2016/06/20 by Chiranjib Saha, Mehrnaz Afshang, Saha, Chiranjib +3 · 1 citation
Economics, Econometrics and Finance · Engineering · Social Sciences · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Information Theory (cs.IT) #Networking and Internet Architecture (cs.NI) #Spatial and Panel Data Analysis
paper · pdf · doi:10.48550/arxiv.1606.06223
openalex publication_date 2016/06/20 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
One of the principal underlying assumptions of current approaches to the\nanalysis of heterogeneous cellular networks (HetNets) with random spatial\nmodels is the uniform distribution of users independent of the base station\n(BS) locations. This assumption is not quite accurate, especially for\nuser-centric capacity-driven small cell deployments where low-power BSs are\ndeployed in the areas of high user density, thus inducing a natural correlation\nin the BS and user locations. In order to capture this correlation, we enrich\nthe existing K-tier Poisson Point Process (PPP) HetNet model by considering\nuser locations as Poisson Cluster Process (PCP) with the BSs at the cluster\ncenters. In particular, we provide the formal analysis of the downlink coverage\nprobability in terms of a general density functions describing the locations of\nusers around the BSs. The derived results are specialized for two cases of\ninterest: (i) Thomas cluster process, where the locations of the users around\nBSs are Gaussian distributed, and (ii) Mat 'ern cluster process, where the\nusers are uniformly distributed inside a disc of a given radius. Tight\nclosed-form bounds for the coverage probability in these two cases are also\nderived. Our results demonstrate that the coverage probability decreases as the\nsize of user clusters around BSs increases, ultimately collapsing to the result\nobtained under the assumption of PPP distribution of users independent of the\nBS locations when the cluster size goes to infinity. Using these results, we\nalso handle mixed user distributions consisting of two types of users: (i)\nuniformly distributed, and (ii) clustered around certain tiers.\n