A demonstration of mobility prediction as a service in cloudified LTE networks

2015 ◽  
Author(s):  
Zhongliang Zhao ◽  
Morteza Karimzadeh ◽  
Torsten Braun ◽  
Aiko Pras ◽  
Hans van den Berg
Keyword(s):  
Mobility Prediction ◽  
Lte Networks

scholarly journals Tracking Area Boundary-aware Protocol for Pseudo Stochastic Mobility Prediction in LTE Networks

2020 ◽  
Vol 12 (5) ◽  
pp. 52-62
Author(s):  
Vincent Omollo Nyangaresi ◽  
◽  
Silvance O. Abeka ◽  
Anthony J. Rodrigues
Keyword(s):  
Random Walk ◽  
Prediction Models ◽  
Mobile Station ◽  
Mobility Model ◽  
Path Model ◽  
Mobility Prediction ◽  
Order Kinetic ◽  
Lte Networks ◽  
Random Direction ◽  
Random Waypoint

Accurate mobility prediction enables efficient and faster paging services in these networks. This in turn facilitates the attainment of higher bandwidths and execution of activities such as handovers at low latencies. The conventional mobility prediction models operate on unrealistic assumptions that make them unsuitable for cellular network mobile station tracking. For instance, the Feynman-Verlet, first order kinetic model and Random Waypoint assume that mobile phones move with constant velocity while Manhattan, Freeway, city area, street unit, obstacle mobility, and pathway mobility postulate that mobile station movement is restricted along certain paths. In addition, obstacle mobility model speculate that the mobile station signal is completely absorbed by an obstacle while random walk, random waypoint, Markovian random walk, random direction, shortest path model, normal walk, and smooth random assume that a mobile station can move in any direction. Moreover, the greatest challenge of the random direction model is the requirement that a border behavior model be specified for the reaction of mobile stations reaching the simulation area boundary. In this paper, a protocol that addresses the border behavior problem is developed. This protocol is shown to detect when the subscriber has moved out of the current tracking area, which is crucial during handovers.

Femtocell Selection Scheme for Reducing Unnecessary Handover and Enhancing Downlink QoS in Cognitive Femtocell Networks

2017 ◽  
Author(s):  
Hoang Nhu Dong ◽  
Hoang Nam Nguyen ◽  
Hoang Trong Minh ◽  
Takahiko Saba
Keyword(s):  
Cellular Networks ◽  
Estimation Methods ◽  
Mobility Prediction ◽  
Capacity Estimation ◽  
Femtocell Networks ◽  
Selection Scheme ◽  
Indoor Communications ◽  
Performance Results ◽  
Cognitive Femtocell

Femtocell networks have been proposed for indoor communications as the extension of cellular networks for enhancing coverage performance. Because femtocells have small coverage radius, typically from 15 to 30 meters, a femtocell user (FU) walking at low speed can still make several femtocell-to-femtocell handovers during its connection. When performing a femtocell-to-femtocell handover, femtocell selection used to select the target handover femtocell has to be able not only to reduce unnecessary handovers and but also to support FU’s quality of service (QoS). In the paper, we propose a femtocell selection scheme for femtocell-tofemtocell handover, named Mobility Prediction and Capacity Estimation based scheme (MPCE-based scheme), which has the advantages of the mobility prediction and femtocell’s available capacity estimation methods. Performance results obtained by computer simulation show that the proposed MPCE-based scheme can reduce unnecessary femtocell-tofemtocell handovers, maintain low data delay and improve the throughput of femtocell users. DOI: 10.32913/rd-ict.vol3.no14.536

A study of X2 and S1 Handover in LTE Networks using MATLAB

2018 ◽  
Vol 6 (5) ◽  
pp. 1118-1122
Author(s):  
B. Singh ◽  
◽  
◽  
M. Yadav
Keyword(s):  
Lte Networks

scholarly journals Dynamic Public Resource Allocation Based on Human Mobility Prediction

2020 ◽  
Vol 4 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Sijie Ruan ◽  
Jie Bao ◽  
Yuxuan Liang ◽  
Ruiyuan Li ◽  
Tianfu He ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Human Mobility ◽  
Mobility Prediction ◽  
Public Resource
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