الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Hussain, B., Tang, X., Li, T., Azhar, M., Khan, D., & Ahmad, F. (2026). Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies. https://omanscience.com/ar/articles/diffusion-based-stress-testing-of-overload-monitoring-for-resilient-emergency-cellular-networks-using-internet-cdr-proxies
MLA 9
Hussain, Bilal, et al. "Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies." https://omanscience.com/ar/articles/diffusion-based-stress-testing-of-overload-monitoring-for-resilient-emergency-cellular-networks-using-internet-cdr-proxies.
شيكاغو (المؤلف–التاريخ)
Hussain, Bilal, Xiao Tang, Tan Li, Muhammad Azhar, Danista Khan, and Fawad Ahmad. 2026. "Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies." https://omanscience.com/ar/articles/diffusion-based-stress-testing-of-overload-monitoring-for-resilient-emergency-cellular-networks-using-internet-cdr-proxies.
هارفارد
Hussain, B., Tang, X., Li, T., Azhar, M., Khan, D. and Ahmad, F. (2026) 'Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies', Available at: https://omanscience.com/ar/articles/diffusion-based-stress-testing-of-overload-monitoring-for-resilient-emergency-cellular-networks-using-internet-cdr-proxies.
فانكوفر
Hussain B, Tang X, Li T, Azhar M, Khan D, Ahmad F. Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies. https://omanscience.com/ar/articles/diffusion-based-stress-testing-of-overload-monitoring-for-resilient-emergency-cellular-networks-using-internet-cdr-proxies
IEEE
B. Hussain, X. Tang, T. Li, M. Azhar, D. Khan, and F. Ahmad, "Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies," https://omanscience.com/ar/articles/diffusion-based-stress-testing-of-overload-monitoring-for-resilient-emergency-cellular-networks-using-internet-cdr-proxies.