Min-cut based partitioning for urban LTE cell site planning

Common approaches for partitioning cell site planning problems into smaller instances typically suffer from the lack of knowledge about the number of clusters k that is appropriate for the particular problem. When applying an iterative processing over methods such as the k-means algorithm or the related k-medoids approach to determine the optimum k, the computational effort can be higher than solving the original problem instance directly. This is particularly the case if the optimum k is small, which happens most likely in urban environments where the user density and the number of cells are high. In this paper, we propose a graph-based method using minimum cut operations to partition a planning instance automatically into an appropriate number of k sub-problems. We demonstrate the benefits of this approach by numerical evaluation of exemplary application to the problem of planning LTE cell site locations in an urban environment.