• Electrical Engineering and Systems Science > Systems and Control [Submitted on 18 Feb 2026] Title:Collaborative Safe Bayesian Optimization View PDF HTML (experimental)Abstract:Mobile networks require safe optimization to adapt to changing conditions in traffic demand and signal transmission quality, in addition to improving service performance metrics. • With the increasing complexity of emerging mobile networks, traditional parameter tuning methods become too conservative or complex to evaluate. • For the first time, we apply safe Bayesian optimization to mobile networks. • Moreover, we develop a new safe collaborative optimization algorithm called CoSBO, leveraging information from multiple optimization tasks in the network and considering multiple safety constraints. • The resulting algorithm is capable of safely tuning the network parameter online with very few iterations. • We demonstrate that the proposed method improves sample efficiency in the early stages of the optimization process by comparing it against the SafeOpt-MC algorithm in a mobile network scenario.
Article Summaries:
- Researchers have introduced the first application of safe Bayesian optimization to mobile network parameter tuning. The new algorithm, CoSBO, jointly exploits data from multiple optimization tasks while respecting several safety constraints, enabling online tuning with very few iterations. Compared with the existing SafeOpt‑MC method, CoSBO demonstrates superior sample efficiency during the early stages of the optimization process in a realistic mobile‑network scenario. This development addresses the growing complexity of emerging networks, where traditional tuning approaches are either overly conservative or computationally burdensome, and offers a more practical, data‑driven solution for maintaining service performance while ensuring safety.
Sources: