Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism
This work addresses adaptability issues in wireless image transmission for communication systems, representing an incremental advance over prior adaptive methods.
The paper tackles the lack of robustness in deep learning-based semantic communication for wireless image transmission by proposing a multi-stage system with a dynamic expert gating mechanism that jointly considers channel state and image content, resulting in significant improvement in reconstruction quality while maintaining transmission efficiency.
Deep learning based semantic communication has achieved significant progress in wireless image transmission, but most existing schemes rely on fixed models and thus lack robustness to diverse image contents and dynamic channel conditions. To improve adaptability, recent studies have developed adaptive semantic communication strategies that adjust transmission or model behavior according to either source content or channel state. More recently, MoE-based semantic communication has emerged as a sparse and efficient adaptive architecture, although existing designs still mainly rely on single-driven routing. To address this limitation, we propose a novel multi-stage end-to-end image semantic communication system for multi-input multi-output (MIMO) channels, built upon an adaptive MoE Swin Transformer block. Specifically, we introduce a dynamic expert gating mechanism that jointly evaluates both real-time CSI and the semantic content of input image patches to compute adaptive routing probabilities. By selectively activating only a specialized subset of experts based on this joint condition, our approach breaks the rigid coupling of traditional adaptive methods and overcomes the bottlenecks of single-driven routing. Simulation results indicate a significant improvement in reconstruction quality over existing methods while maintaining the transmission efficiency.