An approach using support vector regression for mobile location in cellular networks

被引：15

作者：

Timoteo, Robson D. A. ^{[1
]}

Silva, Lizandro N. ^{[2
]}

Cunha, Daniel C. ^{[1
]}

Cavalcanti, George D. C. ^{[1
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat, Av Jornalista Anibal Fernandes S-N, BR-50740560 Recife, PE, Brazil

[2] Telefon Vivo, Ave Engn Domingos Ferreira 837, BR-51011051 Recife, PE, Brazil

来源：

COMPUTER NETWORKS | 2016年 / 95卷

关键词：

Wireless communications; Positioning system; Fingerprinting techniques; Machine learning; Support vector regression; LOCALIZATION;

D O I：

10.1016/j.comnet.2015.12.003

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Wireless positioning systems have become very popular in recent years. One of the reasons is the fact that the use of a new paradigm named Internet of Things has been increasing in the scenario of wireless communications. Since a high demand for accurate positioning in wireless networks has become more intensive, especially for location-based services, the investigation of mobile positioning using radiolocalization techniques is an open research problem. Based on this context, we propose a fingerprinting approach using support vector regression to estimate the position of a mobile terminal in cellular networks. Simulation results indicate the proposed technique has a lower error distance prediction and is less sensitive to a Rayleigh distributed noise than the fingerprinting techniques based on COST-231 and ECC-33 propagation models. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：51 / 61

页数：11

共 50 条

[31] Overhead Line Fault Location using Wavelet Packet Decomposition and Support Vector Regression
Fei, Chunguo
Su, Baili
ELECTRICAL POWER & ENERGY SYSTEMS, PTS 1 AND 2, 2012, 516-517 : 1396 - +
[32] Fault Location in an Unbalanced Distribution System using Support Vector Classification and Regression Analysis
Gururajapathy, Sophi Shilpa
Mokhlis, Hazlie
Bin Illias, Hazlee Azil
Abu Bakar, Ab Halim
Awalin, Lilik Jamilatul
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2018, 13 (02) : 237 - 245
[33] Using Support Vector Regression for Classification
Huang, Bo
Cai, Zhihua
Gu, Qiong
Chen, Changjun
ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS, 2008, 5139 : 581 - 588
[34] On a new approach for Lagrangian support vector regression
S. Balasundaram
Gagandeep Benipal
Neural Computing and Applications, 2018, 29 : 533 - 551
[35] On a new approach for Lagrangian support vector regression
Balasundaram, S.
Benipal, Gagandeep
NEURAL COMPUTING & APPLICATIONS, 2018, 29 (09): : 533 - 551
[36] A novel approach for the hyperparameters of support vector regression
Jeng, JT
Chuang, CC
PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 642 - 647
[37] An approach to support vector regression with Genetic Algorithms
Herrera, Oscar
Kuri, Angel
MICAI 2006: FIFTH MEXICAN INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, : 178 - +
[38] Variational Bayesian approach to support vector regression
Gao, Z
Wong, KYM
PROGRESS OF THEORETICAL PHYSICS SUPPLEMENT, 2005, (157): : 284 - 287
[39] Hierarchical Approach for Multiscale Support Vector Regression
Bellocchio, Francesco
Ferrari, Stefano
Piuri, Vincenzo
Borghese, Nunzio Alberto
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2012, 23 (09) : 1448 - 1460
[40] Nepal Stock Exchange Prediction Using Support Vector Regression and Neural Networks
Pun, Top Bahadur
Shahi, Tej Bahadur
2018 SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS, COMPUTERS AND COMMUNICATIONS (ICAECC), 2018,

← 1 2 3 4 5 →