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Channel Adaptive Equalizer Design Based on FIR Filter via FVSS-NLMS Algorithm

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Modeling, Simulation and Optimization

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 292))

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Abstract

Equalization 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.

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Authors and Affiliations

  1. Federal University of Maranhão, Graduate Program in Electrical Engineering, São Luís, 65080-805, Brazil

    Rodrigo Possidônio Noronha

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  1. Rodrigo Possidônio Noronha
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Correspondence to Rodrigo Possidônio Noronha .

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Editors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India

    Biplab Das

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, India

    Ripon Patgiri

  3. National Institute of Technology Silchar, Silchar, India

    Sivaji Bandyopadhyay

  4. Department of Automatics and Applied Software, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina Emilia Balas

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Noronha, R.P. (2022). Channel Adaptive Equalizer Design Based on FIR Filter via FVSS-NLMS Algorithm. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 292. Springer, Singapore. https://doi.org/10.1007/978-981-19-0836-1_35

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