Neural network techniques for multi-user demodulation

Adaptive methods for demodulating multi-user communication in a direct-sequence spread-spectrum multiple-access (DS/SSMA) environment are investigated. Adaptive radial basis function (RBF) networks that operate with knowledge of only a subset of the system parameters are studied. This approach is further bolstered by the fact that the optimal detector in the synchronous case can be implemented by an RBF network when all of the system parameters are known. The RBF network performance is compared with other multi-user detectors. The centers of the RBF neurons, when the system parameters are not fully known, are determined using clustering techniques. It is shown that the adaptive RBF network obtains near optimal performance and is robust in realistic communication environments