A novel PSO based blind channel estimation and equalization

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Tarih

2012

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Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

As an alternative technique to well-known constant modulus algorithm (CMA), a Decision Feedback Equalizer via Channel Matched Filter (CMF-DFE) based blind channel estimation and equalization algorithm is proposed in this paper. The proposed technique employs Particle Swarm Optimization (PSO) in training, where the conventional CMA and least mean squares (LMS) based training algorithms are found slow and their convergence strictly depend on the step size parameter. On the other hand, if the PSO training algorithm is employed, it provides a faster convergence and less sensitivity to training parameters. Thus, a rapid converging high performance blind channel estimation and equalization method is obtained, as it is compared to CMF-DFE based blind LMS and CMA algorithm. Here, the price is paid for more complexity, however the obtained performance promises to compete with reference training sequence based parameter estimation techniques. © 2012 IEEE.

Açıklama

2012 20th Signal Processing and Communications Applications Conference -- SIU 2012 -- 18 April 2012 through 20 April 2012 -- Fethiye -- Mugla -- 90786

Anahtar Kelimeler

Algorithms, Parameter estimation, Particle swarm optimization (PSO), Blind channel estimation, Channel matched filters, Constant modulus algorithms, Equalization algorithms, Estimation techniques, Faster convergence, Least mean square(LMS), Step size, Training algorithms, Training parameters, Training sequences, Blind equalization

Kaynak

2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings

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N/A

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