2020/03/15 by Zhou Zhou, Lingjia Liu, Zhou, Zhou +7 · 2 citations
Computer Science · Engineering · #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neural Networks and Reservoir Computing #Signal Processing (eess.SP) #Wireless Signal Modulation Classification #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2003.06923
arxiv created 2020/03/15 · openalex publication_date 2020/03/15 · arxiv updated 2020/03/17 · openalex created_date 2020/03/23 · openalex updated_date 2026/07/28
In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC). We first introduce the Time-Frequency RC to take advantage of the structural information inherent in OFDM signals. Using the time domain RC and the time-frequency RC as the building blocks, we provide two extensions of the shallow RC to RCNet: 1) Stacking multiple time domain RCs; 2) Stacking multiple time-frequency RCs into a deep structure. The combination of RNN dynamics, the time-frequency structure of MIMO-OFDM signals, and the deep network enables RCNet to handle the interference and nonlinear distortion of MIMO-OFDM signals to outperform existing methods. Unlike most existing NN-based detection strategies, RCNet is also shown to provide a good generalization performance even with a limited training set (i.e, similar amount of reference signals/training as standard model-based approaches). Numerical experiments demonstrate that the introduced RCNet can offer a faster learning convergence and as much as 20% gain in bit error rate over a shallow RC structure by compensating for the nonlinear distortion of the MIMO-OFDM signal, such as due to power amplifier compression in the transmitter or due to finite quantization resolution in the receiver.