Abstract
A fuzzy-neural FN estimator of stochastic multi-input-multi-output MIMO wireless channels shows dependence on the membership and inference rule generation norms adopted. These two factors decide the ability of the fuzzy-estimator to capture the subtle variations in the input signal patterns and provide corresponding responses with optimum performance. They also determine the precision and processing speed of the FN-estimator while tackling stochastic behaviour of the MIMO channels. The membership and inference rule generation norms must not only capture the subtle variations but also should contribute towards lower bit error rate BER, reduced design and time complexity. Here, we propose the design of a fuzzy multilayer perceptron FMLP-based inference engine design for a FN-based MIMO modelling using multiple membership and inference rule generation norms. Experimental results derived show that a set formulated with seven linguistic hedges and six inference states helps in designing a FN MIMO estimator which can be an important element in the design of adaptive receivers for high data rate wireless communication.
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