Optimizing Spectral Efficiency through Bandwidth Management in Semantic Communication Systems

Semantic communication (SemCom) is a novel paradigm that exploits sophisticated deep learning tools to distil semantic features from source data at transmitter and recover the meaning of the source data based on these semantic features at the receiver. To train proficient semantic coding models with minimum semantic ambiguity, it is of paramount importance to sense abundant data for both model training and background knowledge construction. However, if interminable sensing is performed for getting highly capable SemCom coding models, a large amount of bandwidth resources should be occupied, which may in turn reduce semantic spectrum efficiency (SSE). In this paper, we investigate how to balance data transmission and data sensing in SemCom. Specifically, we first formulate an optimization problem to jointly allocate bandwidth for sensing and data transmission with the objective of maximizing SSE, while subject to resource budgets and service quality requirements. To solve this problem, we introduce the projected gradient method considering the interdependent nature of the variables in system. This method considers not only the direction of the gradient, but also the step of projecting the solution back to the feasible space, thus to efficiently find the optimal solution to the problem. Numerical simulations demonstrate the superiority of our proposed algorithm in terms of SSE, compared with several existing baselines.