A Deep Learning-based Low Overhead Beam Selection in mmWave Communications

Haruhi Echigo, Yuwen Cao, Mondher Bouazizi, Tomoaki Ohtsuki

Research output: Contribution to journalArticlepeer-review


Due to large amounts of available spectrum at high frequencies, millimeter-wave (mmWave) technology has gained significant interest in 5G communications, but mmWave links suffer from sever free space attenuation. Codebook-based beamforming techniques with multiple antennas can effectively alleviate this challenge with low computational complexity and low cost hardware. However, small delay and high-speed communications with beamforming techniques require beam alignment with small overhead to establish the wireless link quickly. In this paper, we propose a deep learning-based low overhead analog beam selection scheme inspired by super-resolution. Deep neural networks are employed to conduct beam quality estimation based on partial beam measurements. Our proposed scheme can cover all the directions of arriving signals with low overhead by utilizing codebooks with different beam widths. Also, for the purpose of further reducing overhead, we build the model predicting beam qualities based on the past beam sweepings. With these beam quality estimation and prediction model, the beam that achieves large signal-to-noise-power-ratio (SNR) can be selected based on partial beam measurements. Simulation results show that the proposed scheme can accurately estimate beam qualities and give high probability of optimal beam selections with low overhead.

Original languageEnglish
JournalIEEE Transactions on Vehicular Technology
Publication statusAccepted/In press - 2021


  • Antenna measurements
  • Array signal processing
  • beam selection
  • beamforming
  • Convolutional LSTM
  • Deep learning
  • Deep learning
  • Millimeter wave communication
  • Predictive models
  • super-resolution
  • Training
  • Wireless communication

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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