Abstract
AbstractEqualization is an important application of the signal processing theory to communication theory, since its function is to cancel noise present in communication channels. The performance of an adaptive equalizer is influenced by the step size of an adaptive algorithm based on stochastic gradient used for updating the estimative of its weight vector. A good performance in terms of speed convergence and steady-state mean square error (MSE) of adaptive equalization can be obtained through of the variable step size. To this end, in this work, the adaptive equalizer design is performed via fuzzy variable step size–normalized least mean square (FVSS-NLMS) algorithm, whose variable step size is obtained through a Mamdani fuzzy inference system (MFIS). The adaptive equalization methodology was analyzed in three signal-to-noise ratio scenarios.KeywordsEqualizerFuzzy systemsNLMSStep sizeSignal processing
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