A Data-Driven Performance Prediction Approach for Cellular Network Parameter Setting via Factorization Machine

Cellular network performance depends heavily on the setting of its network parameters. Current practice of parameter setting relies largely on expert experience, which is often suboptimal and time-consuming. Therefore, how to find the optimal parameters automatically is increasingly concerned by network operator. This article proposes a collaborative learning approach to predict the performance of network elements under different parameters based on factorization machine. The proposed approach captures the potential time-aware correlation between network elements and their parameters to boost the accuracy and efficiency of performance prediction. Extensive experiment results demonstrate that the proposed approach can significantly improve the effectiveness of performance prediction, based on a large-scale real-world network dataset collected from a metropolitan LTE network.