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Issue No.03 - March (2012 vol.23)
pp: 505-512
Vin-sen Feng , National Tsing Hua University, HsingChu
Shih Yu Chang , National Tsing Hua University, HsingChu
ABSTRACT
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.
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 & Distributed Systems, vol.23, no. 3, pp. 505-512, March 2012, doi:10.1109/TPDS.2011.156
REFERENCES
[1] Q. Sun, D.C. Cox, H.C. Huang, and A. Lozano, "Estimation of Continuous Flat Fading MIMO Channels," IEEE Trans. Wireless Comm., vol. 1, no. 3, pp. 549-553, Oct. 2002.
[2] J. Baltersee, G. Fock, and H. Meyr, "Achievable Rate of MIMO Channels with Data-Aided Channel Estimation and Perfect Interleaving," IEEE J. Selected Areas of Comm., vol. 19, no. 12, pp. 2358-2368, Dec. 2001.
[3] V. Vapnik, The Nature of Statistical Learning Theory, second ed. Springer, 1999.
[4] D.A. Tran and T. Nguyen, "Localization in Wireless Sensor Networks Based on Support Vector Machines," IEEE Trans. Parallel and Distributed Systems, vol. 19, no. 7, pp. 981-994, July 2008.
[5] e.a.B. Scholkopf, "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers," IEEE Trans. Signal Processing, vol. 45, no. 11, pp. 2758-2765, Nov. 1997.
[6] A. Papoulis and S.U. Pillai, Probability, Random Variables, and Stochastic Processes, fourth ed. McGraw-Hill, 2002.
[7] A. Ziehe, P. Laskov, G. Nolte, and K.-R. Muller, "A Fast Algorithm for Joint Diagonization with Non-Orthogonal Transformations and Its Application to Blind Source Separation," The J. Machine Learning Research, vol. 5, pp. 777-800, Dec. 2004.
[8] N. Megiddo and A. Tamir, "Linear Time Algorithms for Some Separable Quadratic Programming Problems," Operations Research Letters, vol. 13, pp. 203-211, May 1993.
[9] The Network Simulator—ns-2, http://www.isi.edu/nsnamns/, 2011.
[10] C. Bettstetter, "On the Minimum Node Degree and Connectivity of a Wireless Multihop Network," Proc. Int'l Symp. Mobile Ad Hoc Networking and Computing, pp. 80-91, June 2002.
[11] ALIBSVM—A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/cjlinlibsvm/, 2011.
[12] T.F. Coleman and Y. Li, "A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on Some of the Variables," SIAM J. Optimization (SIOPT), vol. 6, pp. 1040-1058, Nov. 1996.
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