When Does Spatial Cooperation Help? Contamination Regimes in Decentralized Online Spectrum Anomaly Learning
Alp Berke Ardic, Erdem Koyuncu
Abstract
Learning-based spectrum monitors adapt online to benign propagation changes, but persistent rogue transmissions can gradually become incorporated into the learned background. Spatially distributed receivers provide additional evidence for detecting such anomalies, while decentralized model mixing can also spread contamination to receivers with clean measurements. We study this tradeoff using a decentralized online learner with sample gating and model sharing. We show that, without defenses, contamination propagation is governed by the occupancy rate and is independent of cross-receiver correlation. In contrast, the benefit of spatial fusion depends strongly on correlation and the detector operating point, so cooperation can either improve or degrade detection. We also show that fragmented transmissions can evade gating based on temporally smoothed statistics. Combining smoothed and per-sample gating substantially improves robustness to such intermittent contamination.