Heterogeneous Cellular Networks Mixed with LoS and NLoS Transmissions

In the last decades, the rapid increase of user traffc demand for better user experience has pushed the traditional macrocell-only networks being evolving to modern heterogeneous networks(HetNets) with a multi-tier structure. The dense deployment of small-cell base stations (BSs) implies short distances between BSs and users. It is therefore likely that users will see line-of-sight (LoS) links from its serving BS and even nearby interfering BSs, which has not been considered in performance analysis for multi-tier HetNets yet. In this thesis, the dense multi-tier HetNet with LoS and non-line-of-sight (NLoS) transmissions based on a multi-slope path loss model is analyzed. The spatial locations of BSs of any given network tier and those of mobile users are modeled as independent spatial Poisson point processes (PPPs). The expressions of downlink coverage probability are divided for a multi-tier HetNet, based on that the calculations of the area spectral effciency (ASE) and energy effciency (EE) are further proposed. The results demonstrate that in an extremely dense HetNet, both the ASE and EE of the HetNet will drop quickly with further increase of the small-cell density due to the dominance of LoS interfering small-cell links. Following that, the investigation is moved to the probabilistic events of LoS and NLoS transmissions. Four transmission scenarios are simulated with different path loss models, including a linear LoS probability function, a suburban area, a millimetre wave transmission and a 3D path loss model. Accordingly, a user-centric BS clustering strategy is proposed for a non-coherent joint transmissions (JTs) in dense small-cell networks, based on the idea of grouping the BSs with their LoS probabilities to such user above a predefined threshold. The proposed BS clustering strategy is evaluated in the above four transmission environments. Our simulation results show that the coverage probability and spectrum effciency (SE) achieved by the proposed user-centric BS clustering strategy achieve a rapid growth rate with the increasing BS density, and even at extremely high BS densities in all four considered environments. Lastly, following the proposed BS clustering strategy above, a further developed clustering strategy called multi-BS multi-user-equipment (UE) clustering is proposed to allow multiple BSs to serve multiple UEs simultaneously. The main idea of this clustering strategy is to boost network performance in terms of coverage probability and SE at high BS density without sacrificing the ASE. Utilizing stochastic geometry, the closed form expressions of the network performance in terms of coverage probability, SE, ASE and EE are derived in a downlink small-cell network. The results show that the proposed clustering strategy achieves high coverage probability and linear increasing SE and ASE in ultra dense networks at same time.