Deep Learning-Based mmWave Beam Selection for 5G NR/6G with Sub-6 GHz Channel Information: Algorithms and Prototype Validation

In fifth-generation (5G) communications, millimeter wave (mmWave) is one of the key technologies to increase the data rate. To overcome this technology's poor propagation characteristics, it is necessary to employ a number of antennas and form narrow beams. It becomes crucial then, especially f...

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Published in:	IEEE access Vol. 8; p. 1
Main Authors:	Sim, Min Soo, Lim, Yeon-Geun, Park, Sang Hyun, Dai, Linglong, Chae, Chan-Byoung
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	5G NR Algorithms and ultra-low latency Artificial neural networks beam selection beamforming Deep learning Machine learning Millimeter waves mmWave Prototypes Ray tracing Sweeping
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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Abstract	In fifth-generation (5G) communications, millimeter wave (mmWave) is one of the key technologies to increase the data rate. To overcome this technology's poor propagation characteristics, it is necessary to employ a number of antennas and form narrow beams. It becomes crucial then, especially for initial access, to attain fine beam alignment between a next generation NodeB (gNB) and a user equipment (UE). The current 5G New Radio (NR) standard, however, adopts an exhaustive search-based beam sweeping, which causes time overhead of a half frame for initial beam establishment. In this paper, we propose a deep learning-based beam selection, which is compatible with the 5G NR standard. To select a mmWave beam, we exploit sub-6 GHz channel information. We introduce a deep neural network (DNN) structure and explain how we estimate a power delay profile (PDP) of a sub-6 GHz channel, which is used as an input of the DNN. We then validate its performance with real environment-based 3D ray-tracing simulations and over-the-air experiments with a mmWave prototype. Evaluation results confirm that, with support from the sub-6 GHz connection, the proposed beam selection reduces the beam sweeping overhead by up to 79.3 %.
AbstractList	In fifth-generation (5G) communications, millimeter wave (mmWave) is one of the key technologies to increase the data rate. To overcome this technology's poor propagation characteristics, it is necessary to employ a number of antennas and form narrow beams. It becomes crucial then, especially for initial access, to attain fine beam alignment between a next generation NodeB (gNB) and a user equipment (UE). The current 5G New Radio (NR) standard, however, adopts an exhaustive search-based beam sweeping, which causes time overhead of a half frame for initial beam establishment. In this paper, we propose a deep learning-based beam selection, which is compatible with the 5G NR standard. To select a mmWave beam, we exploit sub-6 GHz channel information. We introduce a deep neural network (DNN) structure and explain how we estimate a power delay profile (PDP) of a sub-6 GHz channel, which is used as an input of the DNN. We then validate its performance with real environment-based 3D ray-tracing simulations and over-the-air experiments with a mmWave prototype. Evaluation results confirm that, with support from the sub-6 GHz connection, the proposed beam selection reduces the beam sweeping overhead by up to 79.3 %.
Author	Dai, Linglong Sim, Min Soo Park, Sang Hyun Lim, Yeon-Geun Chae, Chan-Byoung
Author_xml	– sequence: 1 givenname: Min Soo surname: Sim fullname: Sim, Min Soo organization: School of Integrated Technology, Yonsei University, Korea – sequence: 2 givenname: Yeon-Geun surname: Lim fullname: Lim, Yeon-Geun organization: Samsung Electronics, Korea – sequence: 3 givenname: Sang Hyun surname: Park fullname: Park, Sang Hyun organization: School of Integrated Technology, Yonsei University, Korea – sequence: 4 givenname: Linglong surname: Dai fullname: Dai, Linglong organization: Department of Electronic Engineering, Tsinghua University, China – sequence: 5 givenname: Chan-Byoung surname: Chae fullname: Chae, Chan-Byoung organization: School of Integrated Technology, Yonsei University, Korea. (e-mail: cbchae@yonsei.ac.kr)
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SubjectTerms	5G NR Algorithms and ultra-low latency Artificial neural networks beam selection beamforming Deep learning Machine learning Millimeter waves mmWave Prototypes Ray tracing Sweeping
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Title	Deep Learning-Based mmWave Beam Selection for 5G NR/6G with Sub-6 GHz Channel Information: Algorithms and Prototype Validation
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