Deep learning driven beam selection for orthogonal beamforming with limited feedback
This letter studies deep learning methods for beam selection in multiuser beamforming with limited feedback. We construct a set of orthogonal random beams and allocate the beams to users to maximize the sum rate, based on limited feedback regarding the channel power on the orthogonal beams. We formu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | ICT Express |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959521001430 |
| _version_ | 1811277019245182976 |
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| author | Jinho Choi Moldir Yerzhanova Jihong Park Yun Hee Kim |
| author_facet | Jinho Choi Moldir Yerzhanova Jihong Park Yun Hee Kim |
| author_sort | Jinho Choi |
| collection | DOAJ |
| description | This letter studies deep learning methods for beam selection in multiuser beamforming with limited feedback. We construct a set of orthogonal random beams and allocate the beams to users to maximize the sum rate, based on limited feedback regarding the channel power on the orthogonal beams. We formulate the beam allocation problem as a classification or a regression task using a deep neural network (DNN). The results demonstrate that the DNN-based methods achieve higher sum rates than a conventional limited feedback solution in the low signal-to-noise ratio regime under Rician fading, thanks to their robustness to noisy limited feedback. |
| first_indexed | 2024-04-13T00:08:10Z |
| format | Article |
| id | doaj.art-83793ba9b07f4c0a85b244fd57227439 |
| institution | Directory Open Access Journal |
| issn | 2405-9595 |
| language | English |
| last_indexed | 2024-04-13T00:08:10Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj.art-83793ba9b07f4c0a85b244fd572274392022-12-22T03:11:10ZengElsevierICT Express2405-95952022-09-0183473478Deep learning driven beam selection for orthogonal beamforming with limited feedbackJinho Choi0Moldir Yerzhanova1Jihong Park2Yun Hee Kim3School of Information Technology, Deakin University, Burwood, AustraliaDepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin, Republic of KoreaSchool of Information and Technology, Deakin University, Geelong, AustraliaDepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin, Republic of Korea; Corresponding author.This letter studies deep learning methods for beam selection in multiuser beamforming with limited feedback. We construct a set of orthogonal random beams and allocate the beams to users to maximize the sum rate, based on limited feedback regarding the channel power on the orthogonal beams. We formulate the beam allocation problem as a classification or a regression task using a deep neural network (DNN). The results demonstrate that the DNN-based methods achieve higher sum rates than a conventional limited feedback solution in the low signal-to-noise ratio regime under Rician fading, thanks to their robustness to noisy limited feedback.http://www.sciencedirect.com/science/article/pii/S2405959521001430Downlink beamformingDeep learningLimited feedbackOrthogonal beam selection |
| spellingShingle | Jinho Choi Moldir Yerzhanova Jihong Park Yun Hee Kim Deep learning driven beam selection for orthogonal beamforming with limited feedback ICT Express Downlink beamforming Deep learning Limited feedback Orthogonal beam selection |
| title | Deep learning driven beam selection for orthogonal beamforming with limited feedback |
| title_full | Deep learning driven beam selection for orthogonal beamforming with limited feedback |
| title_fullStr | Deep learning driven beam selection for orthogonal beamforming with limited feedback |
| title_full_unstemmed | Deep learning driven beam selection for orthogonal beamforming with limited feedback |
| title_short | Deep learning driven beam selection for orthogonal beamforming with limited feedback |
| title_sort | deep learning driven beam selection for orthogonal beamforming with limited feedback |
| topic | Downlink beamforming Deep learning Limited feedback Orthogonal beam selection |
| url | http://www.sciencedirect.com/science/article/pii/S2405959521001430 |
| work_keys_str_mv | AT jinhochoi deeplearningdrivenbeamselectionfororthogonalbeamformingwithlimitedfeedback AT moldiryerzhanova deeplearningdrivenbeamselectionfororthogonalbeamformingwithlimitedfeedback AT jihongpark deeplearningdrivenbeamselectionfororthogonalbeamformingwithlimitedfeedback AT yunheekim deeplearningdrivenbeamselectionfororthogonalbeamformingwithlimitedfeedback |