Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks

In cooperative massive multiple-input multiple-output (MIMO) networks, channel statistics based adaptive feedback can considerably reduce feedback overhead for channel state information (CSI) acquisition as well as backhaul overhead for CSI sharing. When regularized zero-forcing beamforming is consi...

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Main Authors: Jinho Kang, Wan Choi
Format: Article
Language:English
Published: Elsevier 2021-03-01
Series:ICT Express
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2405959521000060
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spelling doaj-10121f150a87455c896cd44804b163bb2021-03-11T04:25:40ZengElsevierICT Express2405-95952021-03-01711014Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networksJinho Kang0Wan Choi1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Institute of New Media and Communications and Department of Electrical and Computer Engineering, Seoul National University (SNU), Seoul 08826, Republic of KoreaCorresponding author at: Institute of New Media and Communications and Department of Electrical and Computer Engineering, Seoul National University (SNU), Seoul 08826, Republic of Korea.; School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Institute of New Media and Communications and Department of Electrical and Computer Engineering, Seoul National University (SNU), Seoul 08826, Republic of KoreaIn cooperative massive multiple-input multiple-output (MIMO) networks, channel statistics based adaptive feedback can considerably reduce feedback overhead for channel state information (CSI) acquisition as well as backhaul overhead for CSI sharing. When regularized zero-forcing beamforming is considered to coordinate interference with the skewed codebook, average sum rate depends on not only regularization parameters, but also quantization error impacts of serving and interfering channels according to their channel covariance matrices. To improve the average sum rate by effectively controlling the desired signal strength and the interference cancellation, we propose joint optimization of regularization parameters and feedback bit allocation by leveraging adaptive feedback according to the channel covariance matrices.http://www.sciencedirect.com/science/article/pii/S2405959521000060Massive MIMOCooperationBeamformingLimited feedbackChannel statistics
collection DOAJ
language English
format Article
sources DOAJ
author Jinho Kang
Wan Choi
spellingShingle Jinho Kang
Wan Choi
Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks
ICT Express
Massive MIMO
Cooperation
Beamforming
Limited feedback
Channel statistics
author_facet Jinho Kang
Wan Choi
author_sort Jinho Kang
title Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks
title_short Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks
title_full Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks
title_fullStr Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks
title_full_unstemmed Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks
title_sort coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive mimo networks
publisher Elsevier
series ICT Express
issn 2405-9595
publishDate 2021-03-01
description In cooperative massive multiple-input multiple-output (MIMO) networks, channel statistics based adaptive feedback can considerably reduce feedback overhead for channel state information (CSI) acquisition as well as backhaul overhead for CSI sharing. When regularized zero-forcing beamforming is considered to coordinate interference with the skewed codebook, average sum rate depends on not only regularization parameters, but also quantization error impacts of serving and interfering channels according to their channel covariance matrices. To improve the average sum rate by effectively controlling the desired signal strength and the interference cancellation, we propose joint optimization of regularization parameters and feedback bit allocation by leveraging adaptive feedback according to the channel covariance matrices.
topic Massive MIMO
Cooperation
Beamforming
Limited feedback
Channel statistics
url http://www.sciencedirect.com/science/article/pii/S2405959521000060
work_keys_str_mv AT jinhokang coordinatedregularizedzeroforcingbeamformingwithchannelstatisticsbasedadaptivefeedbackforcooperativemassivemimonetworks
AT wanchoi coordinatedregularizedzeroforcingbeamformingwithchannelstatisticsbasedadaptivefeedbackforcooperativemassivemimonetworks
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