Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This paper presents a novel approach to digital signal processing (DSP) utilizing reinforcement learning (RL). Traditional DSP algorithms are frequently designed with static parameters, rendering them ineffective when faced with dynamic or non-stationary signals. The proposed method leverages RL to train a DSP algorithm, allowing it to adapt its operational parameters in real-time based on incoming signal data. This dynamic adaptation aims to optimize performance metrics such as signal-to-noise ratio (SNR), root mean square error (RMSE), or spectral accuracy. The core mechanism involves an RL agent interacting with a simulated DSP environment, learning through trial and error to minimize a defined loss function related to the desired signal processing outcome. The framework is demonstrated conceptually, outlining the key components and potential benefits. Future research will focus on developing specific RL algorithms and evaluating the system's performance against established DSP techniques.
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