MULTI-RESOLUTION BASED DISCRETE WAVELET TRANSFORM FOR ENHANCED SIGNAL COVERAGE PROCESSING AND PREDICTION ANALYSIS
Abstract
Wavelet transform methods are extensively employed to transform signal dataset into different components for effective spatial signal power processing and modelling. In this work, a distinctive multi-resolution discrete wavelet transform algorithm is explored for enhanced processing and decomposition of measured signal power coverage data acquired from operation LTE cellular communication networks. The effectiveness of Symlets (sym) and Daubechies (db) wavelets under five decomposition levels are analyzed based on five quantitative evaluation parameters: root mean square error (RMSE), signal to error reconstruction ratio, (SRER), Pulse amplitude demodulation (PAD), Coefficient of correlation (R) and standard deviation (STD). The results that multi-resolution based processed signal with sym3 wavelet under decomposition level 4 is best compared to others, in terms of the computed statistical indices. The above best performance with sym3 can be attributed to their excellent denoising property of the entire symlets wavelet family. Besides, the obtained results reveal that the choice of wavelet thresholding technique and level of decomposition also significantly impact the sensitivity and reliability of data-driven predictive analytics with adaptive polynomial
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