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학술대회 Deep-learning Based Adaptive Beam Management Technique for Mobile High-speed 5G mmWave Networks
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저자
나웅수, 배병준, 조숙희, 김나연
발행일
201909
출처
International Conference on Consumer Electronics (ICCE) 2019 : Berlin, pp.149-151
DOI
https://dx.doi.org/10.1109/ICCE-Berlin47944.2019.8966183
협약과제
19HR3500, 재난피해 저감을 위한 지상파 UHD기반 재난방송 서비스, 배병준
초록
5G core frequency band, mmWave, is about ten times wider than the existing commercial frequency band, enabling various services to be created. However, due to the characteristics of the mmWave frequency, it has various limitations. In the mobile environment, the misaligned beam problem, in which the SNR is degraded because the beam align between the sender and the receiver does not match, is one of the biggest problems to be solved. In this paper, we propose a adaptive beam management scheme based on deep-learning to solve misaligned beam problem. In the proposed scheme, 5G base-station (gNB) learns the mobility information, SNR, and current beam information of the associated user equipment (UE) by the deep-learning agent, and predicts whether the beam is aligned or not. From the prediction result, gNB and UE perform beam hands-off in advance before loss of connectivity.
키워드
5G mmWave, And Mobile networks, Beam management, Deep-learning
KSP 제안 키워드
High Speed, MmWave networks, Mobile networks, User equipment(UE), base station(BS), deep learning(DL), frequency band, learning agent, management scheme, mobile environment