Research on Partial Updating RLS Algorithm 

Author  CuiKaiHua 
Tutor  JinMingLu 
School  Dalian University of Technology 
Course  Communication and Information System 
Keywords  Communications technology Adaptive Algorithm RLS algorithm Section is adapted to Convergence Analysis 
CLC  TN911.72 
Type  Master's thesis 
Year  2010 
Downloads  178 
Quotes  1 
Adaptive algorithm is an adaptive signal processing is an important component of its efficiency, practicality it is widely used in the antenna array, the predistortion technology, echo cancellation and other fields. LMS adaptive algorithm is divided into two major categories of algorithms and RLS algorithm, in which the LMS algorithm with low computational complexity, but the slow convergence; while the RLS algorithm converges faster, but the computational complexity is very high. This algorithm is based on the data matrix does not have shift invariance case, based on this case, people were raised Split RLS algorithm, HRLS algorithm and PURLS algorithm (Partial Updating RLS Algorithm, abbreviated PURLS algorithm). Where PURLS algorithm performance is better, but because of its timeinterleaved update mechanism, resulting in a number of crossinterference between subfilters, affecting the convergence speed. Based on the above issues, this paper based on the relevant input environment PURLS algorithm is to make a detailed analysis of the convergence, the algorithm gives the ensemble average learning curve expressions that summed affect system performance Several parameters; secondly on the basis of previous work in this paper, an improved partial update RLS algorithm (Improved Partial Updating RLS Algorithm, referred IPURLS algorithm), the algorithm uses the weighting coefficients in the adaptive power system plays a major feature role, first to PURLS algorithm for the iterative determination of the filter coefficients a small size relationship, and then the filter coefficients are rearranged in descending, the order of all the filter coefficients are divided into a plurality of equal parts, then only the the largest part of the weights to be updated in order to achieve the power to focus on weight coefficient updating purposes. When the weight reaches the largest part is relatively stable, to the various parts of the timeupdated alternately. The simulation results show that, IPURLS algorithm can not increase in the basic PURLS algorithm based on computational complexity, improve the convergence speed. Finally, the paper IPURLS algorithm made some improvements, namely preprocessing stage pretreatment NLMS algorithm instead. Because the NLMS algorithm used, its convergence rate faster than the LMS algorithm, while the algorithm is similar to the complexity and the LMS algorithm, with a smaller amount of computation, a faster speed to determine the magnitude relationship between the respective filter coefficients, so as to effectively IPURLS algorithm reduces the computational complexity.