A Lightweight Swin Transformer-Based Resource-Adaptive Semantic Compression Strategy

In multi-terminal downlink semantic communication scenarios, employing a uniform semantic compression ratio caused difficulty in semantic decoding for low-computing-power terminals, as well as underutilized computing power resources and insufficiently refined semantic data for high-computing-power terminals. To address this issue under constraints of limited terminal computing power and link bandwidth, this paper proposed a lightweight Swin Transformer-based resource-adaptive semantic compression strategy. The semantic encoder integrated a gating network and a sparse attention mechanism to customize differentiated semantic compression ratios for individual terminals. A joint computing and bandwidth allocation model was formulated to minimize total system energy consumption. The proximal policy optimization (PPO) algorithm was employed to solve the optimization problem. Simulation results demonstrate that, compared with fixed compression ratio schemes, the proposed strategy reduces total system energy consumption by 39%.