RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection

TL;DR

RC-Struct is a novel neural network architecture leveraging reservoir computing and constellation structure for efficient, online MIMO-OFDM symbol detection, outperforming traditional and existing learning methods especially with limited pilot symbols.

Contribution

Introduces RC-Struct, a structure-based neural network that combines reservoir computing and constellation knowledge for online, efficient MIMO-OFDM detection with minimal pilot symbols.

Findings

Outperforms conventional and state-of-the-art methods in BER

Effective with limited pilot symbols and high-order modulations

Benefits increase with rank and link adaptation

Abstract

In this paper, we introduce a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A binary classifier leverages the repetitive constellation structure in the system to perform multi-class detection. The incorporation of RC allows the RC-Struct to be learned in a purely online fashion with extremely limited pilot symbols in each OFDM subframe. The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity. Experiments show that the introduced RC-Struct outperforms both the conventional model-based symbol detection approaches and the state-of-the-art learning-based strategies in terms of bit error rate (BER). The advantages of RC-Struct over existing methods become more significant when rank and link adaptation are adopted. The introduced RC-Struct sheds light on combining communication domain knowledge and learning-based receive processing for 5G/5G-Advanced and Beyond.