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

TL;DR

This paper introduces a group-wise semantic splitting multiple access framework for multi-user semantic communication, utilizing clustering and combined loss functions to improve semantic recovery and separation, resulting in significant performance gains.

Contribution

It presents a novel group-wise semantic splitting approach with clustering and a combined loss function, enhancing multi-user semantic communication performance.

Findings

Achieves up to 3.26x performance improvement over conventional schemes.

Effectively separates shared and user-specific semantic features.

Demonstrates robustness across various channel conditions.

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 3.26x performance improvement over conventional schemes across various channel conditions, validating its robustness and semantic efficiency for next-generation wireless networks.