Interference Modelling Using Hierarchical Spatial Clustering of Terrain and User Density Maps

In environments with non-uniform user density and correlated slow fading, the Poisson Point Process (PPP) model fails to capture the distribution of aggregate interference. One way to overcome this issue is to divide the deployment area into multiple disjoint clusters and describe the interference from each cluster using a PPP with cluster-specific PPP intensity and pathloss model. Obviously, too few clusters do not describe the distribution of aggregate interference accurately, while on the other hand, too many clusters incur high computational and storage overheads. In this direction, we propose to divide the area according to a hierarchical clustering algorithm which uses the Akaike Information Criterion to balance between complexity and accuracy. The algorithm can be useful for database-assisted spectrum access. It allows a database to assess the interference between systems (overlapping in spectrum but not in space) in a low-complexity manner.