Quantum Calculus-based Volterra LMS for Nonlinear Channel Estimation

Usman, Muhammad; Ibrahim, Muhammad Sohail; Ahmed, Jawwad; Hussain, Safdar; Moinuddin, Muhammad

doi:10.1109/intellect47034.2019.8955461

Cited by 6 publications

(1 citation statement)

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“…The method is shown to achieve a higher convergence rate, compared to the conventional LMS, while maintaining competitive performance. In [16,17,18,19,20,21], adaptive techniques are further proposed for the q parameter. The q-LMS algorithm also been successfully implemented for system identification, unconstrained optimization, neural networks and the design of whitening filters tasks.…”

Section: Introductionmentioning

confidence: 99%

q-LMF: Quantum Calculus-Based Least Mean Fourth Algorithm

Sadiq

Usman

Khan

et al. 2019

Advances in Intelligent Systems and Computing

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Channel estimation is an essential part of modern communication systems as it enhances the overall performance of the system. In recent past a variety of adaptive learning methods have been designed to enhance the robustness and convergence speed of the learning process. However, the need for an optimal technique is still there. Herein, for non-Gaussian noisy environment we propose a new class of stochastic gradient algorithm for channel identification. The proposed q-least mean fourth (q-LMF) is an extension of least mean fourth (LMF) algorithm and it is based on the q-calculus which is also known as Jackson derivative. The proposed algorithm utilizes a novel concept of error-correlation energy and normalization of signal to ensure high convergence rate, better stability and low steady-state error. Contrary to the conventional LMF, the proposed method has more freedom for large step-sizes. Extensive experiments show significant gain in the performance of the proposed q-LMF algorithm in comparison to the contemporary techniques.

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Section: Introductionmentioning

confidence: 99%