Comparison between Epsilon Normalized Least means Square (ϵ-NLMS) and Recursive Least Squares (RLS) Adaptive Algorithms

EasyChair Preprint no. 32

Abstract

There is an evidence that channel estimation in communication systems plays a crucial issue in recovering the transmitted data. In recent years, there has been an increasing interest to solve problems due to channel estimation and equalization especially when the channel impulse response is fast time varying Rician fading distribution that means channel impulse response change rapidly. Therefore, there must be an optimal channel estimation and equalization to recover transmitted data. However. this paper attempt to compare epsilon normalized least mean square (ϵ-NLMS) and recursive least squares (RLS) algorithms by computing their performance ability to track multiple fast time varying Rician fading channel with different values of Doppler frequency, as well as mean square deviation (MSD) has simulated to measure the difference between original channel and what is estimated. The simulation results of this study showed that (ϵ-NLMS) tend to perform fast time varying Rician fading channel better than (RLS) adaptive filter.

Keyphrases: epsillon NLMS, MSD, Rician channel, RLS

@Booklet{EasyChair:32,
  author = {Ali Salah Mahdi and Lwaa Faisal Abdulameer and Ameer Hussein Morad},
  title = {Comparison between Epsilon Normalized Least means Square (ϵ-NLMS) and Recursive Least Squares (RLS) Adaptive Algorithms},
  howpublished = {EasyChair Preprint no. 32},
  doi = {10.29007/h74f},
  year = {EasyChair, 2018}}