Rate-Splitting Multiple Access Enabled Green Probabilistic Semantic Communication over Wireless Networks

In this paper, we propose a multi-user green probabilistic semantic communication (PSC) system for semantic communication (SemCom) facilitated by probabilistic knowledge graphs (PKGs), which are knowledge graphs (KGs) integrated with probability to represent semantic information. In the considered model, semantic information of different users is represented by PKGs. On this basis, we design a semantic compression model for multi-user downlink task-oriented SemCom, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Utilizing rate-splitting multiple access (RSMA) technology, the transmitted messages are splitted into shared and private ones. Considering the limited wireless resources and constraint in semantic communication, an optimization problem with the goal of minimizing system energy consumption comprehensively considering the computation and communication process is formulated. In order to address the optimization problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design with successive convex approximation (SCA) method, semantic compression ratio optimization with variable substitution method, and computation capacity allocation with obtaining closed-form optimal solution. Simulation results verify the effectiveness of the proposed algorithm.