Generalized Neuron Based Digital Communication Channel Equalizer

Equalization is necessary in digital communication system to mitigate the effect of intersymbol interference (ISI) and other nonlinear distortions. In order to reduce complexity the application of generalized neuron (GN) to adaptive channel equalization in a digital communication system with duo-binary signals is investigated. It uses only a single GN thus there is no problem of selection of initial architecture of the neural network giving optimum performance. Low complexity and fast convergence characteristic of GN based equalizer make it suitable for real time application. Bit error rate (BER) over a wide range of signal to noise ratio (SNR) is noted. It has been shown that BER performance approaches to optimal Bayesian solution.