BROWSE
Detail
Conference Paper
Deep-learning Based Adaptive Beam Management Technique for Mobile High-speed 5G mmWave Networks
Cited 8 time in 
Download 3 time
Share 
Download 3 time
Share
- Authors
- Woongsoo Na, Byungjun Bae, Sukhee Cho, Nayeon Kim
- Issue Date
- 2019-09
- Citation
- International Conference on Consumer Electronics (ICCE) 2019 : Berlin, pp.149-151
- Language
- English
- Type
- Conference Paper
- Project Code
- 19HR3500, Terrestrial UHD-based disaster broadcasting service for mitigation of disaster damage, Byungjun Bae
- Abstract
- 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.
- KSP Keywords
- High Speed, MmWave networks, User equipment(UE), base station(BS), deep learning(DL), frequency band, learning agent, management scheme, mobile environment