5G mmWave Multiple Active Protocol Stack Optimized Deep Learning Handover

Jungsuk Baik; Changsung Lee; Jaewook Jung; Jong Moon Chung

doi:10.1109/LWC.2022.3199042

5G mmWave Multiple Active Protocol Stack Optimized Deep Learning Handover

Jungsuk Baik, Changsung Lee, Jaewook Jung, Jong Moon Chung

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

To maintain seamless real-time connectivity for 5G applications, various handover (HO) schemes that can support a near-zero mobility interruption time (MIT) have been proposed. Due to the high frequency of mmWave signals, line-of-sight (LOS) is required for communication, because significant degradation and HO failure (HOF) occurs in non-LOS (NLOS) situations. In this letter, a novel deep learning based multiple active protocol stack (MAPS) optimized HO scheme is proposed to reduce the outage rate during the HO process and decrease the MIT. The performance evaluation results show that a much lower MIT and outage rate can be obtained when using the proposed MAPS HO scheme, compared to other HO techniques.

Original language	English
Pages (from-to)	2265-2269
Number of pages	5
Journal	IEEE Wireless Communications Letters
Volume	11
Issue number	11
DOIs	https://doi.org/10.1109/LWC.2022.3199042
Publication status	Published - 2022 Nov 1

Bibliographical note

Funding Information:
This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) Grant funded by the Ministry of Land, Infrastructure and Transport under Grant RS-2022-00143782 of the Republic of Korea.

Publisher Copyright:
© 2012 IEEE.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/LWC.2022.3199042

Cite this

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title = "5G mmWave Multiple Active Protocol Stack Optimized Deep Learning Handover",

abstract = "To maintain seamless real-time connectivity for 5G applications, various handover (HO) schemes that can support a near-zero mobility interruption time (MIT) have been proposed. Due to the high frequency of mmWave signals, line-of-sight (LOS) is required for communication, because significant degradation and HO failure (HOF) occurs in non-LOS (NLOS) situations. In this letter, a novel deep learning based multiple active protocol stack (MAPS) optimized HO scheme is proposed to reduce the outage rate during the HO process and decrease the MIT. The performance evaluation results show that a much lower MIT and outage rate can be obtained when using the proposed MAPS HO scheme, compared to other HO techniques.",

author = "Jungsuk Baik and Changsung Lee and Jaewook Jung and Chung, {Jong Moon}",

note = "Funding Information: This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) Grant funded by the Ministry of Land, Infrastructure and Transport under Grant RS-2022-00143782 of the Republic of Korea. Publisher Copyright: {\textcopyright} 2012 IEEE.",

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doi = "10.1109/LWC.2022.3199042",

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N1 - Funding Information: This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) Grant funded by the Ministry of Land, Infrastructure and Transport under Grant RS-2022-00143782 of the Republic of Korea. Publisher Copyright: © 2012 IEEE.

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5G mmWave Multiple Active Protocol Stack Optimized Deep Learning Handover

Abstract

Bibliographical note

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