A matrix-completion approach to mobile network localization

2014 ◽  
Author(s):  
Qiang Ye ◽  
Jie Cheng ◽  
Hongwei Du ◽  
Xiaohua Jia ◽  
Jing Zhang
Keyword(s):  
Matrix Completion ◽  
Mobile Network ◽  
Network Localization

scholarly journals Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Inam Ullah ◽  
Xin Su ◽  
Jinxiu Zhu ◽  
Xuewu Zhang ◽  
Dongmin Choi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Mobile Network ◽  
High Accuracy ◽  
Robot Localization ◽  
Network Localization ◽  
Mobile Robot Localization

Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

scholarly journals Millimeter-Wave Channel Estimation using Alternating Direction Method of Multipliers

2021 ◽  
Vol 10 (4) ◽  
pp. 239-242
Author(s):  
Aarab Mohamed Nassim ◽  
Chakkor Otman
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Mobile Networks ◽  
Mimo Systems ◽  
Matrix Completion ◽  
Mobile Network ◽  
Alternating Direction Method ◽  
Low Rank ◽  
Wave Channel ◽  
Alternating Direction

With the explosive growth in demand for mobile data traffic, the contradiction between capacity requirements and spectrum scarcity becomes more and more prominent. The bandwidth is becoming a key issue in 5G mobile networks. However, with the huge bandwidth from 30 GHz to 300 GHz, mmWave communications considered an important part of the 5G mobile network providing multi communication services, where channel state information considers a challenging task for millimeter wave MIMO systems due to the huge number of antennas. Therefore, this paper discusses the channel and signal models of the mmWave, with a novel formulation for mmWave channel estimation inclusive low rank features, that we improved using a developed theory of matrix completion with Alternating Direction Method.

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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