Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism
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 ...
| Authors | Haowen Wan, Qianqian Yang |
|---|---|
| Primary category | cs.LG |
| Published | 3 April 2026 |
| Last updated at source | 6 October 2026 |
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