  In this paper, we explore neural network-based strategies for performing
symbol detection in a MIMO-OFDM system. Building on a reservoir computing
(RC)-based approach towards symbol detection, we introduce a symmetric and
decomposed binary decision neural network to take advantage of the structure
knowledge inherent in the MIMO-OFDM system. To be specific, the binary decision
neural network is added in the frequency domain utilizing the knowledge of the
constellation. We show that the introduced symmetric neural network can
decompose the original $M$-ary detection problem into a series of binary
classification tasks, thus significantly reducing the neural network detector
complexity while offering good generalization performance with limited training
overhead. Numerical evaluations demonstrate that the introduced hybrid
RC-binary decision detection framework performs close to maximum likelihood
model-based symbol detection methods in terms of symbol error rate in the low
SNR regime with imperfect channel state information (CSI).
