Joint Computing Resource and Bandwidth Allocation for Semantic Communication Networks

As a new communication paradigm, neural network-driven semantic communication (SemCom) has demonstrated considerable promise in enhancing resource efficiency by transmitting the semantics rather than all bits of source information. Using a large semantic coding model can accurately distil semantics, and significantly save the required bandwidth. However, this consumes a large amount of computing resources, which are also precious in the network. In this paper, we investigate the joint computing resources and bandwidth allocation for SemCom networks. We first introduce the computing latency model in SemCom, and formulate the joint computing resources and bandwidth allocation optimization problem with the objective of maximizing semantic accuracy. Then, we transform this problem into a deep reinforcement learning framework and exploit a multi-agent proximal policy optimization to solve it. Numerical results show that the proposed method significantly improves the average semantic accuracy in the resource-constrained cases, compared with the two baselines.