Non-linear channel equalization using computationally efficient neuro-fuzzy channel equalizer

The paper investigates the problem of channel equalization in digital cellular radio (DCR). These channels are affected by intersymbol interference (ISI) with non-linearity in the presence of additive white Gaussian noise (AWGN). We propose a computationally efficient neuro-fuzzy system based equalizer for use in communication channels with these anomalies. This equalizer performs close to the optimum maximum a-posteriori probability (MAP) equalizer with a substantial reduction in computational complexity and can be trained with a supervised scalar clustering algorithm. These features can make the equalizer very suitable for mobile communication applications. Simulation studies indicate that this equalizer performs close to the optimal equalizer.