Enhanced Channel Estimation for FBMC/OQAM Using M-IAM-LS-DNN Towards 6G

Alhaj, Nura A., Jamlos, Mohd Faizal, Manap, Sulastri Abdul, Bakhit, Abdelmoneim A., Hamdan Mohamed, Mosab and Gismalla, Mohammed S. M. (2026) Enhanced Channel Estimation for FBMC/OQAM Using M-IAM-LS-DNN Towards 6G. Wireless Personal Communications. ISSN 1572-834X

Preview

PDF Download (2MB) | Preview

Abstract

Filter bank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM) is a multicarrier modulation technique projected to replace orthogonal frequency division multiplexing (OFDM) in upcoming sixth-generation (6G) networks. However, because its orthogonality is confined to real-valued symbols, FBMC/OQAM suffers from intrinsic imaginary interference, which complicates channel estimation (CE) tasks. CE plays a crucial role in wireless communication systems; therefore, accurate CE is essential for next-generation networks, especially those supporting low-latency and vehicular applications. While evaluating channel characteristics accurately is critical, conventional CE methods are often inefficient. Recently, feedforward deep neural networks (DNNs) have garnered attention for their impressive performance in enhancing CE techniques. In this study, we propose and investigate a CE scheme based on neural networks, specifically the M-IAM-LS-DNN (Modified Interference Approximation Method Least Squares) approach, for FBMC/OQAM systems. This method uses neural networks to correct noise errors in LS channel estimation. According to simulation data, the suggested M-IAM-LS-DNN surpasses the conventional M-IAM-LS in terms of accuracy, by reducing the NMSE of the proposed M-IAM-LS-DNN for the 64 subcarriers scenario by 10% and the BER by 33% across a wide range of SNR levels.

Item Type:	Article
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords:	6G; Channel estimation; Deep neural network algorithms; Filter bank multicarrier; Offset quadrature amplitude modulation; Wireless communication
Subjects:	Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	13 May 2026 09:00
Last Modified:	13 May 2026 09:00
URI:	https://norma.ncirl.ie/id/eprint/9300

Actions (login required)

View Item