講演抄録/キーワード |
講演名 |
2018-08-10 15:45
Blockage Aware Beam Allocation in mmWave Using Reinforcement Learning ○Yuva Kumar S.・Fereidoun H. Panahi・Tomoaki Ohtsuki(Keio Univ.) RCS2018-149 |
抄録 |
(和) |
With the advent of the fifth generation (5G) systems, there is an increasing demand for high data rate transmission and serving the increased proliferation of mobile and connected devices. This, in turn, has seen spectrum crunch to serve the demand. One major solution is to move towards millimeter-wave (mmWave) frequencies due to its large spectral bandwidth. However, mmWave frequencies experience large propagation path loss and are very sensitive to blockages like human bodies and buildings. This gives rise to unstable connectivity and unreliable communication. In this report, we propose a Blockage Aware Beam Allocation (BABA) for a typical user equipment (UE) in mmWave networks to reduce the number of blockages experienced by the UE. We use reinforcement learning (RL), where the base station (BS) would be updated with the available and possible blockages for a UE. We consider a mmWave network where base stations (BSs) are distributed in the landscape such that it satisfies path diversity for the UE at any given point, i.e., each UE will be served by secondary beam once the UE experiences blockage in the initial beam. Investigation of the performance of the BABA shows better performance in terms of the number of blockages experienced by the UEs than the mmWave network without RL. |
(英) |
With the advent of the fifth generation (5G) systems, there is an increasing demand for high data rate transmission and serving the increased proliferation of mobile and connected devices. This, in turn, has seen spectrum crunch to serve the demand. One major solution is to move towards millimeter-wave (mmWave) frequencies due to its large spectral bandwidth. However, mmWave frequencies experience large propagation path loss and are very sensitive to blockages like human bodies and buildings. This gives rise to unstable connectivity and unreliable communication. In this report, we propose a Blockage Aware Beam Allocation (BABA) for a typical user equipment (UE) in mmWave networks to reduce the number of blockages experienced by the UE. We use reinforcement learning (RL), where the base station (BS) would be updated with the available and possible blockages for a UE. We consider a mmWave network where base stations (BSs) are distributed in the landscape such that it satisfies path diversity for the UE at any given point, i.e., each UE will be served by secondary beam once the UE experiences blockage in the initial beam. Investigation of the performance of the BABA shows better performance in terms of the number of blockages experienced by the UEs than the mmWave network without RL. |
キーワード |
(和) |
Millimeter-wave communications / blockage probability / stochastic geometry / reinforcement learning / / / / |
(英) |
Millimeter-wave communications / blockage probability / stochastic geometry / reinforcement learning / / / / |
文献情報 |
信学技報, vol. 118, no. 177, RCS2018-149, pp. 99-104, 2018年8月. |
資料番号 |
RCS2018-149 |
発行日 |
2018-08-02 (RCS) |
ISSN |
Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
RCS2018-149 |
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