Joint hybrid 3D beamforming relying on sensor-based training for reconfigurable intelligent surface aided TeraHertz-based multi-user massive MIMO systems

Terahertz (THz) systems have the benefit of high bandwidth and hence are capable of supporting ultra-high data rates, albeit at the cost of high pathloss. Hence they tend to harness high-gain beamforming. Therefore a joint hybrid 3D beamformer relying on sophisticated sensorbased beam training and channel estimation is proposed for Reconfigurable Intelligent Surface (RIS) aided THz Multi-user Massive Multiple Input Multiple Output (MIMO) systems. A novel joint subarray based THz base station (BS) architecture and the corresponding sub-RIS is proposed. The BS, RIS and receiver antenna arrays of the users are all Uniform Planar Arrays (UPAs). Moreover, the conditions of maintaining the orthogonality of the proposed joint architecture are derived in support of spatial multiplexing. The closed-form expressions of the near-field and far-field pathloss are also derived. The Ultra-wideband (UWB) sensors are integrated into the RIS and the user location information obtained by the UWB sensors is exploited for channel estimation. The optimal active and passive beamforming schemes are also derived. Moreover, Precise Beamforming Algorithm (PBA) for joint RIS phase shift and user equipment (UE) receiver beamforming is proposed, which further improves the beamforming accuracy by circumventing the performance limitations imposed by positioning errors. Our simulation results show that the proposed system significantly improves the spectral efficiency, despite its low complexity. Compared to the scheme operating without PBA, our proposed scheme increases the spectral efficiency on average by 10.41%, 10.17%, and 5.19% for the three farfield configurations, and by 5.05% and 3.95% for the two nearfield configurations, respectively. This makes our solution eminently suitable for delay sensitive applications.

Antenna arrays, Array signal processing, Channel estimation, Joint hybrid 3D beamforming, Massive MIMO, Radio frequency, TeraHertz, Three-dimensional displays, Training, UWB sensors, beam-training, sub-RIS

10.1109/JSEN.2022.3182881

1530-437X

14540-14552

Wang, Xufang

abd5b967-032e-4011-81bb-68e5d40e9dba

Lin, Zihuai

ccf46fdb-cda4-4fbe-9cab-41ba727def88

Lin, Feng

07c7383d-53a3-41bb-82f4-98c24495fd01

Hanzo, Lajos

66e7266f-3066-4fc0-8391-e000acce71a1

15 July 2022

Wang, Xufang

abd5b967-032e-4011-81bb-68e5d40e9dba

Lin, Zihuai

ccf46fdb-cda4-4fbe-9cab-41ba727def88

Lin, Feng

07c7383d-53a3-41bb-82f4-98c24495fd01

Hanzo, Lajos

66e7266f-3066-4fc0-8391-e000acce71a1

Wang, Xufang, Lin, Zihuai, Lin, Feng and Hanzo, Lajos (2022) Joint hybrid 3D beamforming relying on sensor-based training for reconfigurable intelligent surface aided TeraHertz-based multi-user massive MIMO systems. IEEE Sensors Journal, 22 (14), 14540-14552. (doi:10.1109/JSEN.2022.3182881).

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Sensors-47919-2022.R1 - Accepted Manuscript

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Published date: 15 July 2022

Additional Information: Funding Information: Date of publication 20 June 2022; date of current version 14 July 2022. This work was supported by the Fujian Provincial Department of Science and Technology Project 2019J01267. The work of Lajos Hanzo was supported in part by the Engineering and Physical Sciences Research Council throughCOALESCE under Project EP/P034284/1 and Project EP/P003990/1 and in part by the European Research Council's Advanced Fellow Grant QuantCom under Grant 789028 Publisher Copyright: © 2001-2012 IEEE.

Keywords: Antenna arrays, Array signal processing, Channel estimation, Joint hybrid 3D beamforming, Massive MIMO, Radio frequency, TeraHertz, Three-dimensional displays, Training, UWB sensors, beam-training, sub-RIS

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