Finding some QoS characteristics of self-similar traffic serviced by a mobile network

2017 ◽  
Author(s):  
Strelkovskaya Irina ◽  
Solovskaya Irina ◽  
Makoganiuk Anastasiya
Keyword(s):  
Mobile Network ◽  
Self Similar

scholarly journals Spline-Extrapolation Method in Traffic Forecasting in 5G Networks

2019 ◽  
Vol 3 ◽  
pp. 8-16 ◽  
Author(s):  
Irina Strelkovskaya ◽  
Irina Solovskaya ◽  
Anastasiya Makoganiuk
Keyword(s):  
Research Work ◽  
Mobile Network ◽  
Spline Functions ◽  
B Splines ◽  
Traffic Forecasting ◽  
Short And Long Term ◽  
Self Similar ◽  
Comparison Of The Results ◽  
Packet Data Transmission

This paper considers the problem of predicting self-similar traffic with a significant number of pulsations and the property of long-term dependence, using various spline functions. The research work focused on the process of modeling self-similar traffic handled in a mobile network. A splineextrapolation method based on various spline functions (linear, cubic and cubic B-splines) is proposed to predict selfsimilar traffic outside the period of time in which packet data transmission occurs. Extrapolation of traffic for short- and long-term forecasts is considered. Comparison of the results of the prediction of self-similar traffic using various spline functions has shown that the accuracy of the forecast can be improved through the use of cubic B-splines. The results allow to conclude that it is advisable to use spline extrapolation in predicting self-similar traffic, thereby recommending this method for use in practice in solving traffic prediction-related problems.

Adaptive Video Streaming Using Bandwidth Estimation for 3.5G Mobile Network

2009 ◽  
Vol E92-B (12) ◽  
pp. 3893-3902
Author(s):  
Hyeong-Min NAM ◽  
Chun-Su PARK ◽  
Seung-Won JUNG ◽  
Sung-Jea KO
Keyword(s):  
Video Streaming ◽  
Mobile Network ◽  
Bandwidth Estimation ◽  
Adaptive Video Streaming ◽  
Adaptive Video

Study of Ad hoc Networks with Reference to MANET, VANET, FANET

2017 ◽  
Vol 7 (7) ◽  
pp. 390 ◽  
Author(s):  
Bodhy Krishna .S
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc Network ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Mobile Network ◽  
Wireless Ad Hoc Network ◽  
Mobile Ad Hoc ◽  
Autonomous Nodes ◽  
Hoc Networks ◽  
Network Administration

A wireless ad hoc network is a decentralized type of wireless network. It is a type of temporary computer-to-computer connection. It is a spontaneous network which includes mobile ad-hoc networks (MANET), vehicular ad-hoc networks (VANET) and Flying ad-hoc networks (FANET). A MANET is a network that has many free or autonomous nodes often composed of mobile devices that can operate without strict top-down network administration [1]. A VANET is a sub form of MANET. It is a technology that uses vehicles as nodes in a network to create a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. This paper discusses the characteristics of these three ad-hoc networks.

SIMULASI NEURAL NETWORK UNTUK PREDIKSI LOKASI USER PADA MOBILE NETWORK

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Alexander Driyarkoro ◽  
Nurain Silalahi ◽  
Joko Haryatno
Keyword(s):  
Neural Network ◽  
Mobile Station ◽  
Mobile Network ◽  
Learning Rate ◽  
Noise Factor ◽  
Optimal Learning ◽  
Air Interface ◽  
Location Area ◽  
Optimal Learning Rate

Prediksi lokasi user pada mobile network merupakan hal sangat penting, karena routing panggilan pada mobile station (MS) bergantung pada posisi MS saat itu. Mobilitas MS yang cukup tinggi, terutama di daerah perkotaan, menyebabkan pencarian (tracking) MS akan berpengaruh pada kinerja sistem mobile network, khususnya dalam hal efisiensi kanal kontrol pada air interface. Salah satu bentuk pencarian adalah dengan mengetahui perilaku gerakan yang menentukan posisi MS. Dari MSC/VLR dapat diketahui posisi MS pada waktu tertentu. Karena location area suatu MS selalu unik dari waktu ke waktu, dan hal itu merupakan perilaku (behaviour) MS, maka dapat dibuat profil pergerakannya. Dengan menggunakan Neural Network (NN) akan diperoleh location area MS pada masa yang akan datang. Model NN yang digunakan pada penelitian ini adalah Propagasi Balik. Beberapa parameter NN yang diteliti dalam mempengaruhi kinerja prediksi lokasi user meliputi noise factor, momentum dan learning rate. Pada penelitian ini diperoleh nilai optimal learning rate = 0,5 dan noise factor = 1.

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