Research On Generalized Correlation And Fuzzy Logic Based Modulation Classification

The various signal pattem,frequency and function of radio are more complicated inspace,and the form diversifies,objectively,require that the radio terminal is able to adapt tomany patterns,multifrequency,multi functional environment.Therefore,in the process ofusing,planning and creating,the radio consumer longs to be able to predict and describ thestates of complicated space radio signals using RKRL(Radio Knowledge RepresentationLanguage),include DOA(Direction Of Arrival),frequency,power,modulation format,signal channel encode,frame structure,etc..Then analyse on this complicated state spacerationally and make reasonal choices.All the above issues form the concept of"CognitiveRadio".As a necessary function module,Automatic modulation classification has attractedmore attention.The intercepted signals are often concealed in noise including Gaussianwhite noise and impulse noise.Moreover,since the former methods of statistical patternrecognition and decision theory are mostly used,the information of classification isn'tadequately utilized.Sometimes,the features are extracted subjectively and easily affected bynoise,so the classification performances aren't satisfying.How to reduce the influence ofsignal noise ratio and interference in features? How to design the classifier with greateradaptability and fault tolerance? All of these problems are the topic way of the paperresearch.Based on the previous works,this thesis has engaged in extensive research on Sub-topicof modulation foramat classification in"Radio Cognition Studies".Considering the largevariation range of communication signal's SNR and the interference from other signals,themodified preprocessing,the novel classification features with excellent quality and theoptimal adaptive classifiers are presented for automatic digital modulation classification.Finally an improved global solution is formed.The main works can be summarizedasfollows:Firstly,several key problems of the preprocessing of modulation classification areintegrated into a part.The instantaneous frequency estimation method at signal sequenceboundary and the accurate carrier frequency estimation method are analysed.A fuzzyweighted filter algorithm with preferable signal details protection capability is presented,which is contributed to improve the classification performance.Then,the selection and abstraction of classification features are discussed.This thesisintroduces the high order orthogonal cumulant (HOOC)and high order orthogonal cycliccumulant (HOOCC)based on the analysis of generalized correlation function.The optimalstructure form of the classification features are selected according to the theoretical analyseand simulation estimation.The novel features actually defines a high Order correlation statistics of the in-phase component and quadrature component of the modulated signal,which contains more essential information of modulation formats and has evidente effect tosuppress noise and interference.The clustering centers of the feature space,which can be thereference information for modulation classifier design,are determined by the fuzzyclustering algorithm.Finally,the non-linear dynamic modeling of an adaptive fuzzy modulation classifier,which based on the training mechanism within the neural network,was presented.Themodel adopted the parallel and hierarchical decision-based structure,which made thefeatures match the classifier and reduced the redundancy of the membership functions andfuzzy rules.The system with initial experience guarantees the controllability of theknowledge inference structure.By applying the training data,the algorithm adaptivelyadjusted and optimized the structure parameter and completed the approximation process.The simulation results verify the better adaptability and fault-tolerance of the system in thepresence of various environment parameters (SNR etc),as well as the improvement of theaverage probability of correct classification and the algorithm efficiency,compared with theneural network classifier or fuzzy classifier.