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 (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.

Automated summary

This study proposes a low complexity ML-MMSE receiver for mmWave systems, which achieves fast beam pair acquisition and data symbol detection through a hybrid beamforming transceiver architecture. The algorithm can track the best receiving beams and produce data estimates for soft decoding while adapting to dynamic changes in the baseband effective channel.