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arXiv · 2509.09499

Mixture of Semantics Transmission for Generative AI-Enabled Semantic Communication Systems

Junjie Ni·Tong Wu·Zhiyong Chen·Yin Xu·Meixia Tao·Wenjun Zhang
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Abstract

In this paper, we propose a mixture of semantics (MoS) transmission strategy for wireless semantic communication systems based on generative artificial intelligence (AI). At the transmitter, we divide an image into regions of interest (ROI) and reigons of non-interest (RONI) to extract their semantic information respectively. Semantic information of ROI can be allocated more bandwidth, while RONI can be represented in a compact form for transmission. At the receiver, a diffusion model reconstructs the full image using the received semantic information of ROI and RONI. Compared to existing generative AI-based methods, MoS enables more efficient use of channel resources by balancing visual fidelity and semantic relevance. Experimental results demonstrate that appropriate ROI-RONI allocation is critical. The MoS achieves notable performance gains in peak signal-to-noise ratio (PSNR) of ROI and CLIP score of RONI.

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Junjie Ni, Tong Wu, Zhiyong Chen, Yin Xu, Meixia Tao, Wenjun Zhang. 2025-09-11. Mixture of Semantics Transmission for Generative AI-Enabled Semantic Communication Systems. https://arxiv.org/abs/2509.09499

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