Metamodel improves confidence in predicting next mobile location
Selective Next-Location Prediction with MetaModelSelect: A Lightweight Post-Hoc Confidence Metamodel
Machine LearningNetworking and Internet Architecture
Summary
Mobile networks try to guess where a phone will be next to make better decisions, but wrong guesses waste resources. The authors created a small extra model called MetaModelSelect to check if a phone location prediction is likely right, without changing the original prediction method. They tested it on real cellular data and found it ranks prediction confidence better than other ways. This means networks can trust the predictions more and avoid acting on poor guesses.
What this means in practice
- •For mobile network engineers: Improve decision-making by filtering reliable next-location predictions to reduce wasted network resources.
- •For telecom operations teams: Deploy confidence scoring to avoid acting on uncertain location predictions in cellular network management.
Tested on one dataset.
Authors
Calvin Jary, Nafiseh Kahani
Abstract
Next-location prediction can support proactive mobile-network decisions, but acting on unreliable predictions can waste resources. We apply MetaModelSelect, a lightweight post-hoc confidence metamodel, to selective cellular next-location prediction. The mobility-specific model combines a learned embedding of the predicted location with the four largest softmax probabilities and Softmax Concentration. It learns whether a frozen recurrent neural network's top-1 prediction is correct, using only its outputs and without retraining the backbone. On path-balanced Changchun cellular mobility trajectories, we evaluate backbones trained with cross-entropy (CE) and self-adaptive training plus entropy minimization (SAT+EM), comparing against eight scalar confidence scores, two location-aware lookup methods, and a raw-logit multilayer perceptron. Relative to maximum softmax probability, MetaModelSelect achieves mean per-run reductions in area under the risk--coverage curve (AURC), excess AURC (E-AURC), and area under the generalized risk--coverage curve (AUGRC) of 2.6%, 4.2%, and 3.0% for CE, and 2.0%, 3.3%, and 1.9% for SAT+EM. It also achieves the lowest reported mean risk at every evaluated partial coverage. The results show that MetaModelSelect improves confidence ranking in this mobility setting while leaving the underlying location predictor unchanged.