Multiuser Detection Algorithms, Power Control Algorithms And DOA Estimation Algorithms Based On SVM Methods

The goal of wireless communications techniques is to offer largest capacity and best quality of service for users. In CDMA systems, the traditional resources that have been used to add capacity to wireless systems are radio bandwidth and transmitter power. However, because the useful radio spectrum is limited and mobile or other portable device requires the use of battery power, which is also limited, these two resource are the most severely limited in modern wireless networks. To increase the capacity of CDMA systems, the most important thing is to suppress the interference using the limited resources. Compared with FDMA or TDMA systems, CDMA systems have many advantages, such as more efficient usage of spectrum, soft capacity, security, higher potential capacity and so on. However, it also suffers from multiple access interference and near far effect, which limit its capacity severely. The multiuser detection, power control and smart antenna technique can be used as power methods to suppress the interference, hence increasing the capacity of CDMA systems.Support vector machine (SVM) is one of the most effective machine learning methods, which are based on principles of structural risk minimization and statistical learning theory. Compared with the traditional learning machine, its generalization ability and nonlinear-spread ability are much more better, moreover, the process of its convergence has no local extremum. Over the past decades, SVM has been applied to many research domain with the development of the machine learning theory. Recently, it has become a new technique for signal processing of wireless communication system. Researchers classify the received signals with SVM, which makes the system detect the deisred mobile's signal correctly, estimate the transmitting power and directions of arrival(DOA) accurately, hence increasing the capacity of CDMA systems and improving the quality of service.In this dissertation, we focus on the advanced signal processing method in CDMA systems based on machine learning methods. The dissertation can be divided into four parts: 1) fast online training of support vector classification; 2) multiuser detection algorithm based on machine learning methods; 3) power control algorithm based on machine learning methods; 4) the estimation of DOA based on machine learning methodsIn the conventional SVM, training data are supplied and computed in batch by solving the quadratic programming problem, therefore, it is time consuming to classify a large data set and can not satisfy the demands of online application, such as the signal processing problem in CDMA systems, which needs periodically retraining because of the update of the training data. A fast on online training of support vector classification(FOSVC) algorithm is proposed in this dissertation to speeds up the training phase by reducing the size of training sample set using K.K.T. conditions. Simulation results show that the FOSVC outperform the other SVMs in term of the number of SVs and training time while keeping the comparable classification errors.the object of multiuser detection is to estimate the transmitted signals accurately as the received signals are known. This is a typical binary classification problem. We can classify the transmitted signals into two classes, one represents +1 and another represents -1. The runtime of conventional SVM-MUD is too long to satisfy the requirement of real-time application. To solve this problem, a multiuser detection method based on...