Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection
This work addresses the problem of efficient and accurate symbol detection in MIMO-OFDM systems for wireless communication, offering a lower-complexity alternative to traditional methods.
This paper proposes a neural network-based approach for symbol detection in MIMO-OFDM systems. By introducing a symmetric and decomposed binary decision neural network that leverages system structure and constellation knowledge, the method significantly reduces detector complexity by transforming M-ary detection into binary classification tasks, achieving performance close to maximum likelihood detection in low SNR with imperfect CSI.
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).