Wavelet Transform Blind Equalization Algorithm Based On Entropy Transform And DSP Simulation

With the development of information theory, people are increasingly interested in information entropy. At present, information entropy has been widely used in image processing, adaptive filtering, digital communications, deconvolution, and pattern recognition, blind source separation and channel equalization in a lot of practice. However, the air change in the amount of information in the underwater acoustic communication, time-varying, frequency change and other characteristics caused by inter-symbol interference, seriously affecting the quality of communication. Therefore, at the receiving end requires the use of information entropy blind equalization to compensate for channel distortion, eliminating inter-symbol interference and improve communication quality.In this paper, the analysis of fractionally spaced equalizer performance, decision feedback structure model and the orthogonal wavelet transform principle on the basis of information entropy is introduced into a fractionally spaced decision feedback equalizer, to carry out the wavelet blind equalization algorithm and multi-mode blind equalization algorithm based on information entropy, to improve the practical value of information entropy and improve the quality of the underwater acoustic channel of communication. The main work is as follows:1Describes the transformation principle of the information entropy and the structure model and performance analysis of several blind equalizers.2In order to overcome the channel distortion and distortion, the integration of fractionally spaced equalizer, decision feedback structure, wavelet transform theory and Renyi entropy, orthogonal wavelet transform rayleigh renyi entropy blind equalization algorithm based on fractionally spaced decision feedback (FSDFEWTR) is proposed. Simulation results verify the effectiveness of the proposed algorithm.3For deep spectral nulls channel fading, the use of the principle of the FSDFEWTR algorithm, combining the advantages spatial diversity, Spatial Diversity Fractionally Spaced Wavelet blind Equalization algorithm based on Rayleigh Renyi entropy (SDFSEWTR) is proposed. Simulation results verify the superiority of the proposed algorithm.4In order to further eliminate the channel fading, Sampling shall function as the window function and combining the advantages of time diversity based on FSDFEWTR algorithms and SDFSEWTR algorithms, Spline function Renyi entropy based on Time Diversity fractionally spaced Decision Feedback Wavelet blind Equalization algorithm(SRTDDFEW) is proposed.The algorithms take into account the time diversity, the fractionally spaced equalizer, decision feedback structure, wavelet theory and Renyi entropy transform the advantages of faster convergence rate, mean square error of the smaller features. Simulation results verify the superiority of the proposed algorithm.5In order to overcome the single-stage equalizer only time compensation of channel distortion inherent defect, based on quadratic inverse party function Tsallis entropy of Cascade Multi-Modulus blind equalization Algorithm (TCMMA) is proposed. The advantage of this algorithm lies in the secondary to compensate for channel distortion and improve the balanced performance of the underwater acoustic channel. Simulation results verify the superiority of the proposed algorithm.6For high-end QAM signal amplitude distortion and phase distortion, based on Gauss window function information entropy of Wavelet Multi-Modulus blind balanced algorithms(R/T/SFDWMMA) is proposed. This algorithm to speed up the convergence rate and reduce the inter-symbol interference, and effectively improve the equalization performance of the high-end QAM signal. Simulation results verify the superiority of the proposed algorithm.7In order to overcome the difficulties of the algorithm DSP simulation, Renyi entropy blind equalization algorithm (FSE-R) is proposed. The simulation results show that DSP channel equalization with good usability.Figure [43] Table [11] Reference[91]...