Stochastic geometry is widely employed to model cellular network. But in most existing works, base stations(BSs) are modelled following a homogeneous Poisson point process(PPP) for one-tier network, or several indepen...Stochastic geometry is widely employed to model cellular network. But in most existing works, base stations(BSs) are modelled following a homogeneous Poisson point process(PPP) for one-tier network, or several independent homogeneous PPP for multi-tier network, which ignore the dependence among BSs. In this paper, a three-tier UDN(Ultra dense network) with Macrocell BSs(MBS) for basic coverage, Picocell BSs(PBSs) deployed outside the coverage area of MBSs for compensating coverage holes, and Femtocell BSs(FBSs) surrounding MBSs for capacity improvement modelled by point process with inter-tier dependence is proposed. The tier association probability, the coverage probability and area spectrum efficiency(ASE) are derived. Simulation results validate our derivation, and results show that the proposed network model has 25%-45% performance gain in ASE.展开更多
基金partially supported by National 863 Program (2014AA01A702)national major project (2016ZX03001011-005)national natural science foundation project (61521061)
文摘Stochastic geometry is widely employed to model cellular network. But in most existing works, base stations(BSs) are modelled following a homogeneous Poisson point process(PPP) for one-tier network, or several independent homogeneous PPP for multi-tier network, which ignore the dependence among BSs. In this paper, a three-tier UDN(Ultra dense network) with Macrocell BSs(MBS) for basic coverage, Picocell BSs(PBSs) deployed outside the coverage area of MBSs for compensating coverage holes, and Femtocell BSs(FBSs) surrounding MBSs for capacity improvement modelled by point process with inter-tier dependence is proposed. The tier association probability, the coverage probability and area spectrum efficiency(ASE) are derived. Simulation results validate our derivation, and results show that the proposed network model has 25%-45% performance gain in ASE.
