A Transformer-Based End-to-End Receiver Design for Wi-Fi 7 Physical Layer

doi:10.12142/ZTECOM.202504005

ZTE Communications ›› 2025, Vol. 23 ›› Issue (4): 27-36.DOI: 10.12142/ZTECOM.202504005

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A Transformer-Based End-to-End Receiver Design for Wi-Fi 7 Physical Layer

LIU Yichen, GAO Ruixin, ZENG Chen, LIU Yingzhuang()

School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China

Received:2025-09-16 Online:2025-12-22 Published:2025-12-22
About author:LIU Yichen is currently pursuing the PhD degree in the School of Electronic Information and Communications, Huazhong University of Science and Technology, China. His research interests include wireless communications, artificial intelligence, and WLAN systems.
GAO Ruixin is currently pursuing the MS degree in the School of Electronic Information and Communications, Huazhong University of Science and Technology, China. Her research interests include machine learning for wireless communications, artificial intelligence, and WLAN systems.
ZENG Chen is currently pursuing the PhD degree in the School of Electronic Information and Communications, Huazhong University of Science and Technology, China. His research interests include machine learning for wireless communications, artificial intelligence, and WLAN systems.
LIU Yingzhuang (liuyz@hust.edu.cn) is currently a professor with the School of Electronic Information and Communications, Huazhong University of Science and Technology, China. Prior to that, he was a postdoctoral researcher with University of Paris XI, France from 2000 to 2001. Since 2003, he has led more than 10 national key projects, published more than 100 papers, and obtained more than 50 patents in broadband wireless communications. His main research interests are in broadband wireless communications, including LTE-Advanced, 5G/6G, and WLAN systems.
Supported by:
the Huawei Technologies Co., Ltd(TP20250612004);the Huawei Technologies Co., Ltd(TP20250612004)

Abstract

Abstract:

The increasing demand for high throughput and low latency in Wi-Fi 7 necessitates a robust receiver design. Traditional receiver architectures, which rely on a cascade of complex, independent signal processing modules, often face performance bottlenecks. Rather than focusing on semantic-level tasks or simplified Additive White Gaussian Noise (AWGN) channels, this paper investigates a bit-level end-to-end receiver for a practical Wi-Fi 7 Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) physical layer. A lightweight Transformer-based encoder-only architecture is proposed to directly map synchronized OFDM signals to decoded bitstreams, replacing the conventional channel estimation, equalization, and data detection. By leveraging the multi-head self-attention mechanism of the Transformer encoder, our model effectively captures long-range spatial–temporal dependencies across antennas and subcarriers, thus learning to compensate for channel distortions without explicit channel state information. This mechanism eliminates the need for explicit channel estimation, enabling the direct extraction of crucial channel and signal features. Experimental results validate the efficacy of the proposed design, demonstrating the significant potential of deep learning for future wireless receiver architectures.

Key words: Transformer, receiver design, Wi-Fi 7, deep learning

LIU Yichen, GAO Ruixin, ZENG Chen, LIU Yingzhuang. A Transformer-Based End-to-End Receiver Design for Wi-Fi 7 Physical Layer[J]. ZTE Communications, 2025, 23(4): 27-36.

Figures/Tables 6

Figure 1 A traditional modular design of PHY transmission

Figure 2 Operation flowchart of the receiver

Figure 3 Lightweight encoder framework of the proposed Transformer-based receiver

Figure 4 BER performance of the traditional scheme

Figure 5 Training and validation curves: (a) loss versus epochs;(b) accuracy versus epochs

Figure 6 BER performance of the proposed scheme

References 18

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A Transformer-Based End-to-End Receiver Design for Wi-Fi 7 Physical Layer

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