Pilot-Free Semantic Communication Over Multi-User Mimo Fading Channels

Wireless communication systems operating in fading channels often demand pilots for channel estimation and data recovery, leading to substantial transmission overhead. In this paper, we propose a novel pilot-free semantic communication system designed for transmitting images over multi-user MIMO (MU-MIMO) fading channels. Specifically, our method involves extracting multi-scale semantic features from the source image at the transmitter, effectively embedding pilot-like information. At the receiver, we extract channel features from these semantic features at each scale, enabling the reconstruction of the source image without requiring explicit channel estimation and signal detection. To enhance the image reconstruction process, we introduce a novel module, called Resnet Transformer, which combines multi-head self-attention (MHSA) with Resnet block. Our experimental results demonstrate that this pilot-free system outperforms existing pilot-aided semantic communication methods in terms of perceptual quality and transmission efficiency.

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Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1109/icip51287.2024.10648086
Citations
2
Primary Topic
Cooperative Communication and Network Coding
Type
article
Field-Weighted Citation Impact
0.70
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article

Pilot-Free Semantic Communication Over Multi-User Mimo Fading Channels

Yiping Duan, Zhaohui Yang, Qianqian Yang, Zhaoyang Zhang et al.
2 citations
Cooperative Communication and Network Coding
0.70
article

Pilot-Free Semantic Communication Over Multi-User Mimo Fading Channels

Yiping Duan, Zhaohui Yang, Qianqian Yang, Zhaoyang Zhang, Weixuan Chen
article en
2 citations

Abstract

Wireless communication systems operating in fading channels often demand pilots for channel estimation and data recovery, leading to substantial transmission overhead. In this paper, we propose a novel pilot-free semantic communication system designed for transmitting images over multi-user MIMO (MU-MIMO) fading channels. Specifically, our method involves extracting multi-scale semantic features from the source image at the transmitter, effectively embedding pilot-like information. At the receiver, we extract channel features from these semantic features at each scale, enabling the reconstruction of the source image without requiring explicit channel estimation and signal detection. To enhance the image reconstruction process, we introduce a novel module, called Resnet Transformer, which combines multi-head self-attention (MHSA) with Resnet block. Our experimental results demonstrate that this pilot-free system outperforms existing pilot-aided semantic communication methods in terms of perceptual quality and transmission efficiency.

Zhejiang University (CN), Tsinghua University (CN)
Openalex Percentile: Top 28%
Cooperative Communication and Network Coding
0.70
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