Adaptive detection using self-organizing map in 16 QAM system

A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equalizer structures utilizing neural computation have been developed for compensating for nonlinear channel distortion. We propose a neural detector based on self-organizing map (SOM) in a 16 QAM system. The proposed scheme uses the SOM algorithm and symbol-by-symbol detector to form a neural detector, and it adapts well to the changing channel conditions because of the topology-preserving property of the SOM algorithm. According to the theoretical analysis and computer simulation results, the proposed scheme is shown to have a better performance than the traditional linear equalizer when faced with nonlinear distortion