Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process

In the era of 5G and beyond, heterogeneous network orchestration has become a tremendous issue. The dilemma facing future systems is how to allocate integrated resources to satisfy multifarious services, which is an imperative but arduous task in forming a systematic mathematical model and quantifying the model with its multi-layer uncertainty characteristics. Aiming at the statistical representation and optimization in multimodal heterogenous networks for 5G and beyond, we propose a novel hybrid probability process (HPP) as a generalized surrogate model and a weighted degenerated upper confidence bound (WDUCB) criterion for Bayesian optimization (BO). We apply the proposed HPP-WDUCB combination to our developed simulation platform and configure several applications of the integration of space information network in next generation communication systems. And we compared the proposed method with other surrogate models and acquisition strategies from a range of perspectives. The experiment results yield significant applicability and excellent performance in multimodal system representation and optimization which provides an effective statistical modeling and orchestration references for network tuning.