Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization
In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a mini...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/162973 |
| _version_ | 1826129652600537088 |
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| author | Xiu, Yue Zhao, Jun Sun, Wei Renzo, Marco Di Gui, Guan Zhang, Zhongpei Wei, Ning |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Xiu, Yue Zhao, Jun Sun, Wei Renzo, Marco Di Gui, Guan Zhang, Zhongpei Wei, Ning |
| author_sort | Xiu, Yue |
| collection | NTU |
| description | In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a maximum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization (AO) algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem by keeping fixed the phase shifts of the RIS and the hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed AO algorithm outperforms existing schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate. |
| first_indexed | 2024-10-01T07:44:01Z |
| format | Journal Article |
| id | ntu-10356/162973 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:44:01Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1629732022-11-14T04:34:14Z Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization Xiu, Yue Zhao, Jun Sun, Wei Renzo, Marco Di Gui, Guan Zhang, Zhongpei Wei, Ning School of Computer Science and Engineering Engineering::Computer science and engineering Reconfigurable Intelligent Surface Millimeter Wave In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a maximum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization (AO) algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem by keeping fixed the phase shifts of the RIS and the hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed AO algorithm outperforms existing schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate. Ministry of Education (MOE) The work of Yue Xiu, Zhongpei Zhang, and Ning Wei was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1802000 and in part by the National Natural Science Foundation of China (NSFC) under Grant 91938202 and Grant 61871070. The work of Jun Zhao was supported by the Singapore Ministry of Education Academic Research Fund Tier 2 under Grant MOE2019-T2-1-176 and in part by the AI Singapore (AISG) 100 Experiments (100E) Program. The work of Marco Di Renzo was supported in part by the European Commission through the H2020 ARIADNE Project under Agreement 871464 and through the H2020 RISE-6G Project under Agreement 101017011. 2022-11-14T04:34:13Z 2022-11-14T04:34:13Z 2021 Journal Article Xiu, Y., Zhao, J., Sun, W., Renzo, M. D., Gui, G., Zhang, Z. & Wei, N. (2021). Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization. IEEE Transactions On Wireless Communications, 20(12), 8393-8409. https://dx.doi.org/10.1109/TWC.2021.3092597 1536-1276 https://hdl.handle.net/10356/162973 10.1109/TWC.2021.3092597 2-s2.0-85113209560 12 20 8393 8409 en MOE2019-T2-1-176 IEEE Transactions on Wireless Communications © 2021 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Reconfigurable Intelligent Surface Millimeter Wave Xiu, Yue Zhao, Jun Sun, Wei Renzo, Marco Di Gui, Guan Zhang, Zhongpei Wei, Ning Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization |
| title | Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization |
| title_full | Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization |
| title_fullStr | Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization |
| title_full_unstemmed | Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization |
| title_short | Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization |
| title_sort | reconfigurable intelligent surfaces aided mmwave noma joint power allocation phase shifts and hybrid beamforming optimization |
| topic | Engineering::Computer science and engineering Reconfigurable Intelligent Surface Millimeter Wave |
| url | https://hdl.handle.net/10356/162973 |
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