Benchmarking New Adaptive Equalizers Leveraging a Different Robust Adaptive Algorithms: NLMS, APA, PAP, and MRNQ
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Context: In the literature, numerous adaptive equalization techniques have been developed to effictively reduce inter-symbol interference in digital communication systems. Objective: This study conducts a comparative analysis of four such adaptive algorithms: NLMS, APA, PAP, and MRNQ, implemented within a decision feed-forward equalizer (DFE) stucture. The purpose of this evaluation is to identify the strengths and limitations of each algorithm under the same transmission conditions. The insights gained from this comparaison can guide the choice of equalization strategy for practical implementations in modern communication systems. Method: The performance of each equalizer is assessed using multiple evaluation criterion, including computational complexity, constellation diagram analysis, the Nyquist criterion, and mean squared error (MSE) performance. Results: All four algorithms effectively reduce inter-symbol interference and ensure proper signal equalization. However, DFE-PAP and APA-DFE show superior speed and accuracy, MRNQ-DFE offers a good trade-off, and NLMS-DFE performs the weakest. Conclusions: In summary, the study highlights DFE-PAP as the most efficient and pratical solution, combining strong ISI mitigation with low computationnal cost. While all algorithms perform well, DFE-PAP and APA-DFE demonstrate clear advantages in convergence speed and accuracy.
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