Two novel blind CFO estimation techniques for CP-OFDM

In this paper two new Cyclic Prefix (CP) based blind Carrier Frequency Offset (CFO) estimations for Orthogonal Frequency Division Multiplexing (OFDM) transmission over multipath channels are proposed. In doing so, we first estimate the maximum delay of the fading channel. We borrow the concept of remodulation introduced in earlier works and use the repetitive structure of CP to calculate a maximum-likelihood-based measure. In the first proposed method we use Particle Swarm Optimisation (PSO) aided search on all possible samples to find the optimal set. This technique provides performance improvement at the expense of more complexity. Then, in a second proposed method we average over the optimal set of samples to estimate CFO. The second technique provides a major improvement over previous works while offering less complexity. Simulation results corroborate that both our proposed methods significantly decrease the Mean Square Error (MSE).

Authors

Institutions

Publication Details

Journal
International Journal of Wireless and Mobile Computing
Published
2025-01-01
DOI
https://doi.org/10.1504/ijwmc.2025.145422
Primary Topic
Blind Source Separation Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Two novel blind CFO estimation techniques for CP-OFDM

Javad Kazemitabar, Hossein Miar Naimi, Mohammadreza Janbazi Roudsari
International Journal of Wireless and Mobile Computing
Blind Source Separation Techniques
article

Two novel blind CFO estimation techniques for CP-OFDM

Javad Kazemitabar, Hossein Miar Naimi, Mohammadreza Janbazi Roudsari
article en

Abstract

In this paper two new Cyclic Prefix (CP) based blind Carrier Frequency Offset (CFO) estimations for Orthogonal Frequency Division Multiplexing (OFDM) transmission over multipath channels are proposed. In doing so, we first estimate the maximum delay of the fading channel. We borrow the concept of remodulation introduced in earlier works and use the repetitive structure of CP to calculate a maximum-likelihood-based measure. In the first proposed method we use Particle Swarm Optimisation (PSO) aided search on all possible samples to find the optimal set. This technique provides performance improvement at the expense of more complexity. Then, in a second proposed method we average over the optimal set of samples to estimate CFO. The second technique provides a major improvement over previous works while offering less complexity. Simulation results corroborate that both our proposed methods significantly decrease the Mean Square Error (MSE).

International Journal of Wireless and Mobile ComputingVol. 28(3)
Babol Noshirvani University of Technology (IR)
Openalex Percentile: Top 93%
Blind Source Separation Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.