Automated Coverage Hole Detection for Cellular Networks Using Radio Environment Maps

The existence of coverage holes in cellular networks is a common problem for mobile operators. Traditionally, the cellular coverage is computed using sophisticated planning tools, and then optimized through drive tests. With the drive tests information, the operators detect the poorly covered areas and take actions to eliminate them. The introduction of self-organized or "cognitive" techniques, would allow the operators to maximize the network's information obtained through drive tests or reported by the mobile users. In this paper we propose the use of spatial Bayesian geo-statistics to build a Radio Environment Map (REM) for real coverage hole detection purposes. Results show that the number of pixels forming the coverage holes, as well as the probability of detecting them, can be significantly increased with the use of REMs, compared to the case where only network measurements are used.