Abstract
In Orthogonal Frequency Division Multiplexing (OFDM) and Multiple-Input Multiple-Output-OFDM (MIMO-OFDM) systems, estimating Carrier Frequency Offset (CFO) is a critical challenge, particularly in degraded channel conditions where traditional methods struggle with precision and adaptability. This comparative study views various existing CFO estimation techniques and identifies three conventional methods-CFOest, CC, and AF-as benchmarks. To enhance estimation accuracy, a machine learning-based approach is proposed to effectively function across different channel conditions. Three distinct CFO estimators are developed using Kernel Support Vector Machine (KSVM), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN), as this is a common strategy in machine learning for identifying optimal solutions. A comparative analysis of their performance demonstrates that the proposed approach outperforms traditional techniques by achieving lower Root Mean Square Error (RMSE), with the ANN-based CFO estimator performing best in larger estimation ranges, while the KSVM-based estimator excels in smaller ranges. To further enhance accuracy, a novel three-step machine learning-based approach is proposed, demonstrating significant improvements in accuracy through subsequent simulations when contrasted with conventional methods and single-step models.