Probabilistic Neural Network Implementation of the Optimum CDMA Multi-user Detector * and Prof.

Ibikunle, F. and Yixin, Zhong Probabilistic Neural Network Implementation of the Optimum CDMA Multi-user Detector * and Prof. Beijing University of Posts & Telecommunications, 100088 Beijing, China.

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Abstract

Application of Neural Network to signal detection in CDMA multi-user communications Gaussian channel is investigated, This paper is motivated by the fact that, in a multi-user CDMA system, the conventional receiver suffers severe performance degradation as the relative powers of the interfering signals become large (i.e., "near-far problem"). Furthermore, in many cases the optimum receiver, which alleviates the near-far problem, is too complex to be of practical use. And by viewing this optimum multi-user detector problem in CDMA channel as an optimum nonlinear classification decision problem, we apply the Probabilistic Neural Network algorithm which has the abilities of arbitrary nonlinear transformations, adaptive learning and tracking to implement this classification decision optimally and adaptively. The performance of the proposed neural detector is evaluated via computer simulations in terms of probability of detection, and it is compared with those of the existing neural and conventional detector schemes in a multi-user environment.

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