Cellular network overload monitoring improved by stress testing with synthetic internet data
Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies
Networking and Internet ArchitectureArtificial IntelligenceMachine Learning
Summary
Cellular networks can get overwhelmed during emergencies, but detailed data needed to detect this is often hard to get due to privacy and cost. The authors use internet usage data as a simpler stand-in to spot when network parts are overloaded. They created a computer model trained on fake overload events and then tested it with realistic synthetic surges, finding that the usual alert settings fail in tricky cases. By retraining the model on these hard examples, alerts become reliable again, providing a practical way to check and improve emergency network monitors before real disasters happen.
What this means in practice
- •For cellular network operators: Improve emergency overload detection systems by stress testing with synthetic internet proxy data and adapting alert thresholds to maintain reliable alerts during real surges.
- •For network reliability engineers: Use a lightweight AI-based workflow with internet-only data to monitor, recalibrate, and adapt cellular overload detectors before deployment under real disaster conditions.
Authors
Bilal Hussain, Xiao Tang, Tan Li, Muhammad Azhar, Danista Khan, Fawad Ahmad
Abstract
Disasters can overload cellular control-plane signaling within minutes, yet fine-grained Radio Resource Control (RRC) or Next Generation (NG) Application Protocol (NGAP) telemetry is privacy-sensitive and costly to collect for analytics. Many emergency monitoring pipelines therefore rely on coarse Call Detail Record (CDR) aggregates. We treat Internet activity in CDR grids as a practical proxy for hidden signaling stress under that constraint. We train a lightweight convolutional neural network (CNN) on stylized overload injections, stress-test it with diffusion-synthesized surges that preserve normal traffic structure, and adapt the detector by retraining on hard synthetic samples. Under stress-test conditions, the default alert threshold fails even though receiver operating characteristic (ROC) curves stay strong: the detector still assigns overloaded cells a larger overload probability than normal cells, but those probabilities fall below the default cutoff 0.5 and are labeled normal, so the F1-maximizing threshold -- selected post hoc on the same stress-test grids (oracle $τ^*$) -- shifts by $0.32 \pm 0.03$ (operating-point drift). Across three random seeds, hard-sample adaptation raises thresholded performance (F1) from 0% (no alerts at the default cutoff 0.5 on any seed) to $85.67 \pm 14.37$% and ranking from ROC-AUC $0.886 \pm 0.040$ to $0.99996 \pm 0.00007$. Diffusion-synthesized surges expose threshold fragility that matched-condition training -- training and testing on the same stylized injections -- hides, and hard-sample adaptation restores usable alerts at the default cutoff. Together, these steps define a reusable pre-deployment stress test for emergency monitors. Internet-only CDR input further supports lightweight AI-native workflows that combine monitoring, recalibration, and adaptation.