LAUSR

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 This approach suffers from the limitations of high costs, low coverage, and out-of-date values.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.