A Wavelet Multi-mode Blind Equalization Method Introducing Immune Optimization Support Vector Machine

Abstract

The invention discloses a wavelet multimode blind equalization method introducing an immune-optimized SVM (Support Vector Machine). Based on the global searching capability of an immune clonal selection algorithm, parameter selection in an SVM blind equalization method is changed from manual selection into automatic determination, and then, the SVM is introduced into an orthogonal wavelet multimode blind equalization method, so that the wavelet multimode blind equalization method introducing the immune-optimized SVM is invented. The method comprises the following steps of: training a small segment of extracted initial data through the SVM to estimate the initial weight value of a blind equalizer, simultaneously, executing optimum selection for the parameters in the SVM by use of an immune algorithm, and taking the initial weight value estimated through the SVM as a weight vector of the orthogonal wavelet multimode blind equalization method. Compared with a multimode blind equalization method, the orthogonal wavelet multimode blind equalization method and an SVM orthogonal wavelet multimode blind equalization method, the method provided by the invention has the advantages of high convergence rate and small steady-state error, and can be used for improving the quality of underwater sound communication better.

Description

technical field [0001] The invention relates to a wavelet multimode blind equalization method which introduces an immune optimization support vector machine into underwater acoustic communication with limited bandwidth. Background technique [0002] In underwater acoustic communication with limited bandwidth, the inter-symbol interference (ISI) caused by channel fading and multipath transmission seriously affects the communication quality and reduces the reliability and transmission rate of underwater data transmission. Therefore, effective channel equalization techniques need to be used to eliminate (see literature [1] Shafayat Abrar, Asoke K.Nandi. Blind equalization of square-QAM signals: a multi-modulus approach.IEEE Trans.Commun.2010.6(58):pp. 1601-1604). Using the constant modulus blind equalization method instead of the traditional adaptive equalization method does not need to send training sequences, which can save a lot of bandwidth and effectively increase the tra...

AI Technical Summary

Problems solved by technology

However, during the construction of the support vector machine, the parameter settings of the SVM have a greater impact on the final classification accuracy.

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