Machine Learning-Based Channel Estimation in Massive MIMO with Channel Aging

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
752 Downloads (Pure)

Abstract

To support the ever increasing number of devices in massive multiple-input multiple-output systems, an excessive amount of overhead is required for conventional orthogonal pilot based channel estimation (CE) schemes. To relax this stringent constraint, we design a machine learning (ML)-based time division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The proposed ML-based predictors involve a pattern extraction and CSI predictor, which are implemented via a convolutional neural network (CNN) and autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network (NARX-RNN), respectively. Numerical results demonstrate that ML-based predictors can remarkably improve the prediction quality, and the optimal CE overhead is provided for practical reference.
Original languageEnglish
Title of host publication20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2019): Proceedings
Publisher IEEE
Number of pages5
DOIs
Publication statusPublished - 29 Aug 2019

Publication series

NameInternational Workshop on Signal Processing Advances in Wireless Communications (SPAWC): Proceedings
PublisherIEEE
ISSN (Electronic)1948-3252

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