Abstract
Coverage is one of the most important targets that has to be achieved by cellular operators. Without coverage provisioning, concepts like service, or Quality of Service (QoS) cannot be considered. Therefore, cellular coverage prediction and enhancement is a basic and prevailing area of research in wireless communications. Our work introduces an automatic and remote self-optimization process based on exploitation of geo-location information for cellular coverage optimization. Specifically, we use Radio Environment Maps (REMs) for cellular network coverage hole detection purposes. We define REM as an intelligent entity which stores incoming radio environmental data and also interpolates this data to benefit from the spatial correlation that exists in it. Furthermore, with the standardization of Minimization of Drive Tests (MDT) in 3GPP, geo-location based solutions/applications are increasingly becoming feasible and popular. The proposed REM-based coverage hole detection approach drastically reduces the required drive tests and enhances the network with self-responsive capabilities to handle key obstacles towards cellular networks autonomy.
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