Journal
IEEE ACCESS
Volume 8, Issue -, Pages 51634-51646Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2980285
Keywords
5G mobile communication; Hidden Markov models; Channel models; Machine learning; Delays; Antenna arrays; Array signal processing; 5G NR; 6G; mmWave; beamforming; deep learning; beam selection; ultra-low latency
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Funding
- Institute of Information and Communications Technology Planning and Evaluation (IITP) - Korea Government (MSIT) [2019-0-00685, 2018-0-00170]
- National Natural Science Foundation of China for Outstanding Young Scholars [61722109]
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In fifth-generation (5G) communications, millimeter wave (mmWave) is one of the key technologies to increase the data rate. To overcome this technology & x2019;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 & x0025;.
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