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

Min Soo Sim, Yeon Geun Lim, Sang Hyun Park, Linglong Dai, Chan Byoung Chae

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)


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 %.

Original languageEnglish
Article number9034044
Pages (from-to)51634-51646
Number of pages13
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) under Grant 2019-0-00685 and Grant 2018-0-00170, and in part by the National Natural Science Foundation of China for Outstanding Young Scholars under Grant 61722109.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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