Research On Wireless Channel Estimation And Chaotic Time Series Prediction

In mobile communication system plenty of technics of statistic theory was used to solve some practical problems, such as channel estimation, signal detection, modulation identification. The whole system is becoming more and more complicated, however. Simpler and smarter solutions are required from the numerous datus. Statistical Learning Theory (SLT) has become more and more popular to people since it was founded by Dr. Vapnik. This framework tries to help us explore the nature and rule among the various data and phenomenon. Compared to the trational statistics, SLT focuses on the research of machine learning based on the small number of samples. It constructs a new framework about learning. In this framework, the rule of statistical reasoning will consider not only the demand of convergence, but also the best optimal result under the circumvent that the limited information can be used. This dissertation focuses on research of the SLT's application in some problems of communication system.The main contributions of this dissertation are achieved and listed as following:1,Having noticed that the chaotic characters lies in the FH-code, we build a LS-SVM model to predict the FH-code sequence. Because of the chaotic character of the FH-code, we utilized Taken's embedding theory and SVM technology to construct a mapping between the one dimension input vector and a high dimension feather space, to realize the data linear or nonlinear classification. Then LS method is applied to train the SVM model and the function of determined by the FH-code's attractor is regressed. Its performance is analyzed and some illustrative simulations are presented.2,Considering the static BP neural network's insufficiency when predicting the chaotic dynamics, we propose a new neuro network including the chaotic neurons and apply it to regress the chaotic dynamic system. We applied the neuro network to predict the Mackey-Glass chaotic series and the L-K FH-code sequence and evaluate the performance of algorithm through the simulations.3,Once little prior samples are available, we utilize the modern statistic learning theory to solve the channel estimation problem. Due to the complication of MIMO nonlinear channel, the performance of channel estimation decreases on accuracy and speed with the nonlinear and nonstationary condition. In the circumstance of little amount of samples to use, this problem is more obvious. The current nonlinear channel estimation method lies in SISO channel or stantionary MIMO channel estimation. Although some methods treat the nonlinear time-variant channel as stationary in a sufficient short time period, the performance in convergenc and accuracy is still unsatisfying. We utilize the LS-SVM to convert the MIMO channel estimation problem to a multi-dimension channel function regression problem, and apply the AM-SVR framework to MIMO channel adaptive estimation. The simulation results demonstrate this method is efficient.4,Without the prior training samples in the non-cooperation condition, an approach for solve the channel estimation and equalization has been presented. This algorithm is based on the finite prior statistical character of received signal, and overcomes the shortcoming in slow convergence which exists in blind channel estimation. A relative weighted method is applied to train this SVM and reduces the amount of calculation in progress. The performance of blind channel estimation is evaluated by MATLAB simulation.5,An improved and more complex Blind Signal Seperation (BSS) algorithm for separating linear convolved mixtures of nonstationary signals in FHSS system is presented. This algorithm relies on the nonstationary nature of the sources to achieve separation, which assumes statistically stationary sources as well as instantaneous mixtures of signals. In practice, the FH/CDMA sources received are nonstationary and linear convolute mixing. A more complex BSS algorithm is required to achieve better source separation. The algorithm is based on minimizing the average squared cross output channel correlation. The mixture coefficients are totally unknown, while some knowledge about temporal model is available. At the same time, an improved MMI algorithm was applied to the BSS of the multiple FHSS signals. The simulation results show the effectiveness of the method in the blind detection of the multiple FHSS signals.