Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity

TL;DR

This paper introduces a Swin Transformer-based semantic communication system designed for multi-user environments with varying computing capacities, enabling adaptive, efficient image transmission and reconstruction.

Contribution

It proposes a multi-purpose transmitter framework that adaptively encodes semantic features considering user capacity and network conditions, improving multi-user semantic communication efficiency.

Findings

Effective semantic feature extraction and compression for diverse user capacities.

Adaptive transmission length improves image reconstruction quality and network efficiency.

Hybrid loss function enhances perceptual quality of reconstructed images.

Abstract

Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system.