國立高雄大學圖資館 |

Language: English

Back

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Application of Fusion-Based Deep Learning Models to Improve Millimeter Wave Beamforming /Abishek Subramanian.
Author:	Subramanian, Abishek,
Description:	1 electronic resource (112 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Notes:	Advisors: Oliveira, Aurenice M. Committee members: Dukka, K. C.; Pinar, Anthony.
Contained By:	Masters Abstracts International85-11.
Subject:	Electrical engineering.
Online resource:	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 /
LDR:02419nmm a22004333i 4500 001 676300
005 20250420214338.5
006 m o d
007 cr|nu||||||||
008 250901s2024 miu||||||m |||||||eng d
020 $a 9798382598437
035 $a (MiAaPQD)AAI31149417
035 $a AAI31149417
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Subramanian, Abishek, $e author. $3 990059
245 1 0 $a Application of Fusion-Based Deep Learning Models to Improve Millimeter Wave Beamforming / $c Abishek Subramanian.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (112 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-11.
500 $a Advisors: Oliveira, Aurenice M. Committee members: Dukka, K. C.; Pinar, Anthony.
502 $b M.S. $c Michigan Technological University $d 2024.
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.
546 $a English
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
653 $a Beamforming
653 $a Federated learning
653 $a Fusion based deep learning
653 $a Millimeter Wave
653 $a Vehicle-to-infrastructure
690 $a 0544
690 $a 0464
690 $a 0540
710 2 $a Michigan Technological University. $b Electrical & Computer Engineering. $e degree granting institution. $3 990060
720 1 $a Oliveira, Aurenice M. $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-11.
790 $a 0129
791 $a M.S.
792 $a 2024
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31149417