CSDNet: causal inference aided handover parameter adjusting effect estimation in cellular networks

Abstract

Due to the growth of densification and multi-band services in emerging cellular networks, the next generation NodeB will confront with a plethora of handover-related Configuration and Optimization Parameters (COPs). Adjusting the handover-related COPs will directly impact the networks Key Performance Indicators (KPIs). Thus, accurately estimating the effect of the handover-related COPs adjustment on KPI values and modeling the COP-KPI relationship can provide guidelines for intelligent network management. Existing studies have applied machine learning to model the intricate relationship between COP and KPI, but these methods merely exploit their correlations. In this paper, considering that there exist the causal effects between COPs and KPIs that go beyond the correlation analysis of them, we propose the causal inference aided handover parameter adjusting effect estimation and design the Causal-Stable Decomposition Network (CSDnet). Specifically, we propose the causal-stable deconstructor module based on self-attention mechanism to characterize the complex causality-stability of KPI induced by the COP adjustment. To ensure the training, we propose the gradient matching loss and the consistency constraint loss. Then, we design the stable decoder and the causal decoder that incorporate the causal information into COP-KPI modeling. Extensive experiments on the COP-KPI dataset collected from the real-world cellular networks verify the effectiveness of CSDnet.