Agentic Continual Adaptation: Enabling Lifelong Learning in Wireless Channel Estimation

As wireless networks evolve toward 6G and beyond, deep learning-based channel estimators face a fundamental challenge: their performance degrades when operating conditions deviate from training distributions. This phenomenon, known as distribution shift, manifests through temporal non-stationarity due to user mobility and operational regime changes across varying signal-to-noise ratios (SNR). Traditional sequential training approaches suffer from catastrophic forgetting, where adaptation to new conditions erases previously learned knowledge. This article introduces Agentic Continual Adaptation (ACA), a framework that formulates continual learning as reinforcement-based sequential decision-making to enable continuous adaptation without forgetting. ACA integrates three synergistic mechanisms: an anchor memory that detects forgetting in real-time through continuous performance monitoring on representative samples, a Reinforcement Learning (RL) agent that dynamically selects from seven adaptation strategies balancing plasticity and stability, and a forgetting-aware rollback mechanism that rejects parameter updates exceeding adaptive degradation thresholds. Experimental evaluation across ten SNR regimes demonstrates 4.3% average improvement in channel estimation accuracy with peak gains of 13.6% at challenging low-SNR conditions, while triggering rollback on only 7.5% of parameter updates. This work provides a practical framework for continual adaptation in next-generation wireless networks, addressing an important part of the gap between laboratory performance and real-world deployment.