@inproceedings{b9f0d378b61846b89cbe084ea04f163c,
title = "Joint Channel Estimation and Hybrid Beamforming via Deep-Unfolding",
abstract = "In this work, we propose an end-to-end deep-unfolding neural network (NN) based joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the sum rate in massive multiple-input multiple-output (MIMO) systems. Specifically, the recursive least-squares (RLS) and stochastic successive convex approximation (SSCA) algorithms are unfolded for channel estimation and hybrid beamforming, respectively. We consider a mixed-timescale scheme, where analog beamforming matrices are designed based on the channel state information (CSI) statistics once in each frame, while the digital beamforming matrices are designed at each time slot based on the equivalent CSI matrices. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms.",
keywords = "Deep-unfolding, channel estimation, hybrid beamforming, massive MIMO, mixed-timescale scheme",
author = "Kai Kang and Qiyu Hu and Yunlong Cai and Guanding Yu and Jakob Hoydis and Eldar, {Yonina C}",
note = "Publisher Copyright: {\textcopyright} 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.; 2022 30th European Signal Processing Conference (EUSIPCO) ; Conference date: 29-08-2022 Through 02-09-2022",
year = "2022",
month = oct,
day = "18",
doi = "https://doi.org/10.23919/EUSIPCO55093.2022.9909602",
language = "الإنجليزيّة",
series = "2022-August",
pages = "658--662",
booktitle = "2022 30th European Signal Processing Conference (EUSIPCO)",
}