Abstract
In mobile networks, detecting and eliminating areas with poor performance is key to optimize end-user experience. In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems to pinpoint problematic locations because the origin of such measurements (i.e., user location) is not registered in most cases. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this paper, a data-driven methodology is proposed to detect the best sites for new small cells to improve network performance based on attributes of connections, such as radio link throughput or data volume, in the radio interface. Unlike state-of-the-art approaches, based on data from only one source (e.g., radio signal level measurements or social media), the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged messages from two social networks (Twitter and Flickr).
Highlights
In the last years, mobile networks have experienced a continuous growth in the amount of users and services [1]
Since user behavior is not the same in all locations, both traffic components are broken down depending on user context. The convenience of this approach is validated with the analysis presented
An automatic context-aware data-driven method has been proposed to precisely detect small areas with coverage or capacity problems in a mobile network based on the performance of connections
Summary
Mobile networks have experienced a continuous growth in the amount of users and services [1]. Most current site selection approaches only take simple network coverage and signal quality indicators due to the difficulty of modeling dynamic packet scheduling with users of multiple services and different radio link conditions in a radio network planning tool. Network re-planning and optimization tasks often has to be done based on measurements only positioned by cell identity and time advance statistics Such an approach leads to large location errors, which prevent estimating user context. Some companies (e.g., OpenSignal [20] or WeFi [20]) provide real crowdsourced measurements collected by anonymous users, which can be used to assess current deployments [21] Social networks are another source of information for understanding user behavior.
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