DiffMoECom: Diffusion Mixture of Experts for Channel-Adaptive Semantic Image Communications

This letter presents a novel generative semantic communications framework for wireless image transmission, designed to excel in dynamic channel conditions and at low signal-to-noise ratios (SNRs). The proposed framework, dubbed DiffMoECom (Diffusion Mixture-of-Experts Communications), leverages a Mixture-of-Experts (MoE) architecture integrated with prompt learning to dynamically select optimal channel adaptation strategies by activating the most relevant prompt experts. A Joint Source and Channel Coding (JSCC) backbone is employed to transmit the low-frequency components of the source image. Furthermore, we introduce a range-space inversion mechanism, which projects the coarse JSCC output into a noisy latent space. This facilitates faithful semantic reconstruction of high-frequency details through conditional diffusion-based null space compensation. Extensive experiments across various fading channels and SNR conditions demonstrate that our method consistently surpasses state-of-the-art DeepJSCC and generative approaches, achieving superior performance in distortion metrics with competitive inference efficiency.

• Publication year

  2026

• Venue

  IEEE Wireless Communications Letters

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/LWC.2025.3633574
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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