Theory Study Of Intelligence Information For Mulitiuser Detection

With the rapid development of wireless mobile communication techniques, it is evident that the wireless mobile communication will greatly facilitate and enrich our work and daily life. In order to provide colorful multimedia service and high rate data service, the 3rd Generation (3G) and beyond 4th Generation (4G) mobile communication systems need higher wireless capacity and systems performance. It is known that Code-Division Multiple-Access (CDMA) mobile communication systems are severe interference-limited systems. Multiple access interference (MAI) and near far problem (NFP) are the main interference in the communications systems. It is important to suppress MAI and NFP of suppressing so that the system performance and capacity are increased. An efficient method suppressed MAI and NFP is multiuser detection (MUD) in which the MAI and NFP are viewed as useful information resource and the relationship between users is sufficiently used to improve the detection performance. So the multiuser detection is one of key techniques in CDMA communication systems.Optimal multiuser detection can not be implemented for its computation complexity of exponent. Since optimal multiuser detection problem of DS-CDMA and MC-CDMA can be viewed as a combinational optimization problem, intelligence computation can be used to resolve multiuser detection. This thesis is dedicated to the application of intelligence computational methods based on bionics to solve the difficult issue of MUD design capable of canceling the so-called multiple access interference and near far problem to reach low bit error rate (BER) with acceptable computation complexity. Our aim is focusing on the novel intelligence MUD algorithm development of DS-CDMA and MC-CDMA systems in Gassian noise and impulse noise environment. So we proposed a series of novel intelligence computation algorithms, and designed some multiuser detectors based on the proposed intelligence computation and classical problem.The main contribution of this thesis for multiuser detection and intelligence information processing can be summarized as follows:1. In order to control user powers, three direction finding problem are researched and corresponding objection functions are proposed. Three intelligence computation methods are designed to resolve corresponding objection functions:differential particle swarm optimization, cultural quantum algorithm and cultural bee colony algorithm. The three direction finding methods are not only effective for power control of multi-user detection technology, but also can be extended to other applications of finding technologies. The proposed cultural quantum algorithm based generalized weighted signal subspace fitting overcome some limitations the fourth-order cumulant-based methods. The proposed fractional lower order covariance subspace fitting method based on PSO algorithm based on differential particle swarm optimization is more suitable for impulsive noise environment. The proposed noncircular signal maximum likelihood method based cultural bee colony effectively used information of noncircular signals information.2. To resolve computation complexity of optimal multiuser detection in DS-CDMA systems, three intelligence computation frameworks are proposed. Based on every intelligence computation framework, a novel optimal multiuser detection based on intelligence computation is designed. Simulation results show that the three kinds of quasi-optimal multiuser detectors based on neural network particle swarm optimization, immune clonal quantum algorithm and clonal quantum algorithm have simple structure and good detection performance for different applications.3. Combined the advantages of artificial neural networks and quantum computation, we proposed a novel quantum neural networks and quantum chaotic neural network. The proposed neural network is the perfect combination of evolution mechanisms of quantum characteristics and neurons which lead to better detection performance. Based on the propose quantum neural network can not only design effective multiuser detection method, but also can be extend to some combinatorial optimization problem which can be solved by Hopfield neural network. Then, based on theory of quantum optimization and shuffled frog leaping, a quantum shuffled frog leaping is proposed and a multiuser detector based on the quantum shuffled frog leaping is designed.4. Based on the stochastic Hopfield neural networks, quantum neural network and two swarm intelligences, neural network fish school and immune ant colony are proposed in MC-CDMA systems model. The performance of the proposed detectors using neural network fish school and immune ant colony close to the optimal detector in multipath fading channel of MC-CDMA systems.5. Discussed multiuser detection model of the non-Gaussian noise in DS-CDMA and MC-CDMA systems, robust multiuser detections for different communications systems are presented. Combined DNA computing theory, swarm intelligence and the theory of the immune system, DNA clonal selection algorithm and DNA fish school algorithm are proposed. Then, three robust multiuser detectors are proposed for different noise environment.