Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)

  • 2022.05a
  • /
  • Pages.517-519
  • /
  • 2022
  • /
  • 2005-0011(pISSN)
  • /
  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Sequence-to-Sequence based Mobile Trajectory Prediction Model in Wireless Network

무선 네트워크에서 시퀀스-투-시퀀스 기반 모바일 궤적 예측 모델

  • Bang, Sammy Yap Xiang (Dept. of Superintelligence, Sungkyunkwan University) ;
  • Yang, Huigyu (Dept. of Superintelligence, Sungkyunkwan University) ;
  • Raza, Syed M. (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Choo, Hyunseung (Department of Electrical and Computer Engineering, Sungkyunkwan University)
  • ;
  • 양희규 (성균관대학교 수퍼인텔리전스학과) ;
  • ;
  • 추현승 (성균관대학교 전자전기컴퓨터공학과)
  • Published : 2022.05.17
Download PDF
⟨ Previous Next ⟩

Abstract

In 5G network environment, proactive mobility management is essential as 5G mobile networks provide new services with ultra-low latency through dense deployment of small cells. The importance of a system that actively controls device handover is emerging and it is essential to predict mobile trajectory during handover. Sequence-to-sequence model is a kind of deep learning model where it converts sequences from one domain to sequences in another domain, and mainly used in natural language processing. In this paper, we developed a system for predicting mobile trajectory in a wireless network environment using sequence-to-sequence model. Handover speed can be increased by utilize our sequence-to-sequence model in actual mobile network environment.

Keywords

Acknowledgement

This work was partly supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience program (IITP-2022-2020-0-01821) and Grand Information Technology Research Center support program (IITP-2022-2015-0-00742) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation). This work was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2C2008447). This work was partly supported by the High-Potential Individuals Global Training Program (IITP-2021-0-02132).