Multi-User Semantic Communications with Interference-Mitigation Learning

Abstract

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. © 2012 IEEE.