{'fraction_non_alphanumeric': 0.018808777429467086, 'fraction_numerical': 0.011494252873563218, 'mean_word_length': 5.517006802721088, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A New Post-whitening Transform Domain LMS Algorithm

Received: 5 March 2019 Accepted: 29 May 2019 This paper proposes a new post-whitening transform domain LMS (POW-TDLMS) algorithm for system identification purposes, where the post whitened and original transformed signals are used during the adaptation and filtering phases, respectively. The main idea behind the proposed algorithm is to introduce a first order adaptive post-whitening filter in the TDLMS algorithm after applying the transform to completely decorrelate the transformed signal. Linear prediction is adopted for the post-whitening and the prediction coefficients are adapted in the time domain. Furthermore, the mean convergence performance analysis of the proposed POWTDLMS algorithm is presented. The simulation results show the superiority of the proposed POW-TDLMS algorithm compared to the conventional TDLMS algorithm in terms of the MSE convergence speed and reached steady state.