Design and Implementation for Deep Learning-based Adjustable Beamforming Training for Millimeter Wave Communication Systems

Li Hsiang Shen, Ting Wei Chang, Kai-Ten Feng, Po-Tsang Huang

Research output: Contribution to journalArticlepeer-review


Millimeter wave (mmWave) provides extremely high throughput owing to their high bandwidth utilization over higher frequencies. To compensate for the severe loss and attenuation, beamforming training is used to determine the optimal beam direction and thereby improve directional transmission power. However, the training overhead will be significantly increased with narrower beams, particularly under conventional exhaustive schemes. Therefore, we propose a learning-based adjustable beam number training (LABNT) scheme to intelligently and flexibly select beam training candidates considering different user mobility and hybrid line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. In LABNT, several deep learning networks are parallelly constructed and connected to a reinforcement learning network to determine training candidates dynamically based on performance rewards. A novel enhanced feature selection method, that is, uniformly distributed mutual information, is developed based on the correlation of historical results to select the beam inputs for each deep learning network. In simulations, the proposed LABNT outperforms other existing schemes in terms of beam alignment accuracy, training latency, and system throughput. Moreover, following the IEEE 802.11ad/ay protocols, we implement realistic mmWave beamforming training on a programmable hardware platform with integrated 60 GHz wireless-gigabit (WiGig) transceiver devices. The experimental results on the WiGig platform demonstrate that the proposed LABNT scheme can achieve real-time performance with approximately Gbps transmission throughput and millisecond-level training overhead.

Original languageEnglish
Pages (from-to)2413-2427
Number of pages15
JournalIEEE Transactions on Vehicular Technology
StatePublished - Mar 2021


  • Array signal processing
  • beamforming training
  • deep learning
  • IEEE 802.11 Standard
  • Millimeter wave
  • Reinforcement learning
  • reinforcement learning
  • Signal to noise ratio
  • Structural beams
  • Throughput
  • Training
  • WiGig platform implementation
  • wireless local area networks


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