Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling

A novel downlink scheduling strategy for time-division duplexing massive multiple-input multiple-output networks is proposed to relieve the pilot contamination. Specifically, users of slow-channel variation and simultaneous uplink pilot transmission are scheduled in different downlink subframes for...

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Main Authors: Zezhong Zhang, Yang Li, Rui Wang
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8435911/
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author Zezhong Zhang
Yang Li
Rui Wang
author_facet Zezhong Zhang
Yang Li
Rui Wang
author_sort Zezhong Zhang
collection DOAJ
description A novel downlink scheduling strategy for time-division duplexing massive multiple-input multiple-output networks is proposed to relieve the pilot contamination. Specifically, users of slow-channel variation and simultaneous uplink pilot transmission are scheduled in different downlink subframes for adjacent cells, and hence the pilot contamination is significantly suppressed at the price of outdated channel state information. We investigate this trade-off by deriving the distribution of downlink signal-to-interference ratio in terms of Doppler effect, which can be approximated tightly and simply by Gaussian distribution. Based on it, a learning-based algorithm is developed for robust downlink rate allocation without priori knowledge on the users' distribution. It is shown that the proposed cross-frame strategy can effectively improve the overall downlink performance of cell-edge users.
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spelling doaj.art-95425cacc1ec4b23843b1a65a84dab462022-12-21T18:18:26ZengIEEEIEEE Access2169-35362018-01-016448584486710.1109/ACCESS.2018.28654418435911Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame SchedulingZezhong Zhang0Yang Li1Rui Wang2https://orcid.org/0000-0003-0044-8932Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, ChinaA novel downlink scheduling strategy for time-division duplexing massive multiple-input multiple-output networks is proposed to relieve the pilot contamination. Specifically, users of slow-channel variation and simultaneous uplink pilot transmission are scheduled in different downlink subframes for adjacent cells, and hence the pilot contamination is significantly suppressed at the price of outdated channel state information. We investigate this trade-off by deriving the distribution of downlink signal-to-interference ratio in terms of Doppler effect, which can be approximated tightly and simply by Gaussian distribution. Based on it, a learning-based algorithm is developed for robust downlink rate allocation without priori knowledge on the users' distribution. It is shown that the proposed cross-frame strategy can effectively improve the overall downlink performance of cell-edge users.https://ieeexplore.ieee.org/document/8435911/Massive MIMOuser schedulingreinforcement learningDoppler effect
spellingShingle Zezhong Zhang
Yang Li
Rui Wang
Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling
IEEE Access
Massive MIMO
user scheduling
reinforcement learning
Doppler effect
title Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling
title_full Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling
title_fullStr Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling
title_full_unstemmed Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling
title_short Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling
title_sort suppressing pilot contamination in massive mimo downlink via cross frame scheduling
topic Massive MIMO
user scheduling
reinforcement learning
Doppler effect
url https://ieeexplore.ieee.org/document/8435911/
work_keys_str_mv AT zezhongzhang suppressingpilotcontaminationinmassivemimodownlinkviacrossframescheduling
AT yangli suppressingpilotcontaminationinmassivemimodownlinkviacrossframescheduling
AT ruiwang suppressingpilotcontaminationinmassivemimodownlinkviacrossframescheduling