With the advent of cellular network technologies, mobile Internet access becomes the norm in everyday life. In the meantime, the complaints made by subscribers about unsatisfactory cellular network access also become increasingly frequent. From a network operator's perspective, achieving accurate and timely cellular network diagnosis about the causes of the complaints is critical for both improving subscriber-perceived experience and maintaining network robustness. We present the Intelligent Customer Care Assistant (ICCA), a distributed fault classification system that exploits a data-driven approach to perform large-scale cellular network diagnosis. ICCA takes massive network data as input, and realizes both offline model training and online feature computation to distinguish between user and network faults in real time. ICCA is currently deployed in a metropolitan LTE network in China that is serving around 50 million subscribers. We show via evaluation that ICCA achieves high classification accuracy (85.3%) and fast query response time (less than 2.3 seconds). We also report our experiences learned from the deployment.

With ubiquitous smartphone usages, it is important for network providers to provide high-quality service to every user in the network. To make more effective planning and scheduling, network providers need an accurate estimate of network quality for base stations and cells from the perspective of user experience. Traditional drive testing approach provides a quality measurement for each area and the quality measurement is obtained from the equipment in a moving vehicle. This approach suffers from the limitations of high costs, low coverage, and out-of-date values. In this paper, we propose a novel crowdsourcing approach for the task of network quality estimation, which incurs little costs and provides timely and accurate quality estimation. The proposed approach collects quality measurements from individual end users within a certain network or cell coverage area, and then aggregates these measurements to obtain a global measurement of network quality. We propose an effective aggregation scheme which infers the information weights of end users and incorporates such weights into the estimation of network quality. Experiments are conducted on two datasets collected from citywide 3G networks, which involve 616,796 users and 22,715 cells. We validate the effectiveness of the proposed approach compared with baseline method. From the aggregated measurement results, we observe some interesting patterns about network quality, which can be explained by network usage and traffic behavior. We also show that proposed approach runs in linear time.

WeChat is one of the most popular mobile messaging applications worldwide. However, due to the proprietary nature of WeChat, its characteristics and performance impact on cellular networks remain largely unexplored. This paper presents the first measurement study that dissects real-world WeChat traffic in a cellular network. We build ChatDissect, a protocol inference tool that infers the unique protocol formats and semantics of WeChat in a fine-grained manner. ChatDissect enables us to distinguish WeChat and its specific tasks from general network traffic traces. As a case study, we collect a real-world dataset from a commercial 3G cellular network in China and use ChatDissect to identify around 150K WeChat users with 16GB of WeChat payloads. We unveil the signatures, server architecture, and workflows of WeChat, and further analyze the activities of the extracted WeChat traffic.