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Determination of Wireless Networks Parameters through Parallel Hierarchical Support Vector Machines
March 2012 (vol. 23 no. 3)
pp. 505-512
ASCII Text
x 
 
Vin-sen Feng, Shih Yu Chang, "Determination of Wireless Networks Parameters through Parallel Hierarchical Support Vector Machines," IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 3, pp. 505-512, March, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TPDS.2011.156,
author = {Vin-sen Feng and Shih Yu Chang},
title = {Determination of Wireless Networks Parameters through Parallel Hierarchical Support Vector Machines},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {23},
number = {3},
issn = {1045-9219},
year = {2012},
pages = {505-512},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2011.156},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - Determination of Wireless Networks Parameters through Parallel Hierarchical Support Vector Machines
IS - 3
SN - 1045-9219
SP505
EP512
EPD - 505-512
A1 - Vin-sen Feng,
A1 - Shih Yu Chang,
PY - 2012
KW - Wireless networks
KW - channel noise estimation
KW - node localization
KW - support vector machine
KW - parallel learning.
VL - 23
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Vin-sen Feng, National Tsing Hua University, HsingChu
Shih Yu Chang, National Tsing Hua University, HsingChu
We consider the problems of 1) estimating the physical locations of nodes in an indoor wireless network, and 2) estimating the channel noise in a MIMO wireless network, since knowing these parameters are important to many tasks of a wireless network such as network management, event detection, location-based service, and routing. A hierarchical support vector machines (H-SVM) scheme is proposed with the following advantages. First, H-SVM offers an efficient evaluation procedure in a distributed manner due to hierarchical structure. Second, H-SVM could determine these parameters based only on simpler network information, e.g., the hop counts, without requiring particular ranging hardware. Third, the exact mean and the variance of the estimation error introduced by H-SVM are derived which are seldom addressed in previous works. Furthermore, we present a parallel learning algorithm to reduce the computation time required for the proposed H-SVM. Thanks for the quicker matrix diagonization technique, our algorithm can reduce the traditional SVM learning complexity from O(n^3) to O(n^2) where n is the training sample size. Finally, the simulation results verify the validity and effectiveness for the proposed H-SVM with parallel learning algorithm.

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Index Terms:
Wireless networks, channel noise estimation, node localization, support vector machine, parallel learning.
Citation:
Vin-sen Feng, Shih Yu Chang, "Determination of Wireless Networks Parameters through Parallel Hierarchical Support Vector Machines," IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 3, pp. 505-512, March 2012, doi:10.1109/TPDS.2011.156
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