Adaptive Fourier Transform Processing: A Deep Reinforcement Learning Approach for Effective Signal Processing

Abstract The Fourier transform is essential, in signal processing as it converts signals from the time domain to the domain. Our study introduces a reinforcement learning technique to optimize Fourier transform parameters. By automating the selection of window length and wavenumber parameters our method enhances the accuracy and efficiency of signal processing. We extensively evaluate this approach using input signals and compare its performance with established methods. Our experiments reveal a reduction in error (MSE) indicating that our method achieves MSE values for various signal types compared to existing techniques. Importantly our approach demonstrates reliability under noise levels and signal characteristics. This innovative strategy provides a framework for signal processing allowing interpretations for applications in fields such as communications, biomedical engineering and audio processing. The integration of reinforcement learning represents progress, in automating and improving signal processing techniques.