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dc.contributor.authorChang, Ting-Weien_US
dc.contributor.authorShen, Li-Hsiangen_US
dc.contributor.authorFeng, Kai-Tenen_US
dc.date.accessioned2019-10-05T00:09:45Z-
dc.date.available2019-10-05T00:09:45Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-1217-6en_US
dc.identifier.issn1550-2252en_US
dc.identifier.urihttp://hdl.handle.net/11536/152942-
dc.description.abstractRecently, 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 learning-based schemes achieve much higher throughput compared to EBS and algorithms in existing literatures.en_US
dc.language.isoen_USen_US
dc.titleLearning-based Beam Training Algorithms for IEEE 802.11ad/ay Networksen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 IEEE 89TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2019-SPRING)en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000482655600090en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper