Anchor-Aided Multi-User Semantic Communication with Adaptive Decoders
Published in IEEE Transactions on Network Science and Engineering, 2026 IF 7.3
Abstract (excerpt). Semantic communication (SemCom) with the assistance of advanced deep learning (DL) techniques is gaining momentum to catch up with the massive increase in users’ demands in quantity and quality. Specifically, SemCom can actively embed the semantic meaning of the data into the transmission process, while eliminating statistical redundancy to preserve bandwidth resources for other users. Therefore, the transmitter encodes the message in the most concise way, while the receiver tries to interpret the message with the DL model and its knowledge of the transmitter’s intended meaning. Most existing works only consider one transmitter and one receiver, which limits their ability to address the diversity in users’ models and capabilities. Therefore, in this paper, we propose a multi-user semantic communication system where each user is equipped with a distinct DL-based joint source-channel decoder architecture, reflecting the diversity in computing capacity. The challenging issue with the proposed system is the catastrophic forgetting property of neural networks …
This journal paper extends our APNOMS 2025 paper, Adaptive Semantic Communication for Multi-User Systems with Non-Uniform Deep Architectures.
Recommended citation: Loc X. Nguyen, Phuong-Nam Tran, Trung Thanh Pham, Avi Deb Raha, Eui-Nam Huh, Zhu Han, Choong Seon Hong. (2026). "Anchor-Aided Multi-User Semantic Communication with Adaptive Decoders." IEEE Transactions on Network Science and Engineering, 1-19.
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