A deep learning driven hybrid beamforming method for millimeter wave MIMO system
The hybrid beamforming is a promising technology for the millimeter wave MIMO system, which provides high spectrum efficiency, high data rate transmission, and a good balance between transmission performance and hardware complexity. The most existing beamforming systems transmit multiple streams by...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-12-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864822001468 |
| _version_ | 1797382868467449856 |
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| author | Jienan Chen Jiyun Tao Siyu Luo Shuai Li Chuan Zhang Wei Xiang |
| author_facet | Jienan Chen Jiyun Tao Siyu Luo Shuai Li Chuan Zhang Wei Xiang |
| author_sort | Jienan Chen |
| collection | DOAJ |
| description | The hybrid beamforming is a promising technology for the millimeter wave MIMO system, which provides high spectrum efficiency, high data rate transmission, and a good balance between transmission performance and hardware complexity. The most existing beamforming systems transmit multiple streams by formulating multiple orthogonal beams. However, the Neural network Hybrid Beamforming (NHB) adopts a totally different strategy, which combines multiple streams into one and transmits by employing a high-order non-orthogonal modulation strategy. Driven by the Deep Learning (DL) hybrid beamforming, in this work, we propose a DL-driven non-orthogonal hybrid beamforming for the single-user multiple streams scenario. We first analyze the beamforming strategy of NHB and prove it with better Bit Error Rate (BER) performance than the orthogonal hybrid beamforming even with the optimal power allocation. Inspired by the NHB, we propose a new DL-driven beamforming scheme to simulate the NHB behavior, which avoids time-consuming neural network training and achieves better BERs than traditional hybrid beamforming. Moreover, our simulation results demonstrate that the DL-driven non-orthogonal beamforming outperforms its traditional orthogonal beamforming counterpart in the presence of sub-connected schemes and imperfect Channel State Information (CSI). |
| first_indexed | 2024-03-08T21:12:34Z |
| format | Article |
| id | doaj.art-9ad478aa3cdc48c79ee5899c59c909e4 |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-03-08T21:12:34Z |
| publishDate | 2023-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-9ad478aa3cdc48c79ee5899c59c909e42023-12-22T05:33:29ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482023-12-019612911300A deep learning driven hybrid beamforming method for millimeter wave MIMO systemJienan Chen0Jiyun Tao1Siyu Luo2Shuai Li3Chuan Zhang4Wei Xiang5The National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, ChinaThe National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, ChinaThe National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, ChinaThe National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, ChinaNational Mobile Communications Research Laboratory, Southeast University, Purple Mountain Laboratories, Nanjing, 210096, ChinaSchool of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, VIC, 3086, Australia; Corresponding author.The hybrid beamforming is a promising technology for the millimeter wave MIMO system, which provides high spectrum efficiency, high data rate transmission, and a good balance between transmission performance and hardware complexity. The most existing beamforming systems transmit multiple streams by formulating multiple orthogonal beams. However, the Neural network Hybrid Beamforming (NHB) adopts a totally different strategy, which combines multiple streams into one and transmits by employing a high-order non-orthogonal modulation strategy. Driven by the Deep Learning (DL) hybrid beamforming, in this work, we propose a DL-driven non-orthogonal hybrid beamforming for the single-user multiple streams scenario. We first analyze the beamforming strategy of NHB and prove it with better Bit Error Rate (BER) performance than the orthogonal hybrid beamforming even with the optimal power allocation. Inspired by the NHB, we propose a new DL-driven beamforming scheme to simulate the NHB behavior, which avoids time-consuming neural network training and achieves better BERs than traditional hybrid beamforming. Moreover, our simulation results demonstrate that the DL-driven non-orthogonal beamforming outperforms its traditional orthogonal beamforming counterpart in the presence of sub-connected schemes and imperfect Channel State Information (CSI).http://www.sciencedirect.com/science/article/pii/S2352864822001468Hybrid beamformingNeural networkDeep learning drivenNon-orthogonal beamforming |
| spellingShingle | Jienan Chen Jiyun Tao Siyu Luo Shuai Li Chuan Zhang Wei Xiang A deep learning driven hybrid beamforming method for millimeter wave MIMO system Digital Communications and Networks Hybrid beamforming Neural network Deep learning driven Non-orthogonal beamforming |
| title | A deep learning driven hybrid beamforming method for millimeter wave MIMO system |
| title_full | A deep learning driven hybrid beamforming method for millimeter wave MIMO system |
| title_fullStr | A deep learning driven hybrid beamforming method for millimeter wave MIMO system |
| title_full_unstemmed | A deep learning driven hybrid beamforming method for millimeter wave MIMO system |
| title_short | A deep learning driven hybrid beamforming method for millimeter wave MIMO system |
| title_sort | deep learning driven hybrid beamforming method for millimeter wave mimo system |
| topic | Hybrid beamforming Neural network Deep learning driven Non-orthogonal beamforming |
| url | http://www.sciencedirect.com/science/article/pii/S2352864822001468 |
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