It is well known that in the next generation (3G) cellular mobile communication systems the code-division multiple access (CDMA) has become the dominant technical standard and related key techniques, such as channel coding/decoding, multiple user detection (MUD), software radio as well as intelligent antenna, are attracting increasingly research interest in recent years. This thesis is dedicated to the application of computational intelligence methods 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 tabu search (TS) based MUD algorithm is firstly proposed, in which the output of a conventional detector is taken as the initial solution, and those points whose Hamming distance to the current solution is 1 are gathered into the neighborhood, then search results of each iteration are put into tabu list and make it tabu forever. This TS-MUD algorithm is shown to be near-far resistant and of low BER with polynomial computational complexity.(2) Two hybrid algorithms by merging the TS and Multi-Stage Detection (MSD) technique are developed: 1) the MSD is used on the output solutions with respect to each iteration of the TS procedure; 2) the MSD is embedded into the TS and used onto the neighborhood at each iteration. Performance improvement of the two algorithms are observed in comparative simulation experiments with respect to the above TS-MUD algorithm. (3) A sub-optimal MUD based on an artificial neural network (ANN) with tabu learning is proposed. In this algorithm, the MUD objective function is mapped onto the energy function of the ANN, a penalty section is added to the energy function according to the TS rule, upon which any solution search always towards the states that has not been visited. This procedure enables the state trajectory to climb out of local minima thereby to converge toward the optimal or a near-optimal solution. This algorithm, justified by simulation experiments, is extremely effective due to its global convergence capability together with square computational complexity.(4) By taking advantages of the GA (genetic algorithm) and HNN Hopfield neural network, a hybrid MUD algorithm is presented. In this detector, GA provides firstly an initial solution at first, upon which the HNN performs local optimization according to the steepest descent mechanism. This novel hybrid algorithm, featuring also merely square computational complexity, requires a much smaller population size and generation number as compared with the detector using GA alone. This fact makes it much efficient than that of GA based MUD and HNN based MUD.(5) A novel MUD based on a radial basis function neural network (RBFNN) trained by an adaptive projective learning algorithm 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. The proposed MUD algorithm is near-far resistant. Besides, the algorithm needs less a priori system information as compared with the detectors using K-Means Clustering RBFNN and Supervised Clustering RBFNN, but exhibits nearly the same good performance as that of the later RBFNN based detector. |