Learning-based beam training algorithms for ieee802.11ad/ay networks

Ting Wei Chang, Li Hsiang Shen, Kai-Ten Feng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Recently, many researches are focusing on millimeter wave (mmWave) due to its large bandwidth resources. In the next 5G generation, wireless multi- gigabit (WiGig) is developed based on IEEE 802.11ad/ay standards at unlicensed mmWave bands for providing extremely high throughput. However, mmWave has high propagation loss due to high frequency transmission properties. Beamforming (BF) technique is adopted to solve this problem, and therefore, we have to perform beam training before data transmission. In the standard, the conventional method for beam training is exhaustive beam search (EBS) which takes too much time so that the data transmission time will decrease. On the other hand, blocked environments may severely degrade WiGig beam training performance, and most of existing algorithms do not consider this issue. Recently, machine and deep learning have been widely used in the wireless communication field. We propose a learning-based beam training (LBT) to simultaneously learn about wireless environments and beam training candidates. We select simplified neural network (NN) model to achieve lower computation overhead. To further refine learning information, we propose two enhanced algorithm, expanded LBT (LBT-E) and history-aided expanded LBT(LBT-HE). LBT-E aims to tackle unexpected slight deviation and LBT- HE make use of historical information to improve beam matching accuracy with acceptable latency. In our simulation, our proposed learningbased schemes achieve much higher throughput compared to EBS and algorithms in existing literatures.

Original languageEnglish
Title of host publication2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728112176
StatePublished - 1 Apr 2019
Event89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia
Duration: 28 Apr 20191 May 2019

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference89th IEEE Vehicular Technology Conference, VTC Spring 2019
CityKuala Lumpur

Fingerprint Dive into the research topics of 'Learning-based beam training algorithms for ieee802.11ad/ay networks'. Together they form a unique fingerprint.

Cite this