Kavli Affiliate: Bo Gu
| First 5 Authors: Haidong Wang, Songhan Zhao, Lanhua Li, Bo Gu, Jing Xu
| Summary:
In this paper, we investigate a joint source-channel encoding (JSCE) scheme
in an intelligent reflecting surface (IRS)-assisted multi-user semantic
communication system. Semantic encoding not only compresses redundant
information, but also enhances information orthogonality in a semantic feature
space. Meanwhile, the IRS can adjust the spatial orthogonality, enabling
concurrent multi-user semantic communication in densely deployed wireless
networks to improve spectrum efficiency. We aim to maximize the users’ semantic
throughput by jointly optimizing the users’ scheduling, the IRS’s passive
beamforming, and the semantic encoding strategies. To tackle this non-convex
problem, we propose an explainable deep neural network-driven deep
reinforcement learning (XD-DRL) framework. Specifically, we employ a deep
neural network (DNN) to serve as a joint source-channel semantic encoder,
enabling transmitters to extract semantic features from raw images. By
leveraging structural similarity, we assign some DNN weight coefficients as the
IRS’s phase shifts, allowing simultaneous optimization of IRS’s passive
beamforming and DNN training. Given the IRS’s passive beamforming and semantic
encoding strategies, user scheduling is optimized using the DRL method.
Numerical results validate that our JSCE scheme achieves superior semantic
throughput compared to the conventional schemes and efficiently reduces the
semantic encoder’s mode size in multi-user scenarios.
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