arXiv:2506.06323v2 Announce Type: replace-cross Abstract: Model-free and reinforcement learning-based adaptive filtering methods are gaining traction for denoising in dynamic, non-stationary environments such as wireless signal channels, biomedical monitoring, and sensor networks. Traditional filters such as LMS, RLS, Wiener, and Kalman are often limited by assumptions of stationarity, the need for exact noise statistics, or fragile parameter tuning. This paper proposes an adaptive filtering framework using Proximal Policy Optimization (PPO), guided by a composite reward that balances SNR impr
The increasing complexity and dynamism of environments like wireless communication and biomedical monitoring demand more robust and adaptive filtering solutions than traditional methods can provide.
This development allows for significant improvements in signal processing in critical non-stationary environments, directly impacting real-world applications requiring high fidelity and adaptability.
Adaptive filtering systems can now leverage reinforcement learning with composite rewards to overcome fundamental limitations of traditional filters, leading to more resilient and intelligent signal denoising.
- · Telecommunications companies
- · Medical device manufacturers
- · Sensor network developers
- · AI/ML research institutions
- · Legacy adaptive filter algorithm developers
- · Systems heavily reliant on stationary environment assumptions
Improved signal quality and reliability in dynamic systems become achievable with less manual tuning.
This advancement could accelerate the development of more autonomous and robust systems across defense, healthcare, and industrial IoT sectors.
The integration of reinforcement learning into fundamental signal processing could lead to a paradigm shift in how complex data streams are managed and interpreted.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG