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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 17 Issue 1, 2026.
Abstract: Generalized Frequency Division Multiplexing (GFDM) has broad application prospects due to its flexible subcarrier structure and low out-of-band leakage. Traditional channel estimation methods for GFDM systems rely on inserting a large number of pilot sequences, which reduces the data transmission rate. To address this problem, a channel estimation method for GFDM systems based on subcarrier correlation is proposed. First, according to the time–frequency characteristics of the prototype filter in the GFDM system, a pilot sequence with a two-dimensional time–frequency block structure (CTFP) is designed. This sequence is adjusted based on the parameters of the prototype filter. Then, the correlation among subcarriers is utilized for channel estimation, which effectively reduces the pilot overhead and improves the data transmission rate and interference resistance of the system. Simulation results show that under the same total time slot overhead, the mean square error and bit error rate performance of the proposed correlation-based methods are similar to those of existing methods, while the data transmission rate is improved by 14.97% compared with conventional methods.
Xiaotian Li, Xiaoqing Yan, Zitian Zhao and Jiameng Pei. “Correlation Characteristics-Based Channel Estimation Method for GFDM Systems”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170114
@article{Li2026,
title = {Correlation Characteristics-Based Channel Estimation Method for GFDM Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170114},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170114},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Xiaotian Li and Xiaoqing Yan and Zitian Zhao and Jiameng Pei}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.