Learning for Detection: MIMO-OFDM Symbol Detection through Downlink Pilots

Abstract

Reservoir computing (RC) is a special recurrent neural network which consists of a fixed high dimensional feature mapping and trained readout weights. In this paper, we introduce a new RC structure for multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) symbol detection, namely windowed echo state network (WESN). The theoretical analysis shows that adding buffers in input layers can bring an enhanced short-term memory (STM) to the underlying neural network. Furthermore, a unified training framework is developed for the WESN MIMO-OFDM symbol detector using both comb and scattered pilot patterns that are compatible with the structure adopted in 3GPP LTE/LTE-Advanced systems. Complexity analysis suggests the advantages of WESN based symbol detector over state-of-the-art symbol detectors such as the linear minimum mean square error (LMMSE) detection and the sphere decoder, when the system is employed with a large number of OFDM sub-carriers. Numerical evaluations illustrate the advantage of the introduced WESN-based symbol detector and demonstrate that the improvement of STM can significantly improve symbol detection performance as well as effectively mitigate model mismatch effects compared to existing methods.

References in corpus (8)

• On the difficulty of training Recurrent Neural Networks
• Deep Learning-Based Communication Over the Air
• Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs
• Learning to Detect
• End-to-end Deep Learning of Optical Fiber Communications
• End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks
• AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
• Improving Massive MIMO Belief Propagation Detector with Deep Neural Network

Cited by in corpus (7)

• Machine Learning for MU-MIMO Receive Processing in OFDM Systems
• RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection
• Underwater Acoustic Communication Channel Modeling using Reservoir Computing
• Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing
• Learning Low-Dimensional Representation for O-RAN Testing via Transformer-ESN
• Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection
• Learning to Equalize OTFS