Temporal-correlation Modeling for Improved CFO Estimation: The BiModule CFO Estimation (BMCE) Framework

The development of beyond-fifth-generation (B5G) communication systems introduces challenges in maintaining timing and frequency synchronization, especially in low SNR and extended coverage scenarios. Accurate carrier frequency offset (CFO) estimation is crucial for establishing calls under such conditions. Existing methods, like maximum likelihood estimation, have limitations, while machine learning (ML) techniques have shown promise in wireless communication. In this work, we propose an ML-based approach using Long Short-Term Memory (LSTM) neural networks and automated machine learning (AutoML) to tune hyperparameters and improve CFO estimation accuracy. We compare our model with a gradientboosting machine (GBM) approach and demonstrate superior accuracy. Our research addresses CFO estimation challenges in B5G systems and offers valuable insights for the development of robust techniques in advanced communication systems.