A Proposal of Normalized Adaptive Direct Blind Equalization and its application
Minoru Komatsu, Nari Tanabe · 2022
This paper proposes normalized adaptive direct blind equalization and its application. There is adaptive direct blind equalization in noisy environments as one of conventional methods. It is known that the conventional method is higher precision even when the observed signals are included the noises. However, the conventional method has the problem that there is lower convergence rate because this method is adaptive algorithm based on Least Square method. In this paper, for solving this problem, we describe normalized adaptive direct blind equalization and propose the method which uses both proposed method and conventional method. The features of proposed method are (i) realization of high performance and stable equalizer estimation with Data Least square(DLS) [1] and (ii) realization of high convergence rate and recovery performance. We show the effectiveness of the proposed method using computer simulations.