Advance Convergence Characteristic Based on Recycling Buffer Structure in Adaptive Transversal Filter

We extend the use of the least squares method to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of a vector of a filter at iteration, we may compute the updated estimate of this vector at iteration upon the arrival of new data. In this paper, we propose a new tap-weight-updated RLS algorithm for an adaptive transversal filter with data-recycling buffer structure. We prove that the convergence speed of the learning curve of an RLS algorithm with a data-recycling buffer is faster than existing RLS algorithms at 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 samples should be increased to converge the specified value from the three-dimensional simulation results of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of the convergence performance is achieved at B times the convergence speed of the mean square error increase in the data recycle buffer number with new proposed RLS algorithm.