Research on Coverage Probability in Ultra-Dense 5G Heterogeneous Cellular Networks Based on Poisson Clustered Process

The cellular networks in 5G are tending to be heterogeneous and ultra-dense networks. The methods used in traditional hexagonal grid model are no more adaptive to the 5G heterogeneous cellular networks today, which are too idealized and inaccurate. A popular approach for analyzing heterogeneous cellular networks is using Poisson point process (PPP), which assumes that the deployment of base stations (BSs) is completely space random (CSR) and from which we can get a lower bound of the probability of coverage probability. However, geographical and human factors should be taken into account in reality. The BSs or mobile users may be clustered in crowded areas such as enterprises and shopping malls so that the space distribution based on PPP will be inaccurate. This motivates us to find a new approach to settle it. In this paper, we focus on using Poisson clustered process (PCP) to model ultra-dense 5G heterogeneous cellular networks which include macrocells, picocells and femtocells. First of all, a model of the locations of BSs based on Poisson clustered process (PCP) has been proposed, and the formation of clusters of the BSs has been discussed. Then the probability model of coverage and its three special cases using SINR have been derived, and the proposed model obeys to the cell selection mechanism based on instantaneous SINR. Finally, according to the proposed model, comparison and analysis of coverage probability using hexagonal grid model, PCP and PPP are done respectively. The numerical results show that using PCP can get higher coverage probability than using PPP.