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Ling, F. and Proakis, J.G. (1984) Non Stationary Learning Characteristics of Least Squares Adaptive Estimation Algorithms. Proceedings of ICASSP84, San Diego, 3.7.1-3.7.4.

has been cited by the following article:

• TITLE: Simulation and Comparative Analysis of SS-LMS & RLS Algorithms for Electronic Dispersion Compensation

  AUTHORS: Gurpreet Kaur, Gurmeet Kaur

  KEYWORDS: Bit Error Rate (BER), Electronic Dispersion Compensation (EDC), Feed-Forward Equalizer (FFE), Recursive Least Square (RLS), Sign-Sign Least Mean Square (SS-LMS)

  JOURNAL NAME: Open Access Library Journal, Vol.1 No.4, July 29, 2014

  ABSTRACT: In this paper electronic feed forward equalization is performed to mitigate the link chromatic dispersion. The equalizer coefficients are computed by a decision-directed process based on the sign-sign least mean square and the recursive least square algorithm. Therefore, this paper evaluates the performance of these algorithms in chromatic dispersion compensation at bit rate of 10 Gb/s. This paper compares these two adaptation algorithms for receiver based on analogue electronic dispersion equalizers by simulation and experiment. This paper concluded that recursive least-square algorithm is computationally more complex than sign-sign least mean square algorithm since matrix inversion is required, but achieves faster convergence.