| Paper Abstract and Keywords |
| Presentation |
2024-11-12 09:15
[Poster Presentation]
AI/ML-Based Multi-TRP/Beam Selection Method in Mixed Use Cases
Masaaki Ito, Issei Kanno, Hiroyuki Shinbo (KDDI Research) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
In Beyond 5G/6G mobile communication systems, the utilization of the mmWave (millimeter-wave) band, which offers wide bandwidth availability, is one of the promising frequency bands to deal with the increasing data traffic and the number of UEs (user equipments) in response to new use cases outlined in ITU-R IMT-2030. However, due to the propagation characteristics of mmWave, radio quality is prone to be degraded by blockage. Methods are used that leverage multiple TRPs (transmission and reception points) and assign beams with high signal power to UEs by each TRP to improve radio quality. Nonetheless, these conventional methods have the issue of decreasing resource utilization efficiency due to assignment of beams with excessive radio quality compared to the required communication quality. To address this issue, it is necessary to select TRPs and beams in such a way that the minimum required radio quality is achieved to satisfy the required communication quality of each UE. Considering real mmWave environments as described at the beginning, there are numerous beams and UEs, with variations in required communication quality, radio quality, and UE positions. Solving the optimization problem of selecting TRPs and beams based on these numerous factors within short cycles of around 100 ms is challenging. Therefore, the authors are exploring TRP and beam selection methods optimized through AI/ML (artificial intelligence/machine learning) based on the aforementioned multiple factors. This presentation introduces the method. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
AI/ML / beamforming / multi-connectivity / resource efficiency / / / / |
| Reference Info. |
IEICE Tech. Rep. |
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| Download PDF |
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| Conference Information |
| Committee |
RISING |
| Conference Date |
2024-11-11 - 2024-11-12 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Kaderu 2・7 (Sapporo) |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Cross-Field Research Association of Super-Intelligent Networking |
| Paper Information |
| Registration To |
RISING |
| Conference Code |
2024-11-RISING |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
AI/ML-Based Multi-TRP/Beam Selection Method in Mixed Use Cases |
| Sub Title (in English) |
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| Keyword(1) |
AI/ML |
| Keyword(2) |
beamforming |
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multi-connectivity |
| Keyword(4) |
resource efficiency |
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| 1st Author's Name |
Masaaki Ito |
| 1st Author's Affiliation |
KDDI Research, Inc. (KDDI Research) |
| 2nd Author's Name |
Issei Kanno |
| 2nd Author's Affiliation |
KDDI Research, Inc. (KDDI Research) |
| 3rd Author's Name |
Hiroyuki Shinbo |
| 3rd Author's Affiliation |
KDDI Research, Inc. (KDDI Research) |
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| Speaker |
Author-1 |
| Date Time |
2024-11-12 09:15:00 |
| Presentation Time |
50 minutes |
| Registration for |
RISING |
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| Volume (vol) |
vol. |
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