A Novel SVM-Based OOK Detector in Low SNR Infrared Channels
Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
[1] V. Vapnik, "Estimation of Dependences Based on Empirical Data,"
Nauka English Translation, Springer Verlag, 1982.
[2] J. Christopher and C. Burges, "A Tutorial on Support Vector Machines
for Pattern Recognition", Kulwer Academic Publishers, Knowledge
Discovery & Data Mining, vol. 12, no. 2, 1998, pp. 121-167.
[3] J. Weston and C. Watkins, "Support Vector Machines from Multi-Class
Pattern Recognition," University Of London, unpublished.
[4] S. Chen, S. Gunn, and C. Harris, "Decision Feedback Equalizer Design
Using Support Vector Machines," Inst. Elect. Eng. Proc. Vision, Image
and Signal Processing, vol. 147, no. 3, 2000, pp. 213-219.
[5] D. Sebald and J. Bucklew, "Support Vector Machine Techniques for
Non Linear Equalization," IEEE Transactions on Signal Processing, vol.
48, 2000, pp. 3217-3266.
[6] F. Albu and D. Martinez, "The Application of Support Vector Machines
with Gaussian Kernels for Overcoming Co-Channel Interference,"
Proceedings of the 9th IEEE International Workshop Neural Networks
for Signal Processing, Madison, WI, Aug. 23-25, 1999, pp. 49-57.
[7] F. Perez-Cruz, A. Navia-Vazquez, P. Alarcon-Dianna, and A. Artes-
Rodriguez, "SVC-Based Equalizer for Burst TDMA Transmission,"
Signal Processing, 2000.
[8] C. Mokbel and F. Hashem, "Support Vector Machines in Digital
Communication," Master Thesis, Univ. of Balamand, Lebanon, 2003.
[9] M. Audeh, J. Khan, and J. Barry, "Decision-Feedback Equalization of
Pulse-Position Modulation on Measured Nondirected Indoor Infrared
channels," IEEE Trans. on Comm., vol. 47, no. 4, 1999, pp. 500 - 510.
[10] B. Mukherjee, Optical Communication Networks. McGraw-Hills, 1997.
[11] C. Hsu and C. Lin, "A Comparison of Methods for Multi-Class Support
Vector Machines," IEEE Trans. Neural Net., vol. 13, 2002, pp.415-425.
[12] A. Smola, "Support Vector Learning: Concepts and Algorithms,"
Australian National University, unpublished.
[13] L. Lukas, P. Dooren & B. De Moor, "Least Square-Support Vector
Machine Classifier: A Large Scale Algorithm," Universite Catholique de
Louvain, Belgium, unpublished.
[14] T. Joachims, "Support Vector Machines", Cornell University,
unpublished.
[15] J. Kahn and J. Barry, "Wireless Infrared Communication," IEEE journal
on selected areas in Communication, vol. 16, 1997.
[16] G. Keiser, Optical Fiber Communications. London: Macgraw Hills,
2000.
[17] H. Uno, K. Kumatani, adn I. Shirakana, "Detection of Digital Modulated
Infrared System," Osaka University, Japan, unpublished.
[18] S. Hranilovic, "Modulation and Constrained Coding Techniques for
Wireless Infrared Communication Channels," University of Toronto,
Canada, unpublished.
[19] J. Daba and M. Bell, "Statistics of the Scattering Cross Section of a
Small Number of Random Scatterers," IEEE Transactions on Antennas
and Propagation, Vol. 43, No. 8, pp.773-783, August 1995.
[20] J. Barry, J. Kahn, W. Krause, E. Lee, and D. Messerchmitt, "Simulation
of Multipath Impulse Response for Indoor Wireless Optical Channels,"
IEEE Journal on Selected Areas in Comm., vol. 11, no. 3, 1993.
[21] K. Mandke, H. Nam, L. Yerramneni, C. Zuniga, T. Rappaport,
"Ultrawide band Wireless," High Frequency Electronics, pp. 22-32,
Sept. 2003.
[22] D. Snyder and M. Miller, Random Point Processes in Time and Space.
New York: Springer-Verlag, 2002.
[23] K. Pelckmans, J. Suykens, T. Gestel, J. De Brabanter, L. Lukas, B.
Hamers, B. De Moor, and J. Vandewalle, "LS-SVMlab Toolbox User-s
Guide Version 1.5," Katholiede Univeristeit Leuven, Belgium,
unpublished. Available http://www.esat.kuleuven.ac.be/sista/lssvmlab/
[24] D. Anguita, A. Boni, and S. Ridella, "A Digital Architecture for SVM:
Theory, Algorithm & FPGA Implementation," IEEE Transaction on
Neural Networks, vol. 14, no. 5, 2003, pp. 993-1000.
[1] V. Vapnik, "Estimation of Dependences Based on Empirical Data,"
Nauka English Translation, Springer Verlag, 1982.
