This work presents an innovative approach to estimating Carrier Frequency Offset (CFO) in Wireless Sensor Networks (WSNs) by integrating deep learning with fuzzy logic. The proposed method combines feature extraction, fuzzy inference, and a deep neural network to deliver highly accurate CFO estimates across diverse channel and noise conditions, significantly outperforming conventional techniques in low-SNR scenarios. CFO estimation is critical for synchronization and reliable wireless communication. While fuzzy logic enhances decision-making and energy efficiency, deep learning offers superior resilience in pattern recognition in WSN Technology. Few existing strategies merge these techniques for real-time CFO estimation under challenging conditions. Following initial CFO detection using the preamble, the method performs multi-parameter feature extraction, integrating the outputs of a GRU-based deep learning network with fuzzy inference for optimal estimation. Simulation and hardware results demonstrate over 70% error reduction, maintaining accuracy above 90% across varying SNRs and channel environments, with strong robustness to mobility and noise.
M. Prabhu (2026) studied this question.