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In this paper, we explore neural network-based strategies for performingsymbol detection in a MIMO-OFDM system. Building on a reservoir computing(RC)-based approach towards symbol detection, we introduce a symmetric anddecomposed binary decision neural network to take advantage of the structureknowledge inherent in the MIMO-OFDM system. To be specific, the binary decisionneural network is added in the frequency domain utilizing the knowledge of theconstellation. We show that the introduced symmetric neural network candecompose the original $M$-ary detection problem into a series of binaryclassification tasks, thus significantly reducing the neural network detectorcomplexity while offering good generalization performance with limited trainingoverhead. Numerical evaluations demonstrate that the introduced hybridRC-binary decision detection framework performs close to maximum likelihoodmodel-based symbol detection methods in terms of symbol error rate in the lowSNR regime with imperfect channel state information (CSI).

Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection