Analysis of a neural detector based on self-organizing map in a 16 QAM system

A signal suffers from nonlinear, linear, and additive distortion when trans mitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equal izer structures utilizing neural computation have been developed for compen sating for nonlinear channel distortion. In this paper, we propose a neural detector based on self-organizing map (SOM) in a 16 QAM system. The propos ed scheme uses the SOM algorithm and symbol-by-symbol detector to form a ne ural detector, and it adapts well to the changing channel conditions, inclu ding nonlinear distortions because of the topology-preserving property of t he SOM algorithm. According to the theoretical analysis and computer simula tion results, the proposed scheme is shown to have better performance than traditional linear equalizer when facing with nonlinear distortion.