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

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.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) A MUD based clonal selection algorithm is proposed. At first , clonal selection algorithm is improved, in which maturation is finished by genetic operator and gauss mutation operator .Then we designed a MUD based CSA which used MSD operator. This CSA-MUD algorithm is shown to be near-far resistant and of low BER with polynomial computational complexity.(2) By taking advantages of the clonal selection algorithm(CSA) and discrete Hopfield neural network, a hybrid MUD algorithm is presented. In this detector, CSA provides firstly an initial solution at first, upon which the DHNN performs local optimization according to the steepest descent mechanism.(3) Based on evolutionary algorithm and Hopfield neural network, three hybrid algorithms was proposed:genetic Hopfield neural network ,evolutionaryprogramming Hopfield neural network, immune Hopfield neural network, then we used the proposed algorithms to design new multi-user detectors .At last, we proposed a multi-user detector based on Hopfield neural network with gauss noise.(4) A novel discrete particle swarm optimization algorithm which is simple and efficacious is proposed. Then a new MUD is designed based on NDPSO algorithm.(5) Two hybrid algorithms by merging the NDPSO and Multistage Detection (MSD) technique are developed. One of methods is that the NDPSO is used as the first stage of the MSD to provide a good initial point for successive stages of the MSD, the other is that the MSD is embedded into the NDPSO at each generation. Such a hybridization of the NDPSO with the MSD reduces its computational complexity by providing faster convergence .In addition, a better initial data estimate supplied by the NDPSO improves the performance of the MSD ,and the embedded MSD improves the performance of the NDPSO .(6) A novel MUD based on a radial basis function neural network (RBFNN) trained by a particle swarm optimization algorithm and traditional method is proposed. Taking only a group,of samples of received signal, this approach can identify the number of RBF function, the centers and the weights of the RBFNN.