Group-wise Semantic Splitting Multiple Access for Multi-User Semantic Communication

Abstract: In this letter, we propose a group-wise semantic splitting multiple access framework for multi-user semantic communication in downlink scenarios. The framework begins by applying a balanced clustering mechanism that groups users based on the similarity of their semantic characteristics, enabling the extraction of group-level common features and user-specific private features. The base station then transmits the common features via multicast and the private features via unicast, effectively leveraging both shared and user-dependent semantic information. To further enhance semantic separability and reconstruction fidelity, we design a composite loss function that integrates a reconstruction loss with a repulsion loss, improving both the accuracy of semantic recovery and the distinctiveness of common embeddings in the latent space. Simulation results demonstrate that the proposed method achieves up to 3.26% performance improvement over conventional schemes across various channel conditions, validating its robustness and semantic efficiency for next-generation wireless networks.