Spatial stochastic models for analysis of heterogeneous cellular networks with repulsively deployed base stations
We consider spatial stochastic models of downlink heterogeneous cellular networks (HCNs) with multiple tiers, where the base stations (BSs) of each tier have a particular spatial density, transmission power and path-loss exponent. Prior works on such spatial models of HCNs assume, due to its tractability, that the BSs are deployed according to homogeneous Poisson point processes. This means that the BSs are located independently of each other and their spatial correlation is ignored. In the current paper, we propose two spatial models for the analysis of downlink HCNs, in which the BSs are deployed according to ? ? -Ginibre point processes. The ? ? -Ginibre point processes constitute a class of determinantal point processes and account for the repulsion between the BSs. Besides, the degree of repulsion is adjustable according to the value of ??(0,1] ? ? ( 0 , 1 ] . In one proposed model, the BSs of different tiers are deployed according to mutually independent ? ? -Ginibre processes, where the ? ? can take different values for the different tiers. In the other model, all the BSs are deployed according to an ? ? -Ginibre point process and they are classified into multiple tiers by mutually independent marks. For these proposed models, we derive computable representations for the coverage probability of a typical userthe probability that the downlink signal-to-interference-plus-noise ratio for the typical user achieves a target threshold. We exhibit the results of some numerical experiments and compare the proposed models and the Poisson based model.
spatial stochastic models heterogeneous cellular networks determinantal point processes coverage probability signal-to-interference-plus-noise ratio ? ? -ginibre point processes ? ? ? ? ?