STAR-RIS Assisted Information Transmission Based on Fairness in Semantic Communication Systems

Semantic communication (SC) is one of the promising solutions for future wireless communications due to its superior transmission efficiency. However, the semantic information cannot be transmitted accurately under noisy channels by the existing methods. For this reason, we propose a fairness-based transmission strategy for STAR-RIS assisted SC systems. On this basis, we investigate two operating protocols of STAR-RIS, energy splitting (ES) and mode switching (MS). More specifically, we maximize the minimum signal-to-noise ratio (SNR) of the users by jointly optimizing the active beamforming and the passive beamforming under the constraint of limited power at the base station (BS). To tackle this max-min optimization problem, for ES, we develop a double-loop iterative algorithm by using the successive convex approximation (SCA) and penalty function methods. For MS protocol, we further add an additional penalty in the objective function to address the optimization problem. Moreover, we rigorously prove that the proposed algorithm can converge to a locally optimal solution. At last, we conduct various experiments to verify the performance of the proposed algorithms. Simulation experiments demonstrate that our algorithm outperforms other benchmark methods in fairness and semantic similarity.