Automatic clustering algorithms for indoor site selection in LTE

Small cell systems are a cost-effective solution to provide adequate coverage inside buildings. Nonetheless, the addition of any indoor site requires evaluating the trade-off between the coverage and capacity gain provided by the new site and its monetary cost. In this paper, a new automatic indoor site selection algorithm based on clustering techniques is presented. The algorithm calculates the number of antennas, radio heads, and baseband units needed for the area under study. Then, a clustering algorithm groups several radio heads of different buildings in a single pooled baseband unit, reducing deployment costs. The proposed clustering algorithm is based on a local refinement algorithm, whose starting point considers a new baseband unit for every new site, and then, possible reallocation to existing units is checked. To assess the method, the proposed indoor site selection algorithm is included in a network planning tool. The algorithm is tested in a real heterogeneous network scenario, taking into account vendor specifications and operator constraints. Results show that the use of the proposed clustering algorithm can reduce the total network cost by up to 58 % in a real scenario.