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Multi-User Semantic Communications With Interference-Mitigation Learning
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DOI：10.1109/LWC.2025.3648737.png)
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Abstract
En 中文
While semantic communications has shown great potential in single-user settings, interference over a shared multiple access channel (MAC) remains a key challenge in multi-user scenarios. In this letter, we propose a novel joint source-channel coding scheme with interference-mitigation learning (JSCC-IM) for task-oriented multi-user semantic communications. The proposed JSCC-IM employs a single-layer decoder to separate desired semantic features from multi-user interference in the aggregated signals over MAC. Then, we design a loss function that explicitly suppresses multi-user interference while preserving desired semantic features. Simulation results show that the JSCC-IM improves inference accuracy over conventional JSCC schemes by more than 5% and 3% in multi-user semantic inference and multi-view semantic fusion, respectively.
Keywords:
Deep joint source-channel coding
image classification
multi-user interference
semantic communications
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5.5
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