Study On Application Of Intelligence Computation On CDMA Multi-user Detector Design

It is evident that the 3rd Generation (3G) and beyond 4G (4G) mobile communication will greatly facilitate and enrich our work and daily life, with the rapid development of modern digital mobile communication techniques. In order to provide colorful multimedia service and high rate data service, the 3G and 4G Communication systems need higher wireless capacity and systems performance. Code-Division Multiple-Access (CDMA) mobile communications systems are interference-limited systems. Multiple access interference (MAI) is the main interference in the communications systems. It is important that the MAI is suppressed so that the system performance and capacity are increased. An efficient method suppressed MAI is multi-user detection (MUD) which views the MAI as an useful resource and makes full use of the relationship between users to increase the detection performance. So the MUD is one of key techniques in CDMA communications systems.Intelligence computation has shown many advantages over conventional optimization algorithm. MUD problem can be viewed as a combinational optimization problem. 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 (MAI) to reach low bit error rate (BER) and high near-far resistant capability with acceptable computation complexity. Our attention is focusing on the sub-optimal MUD algorithm development since the maximal likelihood detection (MLD) based optimal MUD has been shown to have the exponential computation complexity.The main contribution of this thesis can be summarized as follows:(1) At begin, we proposed a multi-user detector based on Hopfield neural network with gauss noise. Then based on evolutionary algorithm and Hopfield neural network, two hybrid algorithms were proposed: Hopfield neural network...