We present a reinforcement learning (RL) framework that treats RF denoising as a sequential decision-making
problem within a cognitive radio environment. Unlike classical
static or heuristic filtering, the agent learns policies that adaptively select FFT-domain actions to preserve spectrum health
under dynamic conditions, including low-SNR regimes and adversarial jammers. Reward shaping is based on physical-layer
metrics—time-difference-of-arrival (TDoA) residual error and
correlation entropy—bridging ML objectives with RF system
performance. Through simulation, we show that RL agents
converge rapidly, achieving up to 35–45% reductions in TDoA
residuals and consistently outperforming hand-tuned filters. Beyond denoising, we argue the same policy framework generalizes
to broader cognitive radio functions such as channel selection,
adaptive beamforming, and interference mitigation. This work
highlights reinforcement learning as a viable control primitive
for autonomous RF sensing and spectrum management.