Skip to yearly menu bar Skip to main content


Poster
in
Workshop: Machine Learning for Wireless Communication and Networks (ML4Wireless)

Continual Learning for Wireless Channel Prediction

Muhammad Mohsin · Muhammad Umer · Ahsan Bilal · Muhammad Ali Jamshed · John Cioffi

[ ] [ Project Page ]
Fri 18 Jul 11 a.m. PDT — noon PDT

Abstract: 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($\approx$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.

Chat is not available.