Deep learning-based downlink channel prediction for FDD massive MIMO system
File(s)
OA Location
Author(s)
Yang, Yuwen
Gao, Feifei
Li, Geoffrey Ye
Jian, Mengnan
Type
Journal Article
Abstract
In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the acquisition of downlink channel state information (CSI) at base station (BS) is a very challenging task due to the overwhelming overheads required for downlink training and uplink feedback. In this letter, we reveal a deterministic uplink-to-downlink mapping function when the position-to-channel mapping is bijective. Motivated by the universal approximation theorem, we then propose a sparse complex-valued neural network (SCNet) to approximate the uplink-to-downlink mapping function. Different from general deep networks that operate in the real domain, the SCNet is constructed in the complex domain and is able to learn the complex-valued mapping function by off-line training. After training, the SCNet is used to directly predict the downlink CSI based on the estimated uplink CSI without the need of either downlink training or uplink feedback. Numerical results show that the SCNet achieves better performance than general deep networks in terms of prediction accuracy and exhibits remarkable robustness over complicated wireless channels, demonstrating its great potential for practical deployments.
Date Issued
2019-11-01
Date Acceptance
2019-08-09
Citation
IEEE Communications Letters, 2019, 23 (11), pp.1994-1998
ISSN
1089-7798
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1994
End Page
1998
Journal / Book Title
IEEE Communications Letters
Volume
23
Issue
11
Copyright Statement
© 2019 The Authors. For the purpose of open access, this work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1109/LCOMM.2019.2934851
Subjects
FDD
massive MIMO
downlink CSI prediction
deep learning
complex-valued neural network
Publication Status
Published
Date Publish Online
2019-08-13