Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper)
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 (MIM...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-03-01
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| Series: | Intelligent and Converged Networks |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/ICN.2022.0003 |
| _version_ | 1828140988328050688 |
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| author | Yu Lu Mo Hao Richard Mackenzie |
| author_facet | Yu Lu Mo Hao Richard Mackenzie |
| author_sort | Yu Lu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T19:18:06Z |
| format | Article |
| id | doaj.art-ac78bfa37f704413955402b3267df3cb |
| institution | Directory Open Access Journal |
| issn | 2708-6240 |
| language | English |
| last_indexed | 2024-04-11T19:18:06Z |
| publishDate | 2022-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Intelligent and Converged Networks |
| spelling | doaj.art-ac78bfa37f704413955402b3267df3cb2022-12-22T04:07:23ZengTsinghua University PressIntelligent and Converged Networks2708-62402022-03-013110311810.23919/ICN.2022.0003Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper)Yu Lu0Mo Hao1Richard Mackenzie2Beijing National Research Center for Information Science and Technology (BNRist), and the Department of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaTsinghua SEM Advanced ICT Laboratory, Tsinghua University, Beijing 100084, ChinaBT Technology, Adastral Park, Ipswich, IP5 3RE, UKBenefiting 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.https://www.sciopen.com/article/10.23919/ICN.2022.0003reconfigurable intelligent surface (ris)thz communicationmassive multiple-input multiple-output (mimo)hybrid precodingdeep learning |
| spellingShingle | Yu Lu Mo Hao Richard Mackenzie Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper) Intelligent and Converged Networks reconfigurable intelligent surface (ris) thz communication massive multiple-input multiple-output (mimo) hybrid precoding deep learning |
| title | Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper) |
| title_full | Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper) |
| title_fullStr | Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper) |
| title_full_unstemmed | Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper) |
| title_short | Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper) |
| title_sort | reconfigurable intelligent surface based hybrid precoding for thz communications invited paper |
| topic | reconfigurable intelligent surface (ris) thz communication massive multiple-input multiple-output (mimo) hybrid precoding deep learning |
| url | https://www.sciopen.com/article/10.23919/ICN.2022.0003 |
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