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Intelligent and Converged Networks 2022, 3(1): 103-118 https://doi.org/10.23919/ICN.2022.0003
Open Access | Issue | Published: 30 March 2022

Reconfigurable intelligent surface based hybrid precoding for THz communications

Show Author's Information Yu Lu1( ),  Mo Hao2,  Richard Mackenzie3
Beijing National Research Center for Information Science and Technology (BNRist), and the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Tsinghua SEM Advanced ICT Laboratory, Tsinghua University, Beijing 100084, China
BT Technology, Adastral Park, Ipswich, IP5 3RE, UK
Keywords:
deep learning, Reconfigurable Intelligent Surface (RIS), THz communication, massive Multiple-Input Multiple-Output (MIMO), hybrid precoding
Cite this article:
Lu Y, Hao M, Mackenzie R. Reconfigurable intelligent surface based hybrid precoding for THz communications . Intelligent and Converged Networks, 2022, 3(1): 103-118. https://doi.org/10.23919/ICN.2022.0003
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Abstract Full text About this article

Benefiting from the growth of the bandwidth, Terahertz (THz) communication can support the new application with explosive requirements of the ultra-high-speed rates for future 6G wireless systems. In order to compensate for the path loss of high frequency, massive Multiple-Input Multiple-Output (MIMO) can be utilized for high array gains by beamforming. However, the existing THz communication with massive MIMO has remarkably high energy consumption because a large number of analog phase shifters should be used to realize the analog beamforming. To solve this problem, a Reconfigurable Intelligent Surface (RIS) based hybrid precoding architecture for THz communication is developed in this paper, where the energy-hungry phased array is replaced by the energy-efficient RIS to realize the analog beamforming of the hybrid precoding. Then, based on the proposed RIS-based architecture, a sum-rate maximization problem for hybrid precoding is investigated. Since the phase shifts implemented by RIS in practice are often discrete, this sum-rate maximization problem with a non-convex constraint is challenging. Next, the sum-rate maximization problem is reformulated as a parallel Deep Neural Network (DNN) based classification problem, which can be solved by the proposed low-complexity Deep Learning based Multiple Discrete Classification (DL-MDC) hybrid precoding scheme. Finally, we provide numerous simulation results to show that the proposed DL-MDC scheme works well both in the theoretical Saleh-Valenzuela channel model and practical 3GPP channel model. Compared with existing iterative search algorithms, the proposed DL-MDC scheme significantly reduces the runtime with a negligible performance loss.


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About this article

Reconfigurable intelligent surface based hybrid precoding for THz communications

Show Author's information Yu Lu1( ), Mo Hao2, Richard Mackenzie3
Beijing National Research Center for Information Science and Technology (BNRist), and the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Tsinghua SEM Advanced ICT Laboratory, Tsinghua University, Beijing 100084, China
BT Technology, Adastral Park, Ipswich, IP5 3RE, UK

Abstract

Benefiting from the growth of the bandwidth, Terahertz (THz) communication can support the new application with explosive requirements of the ultra-high-speed rates for future 6G wireless systems. In order to compensate for the path loss of high frequency, massive Multiple-Input Multiple-Output (MIMO) can be utilized for high array gains by beamforming. However, the existing THz communication with massive MIMO has remarkably high energy consumption because a large number of analog phase shifters should be used to realize the analog beamforming. To solve this problem, a Reconfigurable Intelligent Surface (RIS) based hybrid precoding architecture for THz communication is developed in this paper, where the energy-hungry phased array is replaced by the energy-efficient RIS to realize the analog beamforming of the hybrid precoding. Then, based on the proposed RIS-based architecture, a sum-rate maximization problem for hybrid precoding is investigated. Since the phase shifts implemented by RIS in practice are often discrete, this sum-rate maximization problem with a non-convex constraint is challenging. Next, the sum-rate maximization problem is reformulated as a parallel Deep Neural Network (DNN) based classification problem, which can be solved by the proposed low-complexity Deep Learning based Multiple Discrete Classification (DL-MDC) hybrid precoding scheme. Finally, we provide numerous simulation results to show that the proposed DL-MDC scheme works well both in the theoretical Saleh-Valenzuela channel model and practical 3GPP channel model. Compared with existing iterative search algorithms, the proposed DL-MDC scheme significantly reduces the runtime with a negligible performance loss.

Keywords: deep learning, Reconfigurable Intelligent Surface (RIS), THz communication, massive Multiple-Input Multiple-Output (MIMO), hybrid precoding

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Published: 30 March 2022
Issue date: March 2022

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© All articles included in the journal are copyrighted to the ITU and TUP.

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This work was supported in part by the National Key Research and Development Program of China (No. 2020YFB1807201) and the National Natural Science Foundation of China (No. 62031019).

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This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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