5G mmWave Multiple Active Protocol Stack Optimized Deep Learning Handover

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

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Pages (from-to)2265-2269
Number of pages5
JournalIEEE Wireless Communications Letters
Issue number11
Publication statusPublished - 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


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