Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation

TL;DR

This paper enhances multi-user semantic communication by integrating transfer learning and knowledge distillation, enabling efficient downlink transmission to users with diverse computing resources, validated through extensive simulations.

Contribution

It introduces a novel training approach combining transfer learning and knowledge distillation tailored for multi-user semantic communication with heterogeneous user capabilities.

Findings

Improved decoding performance for low-resource users.

Effective reduction in transmission length and noise mitigation.

Validated through extensive simulation results.

Abstract

Semantic communication, notable for ensuring quality of service by jointly optimizing source and channel coding, effectively extracts data semantics, reduces transmission length, and mitigates channel noise. However, most studies overlook multi-user scenarios and resource availability, limiting real-world application. This paper addresses this gap by focusing on downlink communication from a base station to multiple users with varying computing capacities. Users employ variants of Swin transformer models for source decoding and a simple architecture for channel decoding. We propose a novel training regimen, incorporating transfer learning and knowledge distillation to improve low-computing users' performance. Extensive simulations validate the proposed methods.