Exploiting Multi-User Semantic Communications: A Non-Orthogonal Approach
Ruikang Zhong, Xidong Mu, Yue Chen, Yuanwei Liu · 2024
A novel non-orthogonal semantic communication (NSC) framework is proposed for facilitating high-efficiency multi-user semantic communications. The NSC technique enables non-orthogonal semantic streams among users by sharing the same resource block. A semantic superposition coding (SSC) and a semantic interference tolerated (SIT) decoding paradigm are proposed for the NSC transmitters and receivers, respectively. SSC encoders are a type of joint source-channel encoder enabled by deep learning (DL), which aims to superpose the semantic information for different users to a piece of semantic feature sequence. An SSC encoder at the access point (AP) is paired with several SIT decoders at different user equipment. By jointly training the SSC encoder and all SIT decoders, the SIT decoders can identify the desired semantic information for each user, and the semantic interferences introduced by SSC are mitigated. Simulation results reveal that the proposed NSC scheme considerably improves transmission efficiency. Meanwhile, at high compression ratios, the NSC scheme outperforms conventional orthogonal semantic communications in terms of accuracy gains.