# Continual Learning for Wireless Channel Prediction

arXiv: https://arxiv.org/abs/2506.22471 · HTML: https://mlsanity.com/paper/2506.22471/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity](https://mlsanity.com/paper/2602.18151/) (2026)
- [Adaptive Learning Strategies for AoA-Based Outdoor Localization: A Comprehensive Framework](https://mlsanity.com/paper/2605.05055/) (2026)

## Abstract (arXiv)

Modern 5G/6G deployments routinely face cross-configuration handovers--users traversing cells with different antenna layouts, carrier frequencies, and scattering statistics--which inflate channel-prediction NMSE by 37.5% on average when models are naively fine-tuned. The proposed improvement frames this mismatch as a continual-learning problem and benchmarks three adaptation families: replay with loss-aware reservoirs, synaptic-importance regularization, and memory-free learning-without-forgetting. Across three representative 3GPP urban micro scenarios, the best replay and regularization schemes cut the high-SNR error floor by up to 2~dB (≈ 35%), while even the lightweight distillation recovers up to 30% improvement over baseline handover prediction schemes. These results show that targeted rehearsal and parameter anchoring are essential for handover-robust CSI prediction and suggest a clear migration path for embedding continual-learning hooks into current channel prediction efforts in 3GPP--NR and O-RAN. The full codebase can be found at https://github.com/ahmd-mohsin/continual-learning-channel-prediction.git.