Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots
Zhou Zhou, Lingjia Liu, Hao-Hsuan Chang
Abstract
In this paper, we introduce a reservoir computing (RC) structure, namely, windowed echo state network (WESN), for multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) symbol detection. We show that adding buffers in input layers is able to bring an enhanced short-term memory (STM) to the standard echo state network. A unified training framework is developed for the introduced WESN MIMO-OFDM symbol detector using both comb and scattered patterns, where the training set size is compatible with those adopted in 3GPP LTE/LTE-Advanced standards. Complexity analysis demonstrates the advantages of WESN based symbol detector over state-of-the-art symbol detectors when the number of OFDM sub-carriers is large, where the benchmark methods are chosen as linear minimum mean square error (LMMSE) detection and sphere decoder. Numerical evaluations suggest that WESN can significantly improve the symbol detection performance as well as effectively mitigate model mismatch effects using very limited training symbols.