Causal online learning of safe regions in cloud radio access networks
File(s)
Published online version
Author(s)
Hammar, Kim
Alpcan, Tansu
Lupu, Emil C
Type
Journal Article
Abstract
Cloud radio access networks (RANs) enable cost-effective management of mobile networks by dynamically scaling their capacity on demand. However, deploying adaptive controllers to implement such dynamic scaling in operational networks is challenging due to the risk of breaching service agreements and operational constraints. To mitigate this challenge, we present a novel method for learning the safe operating region of the RAN, i.e., the set of resource allocations and network configurations for which its specification is fulfilled. The method, which we call (C)ausal (O)nline (L)earning, operates in two online phases: an inference phase and an intervention phase. In the first phase, we passively observe the RAN to infer an initial safe region via causal inference and Gaussian process regression. In the second phase, we gradually expand this region through interventional Bayesian learning. We prove that COL ensures that the learned region is safe with a specified probability and that it converges to the full safe region under standard conditions. We experimentally validate COL on a 5 G testbed. The results show that COL quickly learns the safe region while incurring low operational cost and being up to 10× more sample-efficient than current state-of-the-art methods for safe learning.
Date Issued
2026-08-19
Date Acceptance
2026-08-01
Citation
IEEE Transactions on Mobile Computing, 2026
ISSN
1536-1233
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
1
End Page
18
Journal / Book Title
IEEE Transactions on Mobile Computing
Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0
License URL
Publication Status
Published online
Date Publish Online
2026-08-19