[2604.02691] Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism

Computer Science > Machine Learning arXiv:2604.02691 (cs) [Submitted on 3 Apr 2026] Title:Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism Authors:Haowen Wan, Qianqian Yang View a PDF of the paper titled Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism, by Haowen Wan and 1 other authors View PDF HTML (experimental) Abstract: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 ...