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The Improvement of Convergence Characteristic using the New RLS Algorithm in Recycling Buffer Structures  

Kim, Gwang-Jun (Dept.of Computer Engineering, Yosu National University)
Kim, Chun-Suck (Dept.of Electronics Communication Engineering, Yosu National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.7, no.4, 2003 , pp. 691-698 More about this Journal
Abstract
We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-l, we may compute the updated estimate of this vector at iteration n upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RLS algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the B times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.
Keywords
RLS Algorithm; Convergence Characteristic; Transversal filter; Recycling buffer; Tap weight;
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