Open AccessBlind Signal Processing Algorithmsbased based on Recursive Gradient Estimation
Author(s) -
Namyong Kim,
Min Ji Kang
Publication year - 2015
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v5i3.pp548-561
Subject(s) - computational complexity theory , algorithm , computer science , set (abstract data type) , signal processing , block (permutation group theory) , function (biology) , gradient method , mathematical optimization , mathematics , digital signal processing , combinatorics , evolutionary biology , computer hardware , biology , programming language
Blind algorithms based on the Euclidean distance (ED) between the output distribution function and a set of Dirac delta functions have a heavy computational burden of due to some double summation operations for the sample size and symbol points. In this paper, a recursive approach to the estimation of the ED and its gradient is proposed to reduce the computational complexity for efficient implementation of the algorithm. The ED of the algorithm is comprised of information potentials (IPs), and the IPs at the next iteration can be calculated recursively based on the currently obtained IPs. Utilizing the recursively estimated IPs, the next step gradient for the weight update of the algorithm can be estimated recursively with the present gradient. With this recursive approach, the computational complexity of gradient calculation has only . The simulation results show that the proposed gradient estimation method holds significantly reduced computational complexity keeping the same performance as the block processing method