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Application of Fusion-Based Deep Learning Models to Improve Millimeter Wave Beamforming /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Application of Fusion-Based Deep Learning Models to Improve Millimeter Wave Beamforming /Abishek Subramanian.
作者:	Subramanian, Abishek,
面頁冊數:	1 electronic resource (112 pages)
附註:	Source: Masters Abstracts International, Volume: 85-11.
附註:	Advisors: Oliveira, Aurenice M. Committee members: Dukka, K. C.; Pinar, Anthony.
Contained By:	Masters Abstracts International85-11.
標題:	Electrical engineering.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31149417
ISBN:	9798382598437

Application of Fusion-Based Deep Learning Models to Improve Millimeter Wave Beamforming /
Subramanian, Abishek,

Application of Fusion-Based Deep Learning Models to Improve Millimeter Wave Beamforming /Abishek Subramanian. - 1 electronic resource (112 pages)

Source: Masters Abstracts International, Volume: 85-11.

This study addresses the challenge of selecting millimeter Wave (mmWave) beamforming pairs for vehicle-to-infrastructure (V2I) communication, to mitigate latency in highly dynamic vehicular environments. We investigate the use of out-of-band sensor data as side information to model mmWave ray tracing paths and predicting a subset of top-K optimal beamforming pairs for efficient and low-latency searches. Unimodal-Fusion Deep Learning (F-DL) networks was applied to enhance mmWave beamforming process. We started by first investigating the centralized architecture, and then explored a novel distributed architecture through federated learning to minimize resource and latency overheads. The distributed architecture incorporates two biased client selection strategies: MaxLoss and heuristic Multi-Armed Bandit (MAB). This innovative approach streamlines beam selection, improving scalability, robustness and dynamic adaptability.

English

ISBN: 9798382598437Subjects--Topical Terms:

454503
Electrical engineering.
Subjects--Index Terms:

Beamforming

Application of Fusion-Based Deep Learning Models to Improve Millimeter Wave Beamforming /
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520 $a This study addresses the challenge of selecting millimeter Wave (mmWave) beamforming pairs for vehicle-to-infrastructure (V2I) communication, to mitigate latency in highly dynamic vehicular environments. We investigate the use of out-of-band sensor data as side information to model mmWave ray tracing paths and predicting a subset of top-K optimal beamforming pairs for efficient and low-latency searches. Unimodal-Fusion Deep Learning (F-DL) networks was applied to enhance mmWave beamforming process. We started by first investigating the centralized architecture, and then explored a novel distributed architecture through federated learning to minimize resource and latency overheads. The distributed architecture incorporates two biased client selection strategies: MaxLoss and heuristic Multi-Armed Bandit (MAB). This innovative approach streamlines beam selection, improving scalability, robustness and dynamic adaptability.
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590 $a School code: 0129
650 4 $a Electrical engineering. $3 454503
650 4 $a Computer engineering. $3 212944
650 4 $a Automotive engineering. $3 739048
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653 $a Federated learning
653 $a Fusion based deep learning
653 $a Millimeter Wave
653 $a Vehicle-to-infrastructure
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690 $a 0540
710 2 $a Michigan Technological University. $b Electrical & Computer Engineering. $e degree granting institution. $3 990060
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