  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.

Signal Processing;Machine Learning