Continual Learning for Adaptive Beamforming in Dynamic 6G Environments
Dr. Osvaldo Simeone, Dr. Joonhyuk Kang
King's College London / KAIST
Abstract
This paper addresses the challenge of maintaining AI beamforming performance in dynamic 6G environments where channel statistics change over time. We propose a continual learning framework that adapts beamforming models to new environments without catastrophic forgetting of previously learned conditions. Our approach uses elastic weight consolidation combined with experience replay, maintaining beamforming performance within 1 dB of separately trained models across 10 different deployment scenarios.
AI Summary
- Continual learning framework for AI beamforming that adapts without catastrophic forgetting.
- Maintains performance within 1 dB of separately trained models across 10 scenarios.
- Combines elastic weight consolidation with experience replay.
- Enables long-term deployment of AI beamforming in changing environments.
Key Findings
- 1Standard fine-tuning degrades performance on previous environments by up to 5 dB.
- 2Continual learning reduces this degradation to less than 1 dB.
- 3Memory-efficient experience replay requires storing only 1% of past data.
Industry Implications
Solves a critical challenge for deploying AI in real-world 6G networks.
Enables AI models that improve over their entire deployment lifetime.
Reduces the need for periodic model retraining and redeployment.
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