%0 Journal Article %1 noauthororeditor %A Tara.Saikumar, %A Devi, R. Nirmala %A Rao, K. Kishna %D 2012 %J International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) %K Adaptive CMA Fractional LMS MMSE Spaced %N 1 %P 8 %R 10.5121/ijasuc.2012.3104 %T Adaptive MMSE Equalizer for Blind Fractional Spaced CMA Channel Equalization through LMS Algorithm %U http://aircconline.com/ijasuc/V3N1/3112ijasuc04.pdf %V 3 %X The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the LMS algorithm. We present in this paper an multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the standard fractionally spaced CMA (constant modulus algorithm) equalizer in the presence of noise, we show that CMA local minima exist near the minimum mean-square error (MMSE) equalizers. Consequently, Fractional Spaced CMA may converge to a local minimum corresponding to a poorly designed MMSE receiver with considerable large mean-square error. The step size in the LMS algorithm decides both the convergence speed and the residual error level, the highest speed of convergence and residual error level.

@article{noauthororeditor, abstract = {The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the LMS algorithm. We present in this paper an multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the standard fractionally spaced CMA (constant modulus algorithm) equalizer in the presence of noise, we show that CMA local minima exist near the minimum mean-square error (MMSE) equalizers. Consequently, Fractional Spaced CMA may converge to a local minimum corresponding to a poorly designed MMSE receiver with considerable large mean-square error. The step size in the LMS algorithm decides both the convergence speed and the residual error level, the highest speed of convergence and residual error level. }, added-at = {2018-03-09T21:08:23.000+0100}, author = {Tara.Saikumar and Devi, R. Nirmala and Rao, K. Kishna}, biburl = {https://www.bibsonomy.org/bibtex/2aacee8a1af4d542e5006da64444a02fb/usmankhawaja}, doi = {10.5121/ijasuc.2012.3104}, interhash = {6d6f42f529a36f5f796f885f89d8b9ce}, intrahash = {aacee8a1af4d542e5006da64444a02fb}, issn = {0976 - 1764}, journal = {International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) }, keywords = {Adaptive CMA Fractional LMS MMSE Spaced}, language = {English}, month = {February}, number = 1, pages = 8, timestamp = {2018-03-09T21:08:23.000+0100}, title = {Adaptive MMSE Equalizer for Blind Fractional Spaced CMA Channel Equalization through LMS Algorithm }, url = {http://aircconline.com/ijasuc/V3N1/3112ijasuc04.pdf}, volume = 3, year = 2012 }