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.

Cellular service providers add carriers in the network in order to support the increasing demand in voice and data traffic and provide good quality of service to the users. Addition of new carriers requires the network operators to accurately configure their parameters for the desired behaviors. This is a challenging problem because of the large number of parameters related to various functions like user mobility, interference management and load balancing. Furthermore, the same parameters can have varying values across different locations to manage user and traffic behaviors as planned and respond appropriately to different signal propagation patterns and interference. Manual configuration is time-consuming, tedious and error-prone, which could result in poor quality of service. In this paper, we propose a new data-driven recommendation approach Auric to automatically and accurately generate configuration parameters for new carriers added in cellular networks. Our approach incorporates new algorithms based on collaborative filtering and geographical proximity to automatically determine similarity across existing carriers. We conduct a thorough evaluation using real-world LTE network data and observe a high accuracy (96%) across a large number of carriers and configuration parameters. We also share experiences from our deployment and use of Auric in production environments.

Cellular service providers continuously deploy changes in their network in the form of new software releases, service feature introductions, configuration changes, equipment re-homes, firmware upgrades, and topology modifications. It is important to carefully assess the impact of these changes on service performance to validate expected behaviors and take mitigation actions in a timely fashion in case of any unexpected degradation. The diverse nature of the network changes, complex interactions across different layers of the cellular network, and the rapid evolution of the network make it challenging to accurately conduct the assessment. In this paper, we present the design and implementation of our system that enables rigorous, effortless and timely assessment of performance around network changes. We share our lessons learned from the deployment in an operational cellular network over the last eight years.

As smartphone users increasingly rely on cellular networks to access voice, video, and web applications, guaranteeing good performance and high availability is more important than ever. Historically, managing cellular network configuration has been a manual, error-prone process; recently, automated solutions such as SON (Self-Organizing Networks) controllers are being deployed for dynamic tuning of network configuration to improve end-user service performance under dynamic network and traffic conditions. SON automates many aspects of cellular network configuration, but it is nonetheless susceptible to software bugs and expected traffic changes that could result in sub-optimal performance. In this paper, we propose a capability (Veracity) to analyze and quantify the performance effects of SON actions. Assessing the effects of SON control is difficult because of the dynamic nature of SON and the dependency of end-user performance on factors such as radio channel quality, mobility and traffic load. Veracity addresses these using model-driven impact detection and quantification. Our evaluation using data collected from an operational cellular network demonstrates that Veracity is accurate. Veracity is now being used by the service providers' field operation teams for the assessment of SON effectiveness in arenas and stadiums.