RoME-QCD: Robust and Measurement Efficient Quickest Change Detection in 5G Networks

To effectively monitor a network and verify its performance, it is essential to quickly detect sudden changes in its state, even when the form of such a change is initially unknown. While classical quickest change detection methods are potentially useful, they rely on probing the network state periodically, which in turn, may induce high measurement costs. In this paper, we extend existing frameworks in quickest change detection to allow for both adaptive measurement periods and unknown post-change measurement distribution. In our extended framework, the agent decides both when to raise an alarm and when to take the next measurement (if any), maintaining a trade-off between detection delay, false alarm rate and measurement costs. We evaluate both classical methods with periodic measurements as well as our adaptive scheme, called RoME-QCD (Robust and Measurement Efficient Quickest Change Detection). We demonstrate the latter's superiority analytically and verify this observation via numerical experiments, both using one-way delay data from a 5G testbed and synthetically generated data.