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LMS Algorithm for Adaptive Transversal Equalization of a Linear Dispersive Communication Channel

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  • Farrukh Arslan

Abstract

The presence of any type of distortion in communication system, regardless of the causes, is undesirable and undeniably has a negative impacts on the system in general and therefore it is necessary to eliminate its effects. This study employs one of the well-known algorithms for adaptive equalization of linear dispersive communication channel which is Least Mean Square (LMS) algorithm. The LMS technique is basically utilized to eliminate the noise in communication channel. The novelty of this paper includes the profoundly analyzing of the influence of rate of convergence, miss-adjustment, computational requirement, and sensitivity to Eigen-value spread in sufficient details in a simple and plain way. Moreover, the system performance improvement employing the feedback equalizer technique is intensively presented which shows that our methodology is very effective to eliminate the noise in the system. The simulation work has been performed with MATLAB software.

Suggested Citation

  • Farrukh Arslan, 2020. "LMS Algorithm for Adaptive Transversal Equalization of a Linear Dispersive Communication Channel," Review of Computer Engineering Research, Conscientia Beam, vol. 7(2), pages 73-85.
    • Handle: RePEc:pkp:rocere:v:7:y:2020:i:2:p:73-85:id:1481
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    Cited by:

    1. Abdulaziz S. Alkabaa & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Nazemi & El Mostafa Kalmoun, 2022. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach," Mathematics, MDPI, vol. 10(4), pages 1-21, February.

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