@inproceedings{f3ddcb4a762c460aa3efd75e685f980e,
title = "HyperRNN: Deep Learning-Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO",
abstract = "In order to unlock the full advantages of massive multiple input multiple output (MIMO) in the downlink, channel state information (CSI) is required at the base station (BS) to optimize the beamforming matrices. In frequency division duplex (FDD) systems, full channel reciprocity does not hold, and CSI acquisition generally requires downlink pilot transmission followed by uplink feedback. Prior work proposed the end-to-end design of pilot transmission, feedback, and CSI estimation via deep learning. In this work, we introduce an enhanced end-to-end design that leverages partial uplink-downlink reciprocity and temporal correlation of the fading processes by utilizing jointly downlink and uplink pilots. The proposed method is based on a novel deep learning architecture - HyperRNN - that combines hypernetworks and recurrent neural networks (RNNs) to optimize the transfer of long-term channel features from uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a lower normalized mean square error (NMSE) performance, and that it reduces requirements in terms of pilot lengths.",
keywords = "FDD, deep learning, massive MIMO",
author = "Yusha Liu and Osvaldo Simeone",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021 ; Conference date: 27-09-2021 Through 30-09-2021",
year = "2021",
doi = "10.1109/SPAWC51858.2021.9593238",
language = "English (US)",
series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "31--35",
booktitle = "2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021",
address = "United States",
}