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

TL;DR

This paper presents a neural network approach that leverages the structure of MIMO-OFDM systems to improve symbol detection, reducing complexity and maintaining high accuracy even with limited training data.

Contribution

It introduces a symmetric binary decision neural network integrated with reservoir computing, exploiting system structure for efficient MIMO-OFDM detection.

Findings

Performs close to maximum likelihood detection at low SNR

Reduces neural network complexity via binary classification

Achieves good generalization with limited training data

Abstract

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).