED）发射器执行语义压缩以减少数据量，这会产生额外的计算开销。压缩后的语义信息被传输给用户，用户利用一个需要定期更新以确保同步的共享知识库进行语义推理。在PSCom系统中，知识库由概率图表示。为了实现知识和信息数据的同步传输，采用了速率分割多址接入（RSMA）技术。该优化问题聚焦于通过联合优化发射波束成形、直流（DC）偏置、公共速率分配和语义压缩比，同时考虑通信和计算成本，以最大化能量效率。为了解决此问题，本文开发了一种基于逐次凸逼近（SCA）和Dinkelbach方法的交替优化算法。仿真结果验证了所提方法的有效性。