A ML-MMSE Receiver for Millimeter Wave User-Equipment Detection: Beamforming, Beamtracking, and Data-Symbols Detection

For a millimeter wave (mmWave) system consisting of a basestation (BS) and a mobile user-equipment (UE), the problem of signal-and-data detection is investigated, and a low complexity ML-MMSE receiver for mmWave beamforming, beamtracking, and data-symbols detection is proposed. Specifically, with a...

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Bibliographic Details
Published inIEEE transactions on wireless communications Vol. 20; no. 8; pp. 5301 - 5313
Main Authors Yu, Jiun-Hung, Yu, Zhen-Hao, Wu, Kang-Li, Lee, Ta-Sung, Su, Yu T.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Array signal processing
Baseband
Beamforming
beamtracking
Chains
Channel estimation
Manifolds
Millimeter wave communication
Millimeter waves
ML-MMSE receiver
mmWave signal detection
Radio frequency
Symbols
Training
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Summary:For a millimeter wave (mmWave) system consisting of a basestation (BS) and a mobile user-equipment (UE), the problem of signal-and-data detection is investigated, and a low complexity ML-MMSE receiver for mmWave beamforming, beamtracking, and data-symbols detection is proposed. Specifically, with a (practical) hybrid beamforming transceiver architecture at both the BS and UE, a multistage angle-of-arrival (AoA) estimation based beamtraining algorithm that provides fast acquisition of the best beam pairs for the BS-UE link is developed. In addition, a novel joint beamtracking and data-symbols detection algorithm, equipped with an adaptive equalizer, is also developed. The algorithm can simultaneously track the best receiving beams, and produce the data estimates that are easy to extract soft bit information for soft decoding; also, it can tackle the dynamic changes of the baseband effective channel. Analytical and simulation results show that the proposed receiver performs well over a broad range of SNR-it can rapidly acquire the most dominant AoAs for beamforming and constantly track the best moving beams due to UE's mobility or device rotation-and in particular, it achieves near-optimal spectral efficiency for a mobile UE with a single RF chain or very few RF chains.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2021.3066652