Composite Reward Design in PPO-Driven Adaptive Filtering
This work addresses robust, low-latency signal filtering for applications such as wireless communications, but it is incremental as it builds on existing reinforcement learning methods.
The paper tackled adaptive filtering for denoising in dynamic environments by proposing a PPO-driven framework with a composite reward, achieving real-time performance and outperforming classical filters like LMS and Kalman.
Model-free and reinforcement learning-based adaptive filtering methods are gaining traction for denoising in dynamic, non-stationary environments such as wireless signal channels. Traditional filters like LMS, RLS, Wiener, and Kalman are limited by assumptions of stationary or requiring complex fine-tuning or exact noise statistics or fixed models. This letter proposes an adaptive filtering framework using Proximal Policy Optimization (PPO), guided by a composite reward that balances SNR improvement, MSE reduction, and residual smoothness. Experiments on synthetic signals with various noise types show that our PPO agent generalizes beyond its training distribution, achieving real-time performance and outperforming classical filters. This work demonstrates the viability of policy-gradient reinforcement learning for robust, low-latency adaptive signal filtering.