A SON Function for Steering Users in Multi-Layer LTE Networks Based on Their Mobility Behaviour

被引:0
|
作者
Sas, Bart [1 ]
Spaey, Kathleen [1 ]
Blondia, Chris [1 ]
机构
[1] Univ Antwerp, iMinds, B-2020 Antwerp, Belgium
关键词
Self-Organising Network (SON); Traffic Steering; Long-Term Evolution (LTE); Multi-layer; High Mobility;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
In cellular networks, users that make frequent handovers and have a low time-of-stay in a cell (i.e., highly mobile users) might have a negative impact on the network performance. Furthermore the Quality of Service (QoS) experienced by these users might be low. This paper introduces a Self-Organising Network (SON) function, called the High Mobility SON function, that aims at reducing the amount of short stays in a multi-layer Long-Term Evolution (LTE) network. It does this by predicting the mobility behaviour of currently active users based on measurements, which were collected by users that were active in the past. Based on these predictions, the SON function aims at refraining from handovers to cells in which the user is likely to stay for a small amount of time, and at steering the user more appropriately. To assess the ability of the SON function to achieve its goals, simulations were performed in a scenario in which both macro and micro cells are deployed. Results show that the developed SON function is able to reduce the number of handovers by 17-23% and the number of short stays by as much as 43-49% at the cost of reducing the spectral efficiency by 13-15%.
引用
收藏
页数:7
相关论文
共 50 条
  • [31] Multi-layer Monitoring of Overlay Networks
    Demirci, Mehmet
    Lo, Samantha
    Seetharaman, Srini
    Ammar, Mostafa
    PASSIVE AND ACTIVE NETWORK MEASUREMENT, PROCEEDINGS, 2009, 5448 : 77 - +
  • [32] Open and Disaggregated Multi-Layer Networks
    De Leenheer, M.
    Koshibe, A.
    Higuchi, Y.
    Shiota, N.
    Wu, H.
    Furusawa, T.
    Tofigh, T.
    Parulkar, G.
    2017 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION (OFC), 2017,
  • [33] Traffic engineering for multi-layer networks
    Jajszczyk, Andrzej
    Mukherjee, Biswanath
    Sabella, Roberto
    Xiao, XiPeng
    Zukerman, M.
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2007, 25 (05) : 865 - 867
  • [34] Identification of communities in urban mobility networks using multi-layer graphs of network traffic
    Yildirimoglu, Mehmet
    Kim, Jiwon
    20TH EURO WORKING GROUP ON TRANSPORTATION MEETING, EWGT 2017, 2017, 27 : 1034 - 1041
  • [35] Identification of communities in urban mobility networks using multi-layer graphs of network traffic
    Yildirimoglu, Mehmet
    Kim, Jiwon
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2018, 89 : 254 - 267
  • [36] Design of a multi-layer beam-steering WLAN antenna
    Yilmaz, Kardelen
    Nesimoglu, Tayfun
    2018 18TH MEDITERRANEAN MICROWAVE SYMPOSIUM (MMS), 2018, : 26 - 31
  • [37] Modeling multi-type information propagation based on multi-layer networks
    Chen, Libin
    Feng, Yuan
    Zeng, Chengyi
    Liu, Hongfu
    Chen, Jing
    2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 781 - 787
  • [38] The layer effect on multi-layer cellular neural networks
    Ban, Jung-Chao
    Chang, Chih-Hung
    APPLIED MATHEMATICS LETTERS, 2013, 26 (07) : 706 - 709
  • [39] Multi-layer Network Coding for Multiuser Relay Networks With Non-Uniform-Rate Users
    Peng, Chunling
    Li, Fangwei
    Liu, Huaping
    2016 IEEE 84TH VEHICULAR TECHNOLOGY CONFERENCE (VTC FALL), 2016,
  • [40] Activation Function Integration for Accelerating Multi-Layer Graph Convolutional Neural Networks
    Grailoo, Mahdieh
    Nikoubin, Tooraj
    Gustafsson, Oscar
    Nunez-Yanez, Jose
    17TH IEEE DALLAS CIRCUITS AND SYSTEMS CONFERENCE, DCAS 2024, 2024,