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

• Published in: 2020 29th Wireless and Optical Communications Conference (WOCC) pp. 1 - 6
• Main Authors: Li, Lianjun, Liu, Lingjia, Zhang, Jianzhong, Ashdown, Jonathan D., Yi, Yang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2020
• Subjects: limited training; neural network; reservoir computing; symbol detection; USRP; Wi-Fi
• ISSN: 2379-1276
• DOI: 10.1109/WOCC48579.2020.9114937

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.