Cellular service providers (CSPs) require predicting the network performance for various reasons such as analyzing the impact of planned configuration changes and large-scale events on the network. Although network configurations are widely considered as key predictors of performance, we claim that they are insufficient for accurately predicting cellular network performance. The cellular networks are impacted by unmeasured external factors (e.g., weather, called latents), therefore, the performance prediction based solely on configurations may result in confounding effects. We show that the Mobility, Access, and Traffic (MAT) metrics should be considered in addition as network performance predictors. Using a large dataset collected from a live cellular network, we validate the claim and show the benefit of using MAT metrics for accurate performance prediction.

Cellular service providers (CSPs) aim to optimize network performance and enhance user experience by tuning network configurations. However, this process often requires continuous live network testing, which incurs significant operational costs. In this paper, we focus on predicting the impact of configuration changes using historical data, thereby reducing the need for live network tests. A key challenge in developing such a model is accounting for unobserved external factors (e.g., weather, referred to as latents) that can introduce confounding effects between configurations and performance metrics. To address this, we employ intermediate network metrics, called Mobility, Access, and Traffic (MAT) metrics, which are influenced by both configurations and latents, and in turn, affect performance metrics. We introduce Configuration Impact Prediction Analysis Toolkit (CIPAT), a novel two-stage toolkit developed using a comprehensive real-world dataset from live LTE networks. Our evaluation demonstrates that CIPAT enables CSPs to predict the performance impact of proposed configuration changes with up to 86% accuracy and 85% efficacy, thereby reducing the operational costs associated with configuration tuning.

Managing network configuration and improving service experience effectively is essential for cellular service providers (CSPs). This is challenging because of cellular networks' large scale and complexity, the wide variety of configuration parameters, and the performance impact tradeoffs resulting across multiple metrics and geographical locations. This paper focuses on learning and using network contexts to recommend performance-improving configurations. While learning contexts, one must carefully account for the configuration parameter dependency, performance impact confusion that can arise due to co-occurring unrelated changes, and uneven change deployment distribution across locations. We present a new solution Chroma that addresses the above challenges. Using real-world data collected from a large operational LTE and 5G cellular service provider, we thoroughly evaluate and demonstrate the efficacy of Chroma. We successfully trial Chroma on an operational cellular network and highlight its benefits in practical settings.

Cellular service operators frequently tune the network configuration to optimize coverage, support seamless handovers, minimize channel interference, and improve the service performance experience to the end-users. Tuning such a complicated network is highly challenging because of the many configuration parameters, evolving complexity of cellular networks, and diverse requirements across voice, video, and data applications. Any misconfigurations or even poor settings can significantly negatively impact service quality. In this paper, we propose a new approach Aurora that derives best practices knowledge from exploration of the massive existing configuration in the network and uses conformity-based recommendation with performance-based filtering to improve cellular service. We implemented and evaluated Aurora using data from a very large LTE and 5G cellular service provider. Our operational experience over the last one year highlights the benefits of Aurora and exposes exciting research opportunities and challenges in configuration tuning and performance management.