Reservoir Computing Meets Wi-Fi in Software Radios: Neural Network-based Symbol Detection using Training Sequences and Pilots

Lianjun Li, Lingjia Liu, Jianzhong Zhang, J. Ashdown, Y. Yi
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引用次数: 8

Abstract

In this paper, we introduce a neural network (NN)based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software defined radio (SDR) platform to further provide realistic and meaningful performance comparison against the traditional Wi-Fi receiver. Over the air experiment results show that the introduced RC-based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work.
水库计算满足软件无线电中的Wi-Fi:使用训练序列和飞行员的基于神经网络的符号检测
本文介绍了一种基于神经网络的Wi-Fi系统符号检测方案及其在软件无线电中的相关硬件实现。具体来说,采用一种特殊的递归神经网络(RNN)——储层计算(RC)来完成Wi-Fi接收机的符号检测任务。本文没有引入额外的训练开销/设置来促进基于rc的符号检测,而是引入了一种新的训练框架,利用现有Wi-Fi协议(如IEEE 802.11标准)中的信号结构,即基于rc的符号检测器将利用Wi-Fi发射机发送的固有的长/短训练序列和结构化导频对发送符号进行在线学习。换句话说,与现有的Wi-Fi系统相比,我们引入的基于神经网络的符号检测器不需要任何额外的训练集。本文介绍的基于rc的Wi-Fi符号检测器在软件定义无线电(SDR)平台上实现,进一步提供与传统Wi-Fi接收机真实而有意义的性能比较。空中实验结果表明,所引入的基于rc的Wi-Fi符号检测器在各种环境下都优于传统的Wi-Fi符号检测方法,这表明了我们工作的重要性和相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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