한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지

Multi-User Detection using Support Vector Machines

  • Lee, Jung-Sik (School of Electronics & Information Eng., Kunsan National University) ;
  • Lee, Jae-Wan (School of Electronics & Information Eng., Kunsan National University) ;
  • Hwang, Jae-Jeong (School of Electronics & Information Eng., Kunsan National University) ;
  • Chung, Kyung-Taek (School of Electronics & Information Eng., Kunsan National University)
  • 발행 : 2009.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, support vector machines (SVM) are applied to multi-user detector (MUD) for direct sequence (DS)-CDMA system. This work shows an analytical performance of SVM based multi-user detector with some of kernel functions, such as linear, sigmoid, and Gaussian. The basic idea in SVM based training is to select the proper number of support vectors by maximizing the margin between two different classes. In simulation studies, the performance of SVM based MUD with different kernel functions is compared in terms of the number of selected support vectors, their corresponding decision boundary, and finally the bit error rate. It was found that controlling parameter, in SVM training have an effect, in some degree, to SVM based MUD with both sigmoid and Gaussian kernel. It is shown that SVM based MUD with Gaussian kernels outperforms those with other kernels.

키워드

참고문헌

  1. Madhow, U., Honig, M. L., 'MMSE Interference Suppression for Direct Sequence Spread Spectrum CDMA,' IEEE Transactions on Communications, Vol.42., pp.3178-3188, 1994 https://doi.org/10.1109/26.339839
  2. Poor, H.V.,Verdu, S., 'Probability of Errors in MMSE Multiuser Detection,' IEEE Transactions on Information Theory, Vol.43, pp.858-871, 1997 https://doi.org/10.1109/18.568697
  3. Aazhang, B., Paris, B.P., Orsak, G.C., 'Neural Networks for Multiuser Detection in Code- Division Multiple-Access Channels,' IEEE Transactions on Communications, Vol.40, pp.1212-1222, 1992 https://doi.org/10.1109/26.153366
  4. Mitra, U., Poor, H.V., 'Neural Network Techniques for Adaptive Multiuser Demodulation,' IEEE Journal on Selected Areas in Communications, Vol.12, pp.1460-1470, 1994 https://doi.org/10.1109/49.339913
  5. Cruickshank, D.G.M., 'Radial Basis Function Receivers for DS-CDMA,' IEE Electronic Letters, Vol.32, pp.188-190, 1996 https://doi.org/10.1049/el:19960175
  6. Matyjas, J.D., Psaromiligkos, I.N., Batalama, S.N., Medley, M.J., 'Fast Converging Minimum Probability of Error Neural Network Receivers for DS-CDMA Communications,' IEEE Transactions on Neural Networks, Vol.15, pp.445-454, 2004 https://doi.org/10.1109/TNN.2004.824409
  7. Chen, S., Mulgrew, B., Grant, P.M., 'A Clustering Technique for Digital Communication Channel Equalization using Radial Basis Function Networks,' IEEE Transactions on Neural Networks, Vol.4, pp.570-579, 1993 https://doi.org/10.1109/72.238312
  8. Lee, J., Beach, C.D., Tepedelenlioglu, N., 'A Practical Radial Basis Function Equalizer,' IEEE Transactions on Neural Networks, Vol.10, pp. 450-455, 1999 https://doi.org/10.1109/72.750577
  9. Chen, S., Samingan, A. K., Hanzo, L., 'Support Vector Machine Multiuser Receiver for DS-CDMA Signals in Multipath Channels', IEEE Trans. on Neural Networks, Vol.12, pp.604-611, 2001 https://doi.org/10.1109/72.925563
  10. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995
  11. Burges, C. C. 'A tutorial on support Vector Machine for Pattern Recognition,' Data Mining and Knowledge Discovery, Vol.2, pp.121-167, 1998 https://doi.org/10.1023/A:1009715923555