Continual Learning for Wireless Channel Prediction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909664400113664 |
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| author | Mohsin, Muhammad Ahmed Umer, Muhammad Bilal, Ahsan Jamshed, Muhammad Ali Cioffi, John M. |
| author_facet | Mohsin, Muhammad Ahmed Umer, Muhammad Bilal, Ahsan Jamshed, Muhammad Ali Cioffi, John M. |
| contents | 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. The full codebase can be found at https://github.com/ahmd-mohsin/continual-learning-channel-prediction.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continual Learning for Wireless Channel Prediction Mohsin, Muhammad Ahmed Umer, Muhammad Bilal, Ahsan Jamshed, Muhammad Ali Cioffi, John M. Signal Processing Networking and Internet Architecture 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. The full codebase can be found at https://github.com/ahmd-mohsin/continual-learning-channel-prediction.git. |
| title | Continual Learning for Wireless Channel Prediction |
| topic | Signal Processing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2506.22471 |