# Quantum Calculus-based Volterra LMS for Nonlinear Channel Estimation

arXiv: https://arxiv.org/abs/1908.02510 · HTML: https://mlsanity.com/paper/1908.02510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [q-RBFNN:A Quantum Calculus-based RBF Neural Network](https://mlsanity.com/paper/2106.01370/) (2021)

## Abstract (arXiv)

A novel adaptive filtering method called q-Volterra least mean square (q-VLMS) is presented in this paper. The q-VLMS is a nonlinear extension of conventional LMS and it is based on Jackson's derivative also known as q-calculus. In Volterra LMS, due to large variance of input signal the convergence speed is very low. With proper manipulation we successfully improved the convergence performance of the Volterra LMS. The proposed algorithm is analyzed for the step-size bounds and results of analysis are verified through computer simulations for nonlinear channel estimation problem.