BRNN-LSTM for Initial Access in Millimeter Wave Communications
The use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial searc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/13/1505 |
| _version_ | 1797529324999409664 |
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| author | Adel Aldalbahi Farzad Shahabi Mohammed Jasim |
| author_facet | Adel Aldalbahi Farzad Shahabi Mohammed Jasim |
| author_sort | Adel Aldalbahi |
| collection | DOAJ |
| description | The use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial search to determine the best beam directions that return highest signal levels. The high number of signal measurements here prolongs access times and latencies, as well as increasing power and energy consumption. Hence this paper proposes a first study on leveraging deep learning schemes to simplify the beam access procedure in standalone mmWave networks. The proposed scheme combines bidirectional recurrent neural network (BRNN) and long short-term memory (LSTM) to achieve fast initial access times. Namely, the scheme predicts the best beam index for use in the next time step once a MS accesses the network, e.g., transition from sleep to active (or idle) modes. The scheme eliminates the need for beam scanning, thereby achieving ultra-low access times and energy efficiencies as compared to existing methods. |
| first_indexed | 2024-03-10T10:12:12Z |
| format | Article |
| id | doaj.art-f4d20a8edfc04d58aa67b55116f8d14d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T10:12:12Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f4d20a8edfc04d58aa67b55116f8d14d2023-11-22T01:08:44ZengMDPI AGElectronics2079-92922021-06-011013150510.3390/electronics10131505BRNN-LSTM for Initial Access in Millimeter Wave CommunicationsAdel Aldalbahi0Farzad Shahabi1Mohammed Jasim2Department of Electrical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi ArabiaDepartment of Electrical Engineering, School of Engineering, University of South Florida, Tampa, FL 33620, USASchool of Engineering, University of Mount Union, Alliance, OH 44601, USAThe use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial search to determine the best beam directions that return highest signal levels. The high number of signal measurements here prolongs access times and latencies, as well as increasing power and energy consumption. Hence this paper proposes a first study on leveraging deep learning schemes to simplify the beam access procedure in standalone mmWave networks. The proposed scheme combines bidirectional recurrent neural network (BRNN) and long short-term memory (LSTM) to achieve fast initial access times. Namely, the scheme predicts the best beam index for use in the next time step once a MS accesses the network, e.g., transition from sleep to active (or idle) modes. The scheme eliminates the need for beam scanning, thereby achieving ultra-low access times and energy efficiencies as compared to existing methods.https://www.mdpi.com/2079-9292/10/13/1505millimeter wavebeamforminginitial beam accessbidirectional recurrent neural networklong short-term memoryaccess times |
| spellingShingle | Adel Aldalbahi Farzad Shahabi Mohammed Jasim BRNN-LSTM for Initial Access in Millimeter Wave Communications Electronics millimeter wave beamforming initial beam access bidirectional recurrent neural network long short-term memory access times |
| title | BRNN-LSTM for Initial Access in Millimeter Wave Communications |
| title_full | BRNN-LSTM for Initial Access in Millimeter Wave Communications |
| title_fullStr | BRNN-LSTM for Initial Access in Millimeter Wave Communications |
| title_full_unstemmed | BRNN-LSTM for Initial Access in Millimeter Wave Communications |
| title_short | BRNN-LSTM for Initial Access in Millimeter Wave Communications |
| title_sort | brnn lstm for initial access in millimeter wave communications |
| topic | millimeter wave beamforming initial beam access bidirectional recurrent neural network long short-term memory access times |
| url | https://www.mdpi.com/2079-9292/10/13/1505 |
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