Cdma Multi-user Detection Method Based On The Algorithm To Be Eco-study

Code Division Multiple Access (CDMA) has more advantages than other access technologies, so it has already became the core technology of the third generation wireless communication systems, but these systems are all interference-limited, the system performance and capacity is limited by the Multi-Access Interference (MAI). Multi-user Detector (MUD) is the key interference cancellation technology of the wideband CDMA communication system, the optimal MUD can overcome the MAI theoretically, but it can not be carried out in real time on the current technology condition due to its high complexity, In resent years, there are many new intelligence MUD methods which utilize some neural networks and genetic algorithms and have lower computational complexity. At the same time, In the field of intelligent computation, there are several types of system optimization algorithms that have emerged, these are called Ecological System Optimization Algorithms (ESOAs), this category of novel optimization algorithms emulates the mechanism of natural ecological systems,(e.g. Immune Algorithms, Ant colony Algorithms etc.). There are several distinctive features in the ESOAs that are different from conventional optimization approaches, and they have already been used to solve many classical NP problems.The work of this thesis is a part of the Nature Science Research Project of Anhui Education Department, "Research on the optimization algorithms of NP-hard problem in CDMA multi-user detection with large user numbers". It deals with MUD problem with two kinds of typical new ESOAs, aimed to find out new methods of MUD. Based on their basic structures, some improvements and according application methods to these algorithms are proposed according to the practical problems emerging from Multi-user detection in CDMA communication systems. The validity of these algorithmshas been proven by case studies. The content of this thesis mainly includes:1. Analysis of the MUD problem in CDMA communication systems, coming out from the theory of computational complexity. The newly found Ecological System Optimization Algorithm is then introduced, and both of Ant Colony Optimization Algorithm and Particle Swarm Optimization Algorithm are compared and analyzed briefly.2. Ant Colony Optimization Algorithm for the MUD problem in CDMA communication systems. Based on the typical ant algorithm, our approach takes a pair ants meeting and search space partition mechanisms, and it is utilized to solve the MUD problem in CDMA communications. Analyses and simulation results show that our approach has polynomial computational complexity, and the Bit Error Rate (BER) performance of the algorithm is better than both the conventional detector and decorrelation detector.3. Particle Swarm Optimization Algorithm is a newly developed evolutionary algorithm, which is based on swarm intelligence, and has the properties to converge quickly, with simple rules, and has more applications in continuous space optimization. We study it on discrete space, and describe a discrete Particle Swarm Optimization Algorithm for the MUD problem in CDMA communication systems. Analyses and simulation results show our approach has lower computational complexity, and the BER performance of the algorithm is better than the conventional detector.4. Finally, the work of this thesis is summarized and the direction of further research on ESOAs, realization in the field of CDMA MUD and the simplification of the complexity of these algorithms is discussed.