[2] J. Christopher and C. Burges, "A Tutorial on Support Vector Machines
for Pattern Recognition", Kulwer Academic Publishers, Knowledge
Discovery & Data Mining, vol. 12, no. 2, 1998, pp. 121-167.
[3] J. Weston and C. Watkins, "Support Vector Machines from Multi-Class
Pattern Recognition," University Of London, unpublished.
[4] S. Chen, S. Gunn, and C. Harris, "Decision Feedback Equalizer Design
Using Support Vector Machines," Inst. Elect. Eng. Proc. Vision, Image
and Signal Processing, vol. 147, no. 3, 2000, pp. 213-219.
[5] D. Sebald and J. Bucklew, "Support Vector Machine Techniques for
Non Linear Equalization," IEEE Transactions on Signal Processing, vol.
48, 2000, pp. 3217-3266.
[6] F. Albu and D. Martinez, "The Application of Support Vector Machines
with Gaussian Kernels for Overcoming Co-Channel Interference,"
Proceedings of the 9th IEEE International Workshop Neural Networks
for Signal Processing, Madison, WI, Aug. 23-25, 1999, pp. 49-57.
[7] F. Perez-Cruz, A. Navia-Vazquez, P. Alarcon-Dianna, and A. Artes-
Rodriguez, "SVC-Based Equalizer for Burst TDMA Transmission,"
Signal Processing, 2000.
[8] C. Mokbel and F. Hashem, "Support Vector Machines in Digital
Communication," Master Thesis, Univ. of Balamand, Lebanon, 2003.
[9] M. Audeh, J. Khan, and J. Barry, "Decision-Feedback Equalization of
Pulse-Position Modulation on Measured Nondirected Indoor Infrared
channels," IEEE Trans. on Comm., vol. 47, no. 4, 1999, pp. 500 - 510.
[10] B. Mukherjee, Optical Communication Networks. McGraw-Hills, 1997.
[11] C. Hsu and C. Lin, "A Comparison of Methods for Multi-Class Support
Vector Machines," IEEE Trans. Neural Net., vol. 13, 2002, pp.415-425.
[12] A. Smola, "Support Vector Learning: Concepts and Algorithms,"
Australian National University, unpublished.
[13] L. Lukas, P. Dooren & B. De Moor, "Least Square-Support Vector
Machine Classifier: A Large Scale Algorithm," Universite Catholique de
Louvain, Belgium, unpublished.
[14] T. Joachims, "Support Vector Machines", Cornell University,
unpublished.
[15] J. Kahn and J. Barry, "Wireless Infrared Communication," IEEE journal
on selected areas in Communication, vol. 16, 1997.
[16] G. Keiser, Optical Fiber Communications. London: Macgraw Hills,
2000.
[17] H. Uno, K. Kumatani, adn I. Shirakana, "Detection of Digital Modulated
Infrared System," Osaka University, Japan, unpublished.
[18] S. Hranilovic, "Modulation and Constrained Coding Techniques for
Wireless Infrared Communication Channels," University of Toronto,
Canada, unpublished.
[19] J. Daba and M. Bell, "Statistics of the Scattering Cross Section of a
Small Number of Random Scatterers," IEEE Transactions on Antennas
and Propagation, Vol. 43, No. 8, pp.773-783, August 1995.
[20] J. Barry, J. Kahn, W. Krause, E. Lee, and D. Messerchmitt, "Simulation
of Multipath Impulse Response for Indoor Wireless Optical Channels,"
IEEE Journal on Selected Areas in Comm., vol. 11, no. 3, 1993.
[21] K. Mandke, H. Nam, L. Yerramneni, C. Zuniga, T. Rappaport,
"Ultrawide band Wireless," High Frequency Electronics, pp. 22-32,
Sept. 2003.
[22] D. Snyder and M. Miller, Random Point Processes in Time and Space.
New York: Springer-Verlag, 2002.
[23] K. Pelckmans, J. Suykens, T. Gestel, J. De Brabanter, L. Lukas, B.
Hamers, B. De Moor, and J. Vandewalle, "LS-SVMlab Toolbox User-s
Guide Version 1.5," Katholiede Univeristeit Leuven, Belgium,
unpublished. Available http://www.esat.kuleuven.ac.be/sista/lssvmlab/
[24] D. Anguita, A. Boni, and S. Ridella, "A Digital Architecture for SVM:
Theory, Algorithm & FPGA Implementation," IEEE Transaction on
Neural Networks, vol. 14, no. 5, 2003, pp. 993-1000.
@article{"International Journal of Electrical, Electronic and Communication Sciences:54158", author = "J. P. Dubois and O. M. Abdul-Latif", title = "A Novel SVM-Based OOK Detector in Low SNR Infrared Channels", abstract = "Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
", keywords = "Least square-support vector machine, on-off keying, matched filter, maximum likelihood detector, wireless infrared communication.", volume = "1", number = "7", pages = "982-5", }
