Neural Network Techniques for Multi-user Demodulation

| Adaptive methods for demodulating multiuser communication in a Direct-Sequence Spread-Spectrum Multiple-Access (DS/SSMA) environment are investigated. In this setting the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. 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 a RBF network when all of the system parameters are known. The RBF network's performance is compared with other multiuser detectors. The centers of the RBF neurons, when the system parameters are not fully known, are determined using clustering techniques. This work shows that the adaptive RBF network obtains near optimal performance and is robust in realistic communication environments.