Performance Analysis of Different Adaptive Algorithms for Equalization

Authors

  • Priyanka Aggarwal Department of Electronics and Communication Engineering Graphic Era University, Dehradun, Uttarakhand, India
  • Subhash Chandra Yadav Department of Electronics and Communication Engineering Graphic Era University, Dehradun, Uttarakhand, India
  • Pradeep Juneja Department of Electronics and Communication Engineering
  • R. G. Varshney Department of Mathematics Graphic Era University, Dehradun, Uttarakhand, India

Keywords:

LMS, NLMS, RLS, Adaptive filtering, Convergence rate.

Abstract

The major problems in wireless communication are time dispersion and inter symbol interference. In order to
cancel out the effect introduced by the unknown channel and to recover the original signal as from the distorted
signal, a channel equalizer is required to compensate the effect of channel distortion, time variation and can adapt
it-self to the changes in channel characteristics. The equalizers are expected to have fast convergence rate in
communication systems which is difficult to achieve with conventional adaptive algorithms. LMS is widely used
because it is simple and robust, but performs poor in terms of convergence rate. NLMS is an improved version of
LMS and provides better convergence. RLS exhibit best performance but complex and unstable. In this paper we
simulated adaptive algorithms such as LMS, NLMs and RLS algorithms in MATLAB and compared their
performance

Downloads

Download data is not yet available.

References

Barry, J. R., Lee, E. A., & Messerschmitt, D. G. (2004). Digital communication. Springer Science & Business

Media.

Borisagar, K. R., & Kulkarni, D. G. (2010). Simulation and comparative analysis of LMS and RLS algorithms

using real time speech input signal. Global Journal of Research in Engineering, 10(5).

Eleftheriou, E., & Falconer, D. (1986). Tracking properties and steady-state performance of RLS adaptive filter

algorithms. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(5), 1097-1110.

Haykin, S. S. (2008). Adaptive filter theory. Pearson Education India.

Haykin, S., & Moher, M. (2007). Introduction to Analog & Digital Communications, Hoboken.

Ifeachor, E. C., & Jervis, B. W. (2002). Digital signal processing: a practical approach. Pearson Education.

Razzak, I. (2015). Adaptive filtering algorithms for channel equalization in wireless communication. Indian

Journal of Science and Technology, 8(17).

Reddy, B. S., & Krishna, V. R. (2013, October). Implementation of Adaptive Filter Based on LMS Algorithm.

In International Journal of Engineering Research and Technology, 2(10) (October-2013)). ESRSA Publications.

Tato, L. M., & Miranda, H. C. (2002). Simulation of an RLS Adaptive Equalizer using Simulink.

Downloads

Published

2023-02-28

How to Cite

Aggarwal, P., Yadav, S. C., Juneja, P., & Varshney, R. G. (2023). Performance Analysis of Different Adaptive Algorithms for Equalization. Journal of Graphic Era University, 4(2), 92–102. Retrieved from https://riverpublishersjournal.com/index.php/JGEU/article/view/121

Issue

Section

Articles