论文标题

SIGT:基于变压器的高效端到端MIMO-OFDM接收器框架

SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer

论文作者

Ren, Ziyou, Cheng, Nan, Sun, Ruijin, Wang, Xiucheng, Lu, Ning, Xu, Wenchao

论文摘要

多输入多输出和正交频分多路复用(MIMO-OFDM)是4G和随后的无线通信系统中的关键技术。通常,MIMO-OFDM接收器由具有不同功能的多个级联块执行,并且每个块中的算法是根据理想的无线通道分布的理想假设设计的。但是,这些假设在实用的复杂无线环境中可能会失败。深度学习(DL)方法具有从复杂和巨大数据中捕获关键功能的能力。在本文中,提出了一个基于\ textit {变压器}的新型端到端MIMO-OFDM接收器框架,名为Sigt。通过将从每个天线接收到变压器的令牌接收到的信号,可以学习不同天线的空间相关性,并且可以减轻关键的零射击问题。此外,没有插入的飞行员可以很好地工作,提出的SIGT框架可以很好地工作,从而提高了有用的数据传输效率。实验结果表明,即使在低SNR环境或少量训练样本中,SIGT在信号恢复精度方面的性能也比基准方法更高。代码可在https://github.com/sigtransformer/sigt上找到。

Multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) are the key technologies in 4G and subsequent wireless communication systems. Conventionally, the MIMO-OFDM receiver is performed by multiple cascaded blocks with different functions and the algorithm in each block is designed based on ideal assumptions of wireless channel distributions. However, these assumptions may fail in practical complex wireless environments. The deep learning (DL) method has the ability to capture key features from complex and huge data. In this paper, a novel end-to-end MIMO-OFDM receiver framework based on \textit{transformer}, named SigT, is proposed. By regarding the signal received from each antenna as a token of the transformer, the spatial correlation of different antennas can be learned and the critical zero-shot problem can be mitigated. Furthermore, the proposed SigT framework can work well without the inserted pilots, which improves the useful data transmission efficiency. Experiment results show that SigT achieves much higher performance in terms of signal recovery accuracy than benchmark methods, even in a low SNR environment or with a small number of training samples. Code is available at https://github.com/SigTransformer/SigT.

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