Research ArticleOpen AccessGoogle Scholar indexed
Using Least Squares Support Vector Machines for Frequency Estimation
- 1
- 2
- 3
International Journal of Communications, Network and System Sciences·Volume 03 (2010)·Pages 821–825·Published 27 October 2010·DOI10.4236/ijcns.2010.310111
Copy link · social · email
Abstract
Frequency estimation is transformed to a pattern recognition problem, and a least squares support vector machine (LS-SVM) estimator is derived. The estimator can work efficiently without the need of statistics knowledge of the observations, and the estimation performance is insensitive to the carrier phase. Simulation results are presented showing that proposed estimators offer better performance than traditional Maximum Likelihood (ML) estimator at low SNR, since classification-based method does not have the threshold effect of nonlinear estimation.
KeywordsCarrier RecoveryLS-SVMPattern Recognition
- D. C. Rife and R. R. Boorstyn, “Single-Tone Parameter Estimation from Discrete-Time Observations,” IEEE Transactions on Information Theory, Vol. 20, No. 5, 1974, pp. 591-598.
- M. Morelli and U. Mengali, “Feedforward Frequency Estimation for PSK: A Tutorial Review,” European Transactions on Telecommunications, Vol. 9, No. 2, 1998, pp. 103-116.
- W. Y. Kuo and M. P. Fitz, “Frequency Offset Compensation of Pilot Symbol Assisted Modulation in Frequency Flat Fading,” IEEE Transactions on Communications, Vol. 45, No. 11, 1997, pp. 1412-1416.
- U. Mengali and M. Morelli, “Data-Aided Frequency Estimation for Burst Digital Transmission,” IEEE Transactions on Communications, Vol. 45, No. 1, 1997, pp. 23-25.
- Y. Wang, E. Serpedin and P. Ciblat, “Optimal Blind Carrier Recovery for MPSK Burst Transmissions,” IEEE Transactions on Communications, Vol. 51, No. 9, 2003, pp. 1571-1581.
- D. N. Swingler, “Approximate Bounds on Frequency Estimaties for Short Cissoids in Colored Noise,” IEEE Transactions on Signal Processing, Vol. 46, No. 5, 1998, pp. 1456-1458.
- J. Viterbi and A. M. Viterbi, “Nonlinear Estimation of Psk-Modulated Carrier Phase with Application to Burst Digital Transmissions,” IEEE Transactions on Information Theory, Vol. 29, No. 4, 1983, pp. 543-551.
- V. N. Vapnik, “Statistical learning theory,” John Wiley & Sons, Inc., New York, 1998.
- D. J. Sebald, “Support Vector Machine Techniques for Nonlinear Equalization,” IEEE Transactions on Signal Processing, Vol. 48, No. 11, 2000, pp. 3217-3226.
- S. Chen, S. Gunn and C. Harris, “Decision Feedbach Equalizer Design Using Support Vector Machines,” Vision, Image and Signal Processing, Vol. 147, No. 3, 2000, pp. 213-219.
- F. Perez-Cruz, A. Navia-Vazquez, P. Alarcon-Dianna, and A. Artes-Rodriguez, “SVC-Based Equalizer for Burst TDMA Transmission,” Signal Processing, Vol. 81, No. 6, 2001, pp. 1681-1693.
- J. G. Proakis, “Digital Communications,” 4th Edition, McGraw-Hill, New York, 2001.