Neural detection of QAM signal with strongly nonlinear receiver

Neural receiver structures have been developed for adaptive discrete-signal detection in telecommunication applications. Neural networks combined with conventional equalizers improve the performance especially in compensating for nonlinear distortions. These distortions may result, for instance, from nonlinear amplification implemented for reducing the power consumption. In this paper, the behavior of the neural receiver in multipath channel with additive white Gaussian noise has been investigated. The transmitted signal is quadrature amplitude modulated (QAM). A receiver structure based on self-organizing map (SOM) is compared with a conventional decision feedback equalizer (DFE).