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Capacity driven small cell deployment in heterogeneous cellular networks: Outage probability and rate coverage analysis
Author(s) -
Ullah Arif,
Haq Abbas Ziaul,
Muhammad Fazal,
Abbas Ghulam,
Jiao Lei
Publication year - 2020
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3876
Subject(s) - stochastic geometry , computer science , poisson point process , coverage probability , base station , poisson distribution , cellular network , software deployment , monte carlo method , heterogeneous network , point process , distributed computing , computer network , mathematics , wireless network , statistics , telecommunications , wireless , confidence interval , operating system
Heterogeneous cellular networks (HCNets) are one of the key enabling technologies to improve performance gain of future cellular networks. Stochastic geometry is considered a promising tool to model and analyze HCNets. Users and base stations (BSs) are generally distributed uniformly using a homogeneous Poisson point process (HPPP). The assumption of uniformly distributed users is not suitable in HCNets because of the existence of clustered users in hotspots. To consider the correlation between the users and BSs, deployment of small base stations in these areas are of great concern to increase the performance of HCNets. In this article, we assume the notion of mixed user distribution, wherein the network users are the superposition of clustered and uniform users, modeled through HPPP and Poisson cluster process, respectively. We evaluate outage probability and rate coverage of the proposed HCNet model. We compare the network performance of the proposed mixed user distribution model with the conventional uniformly distributed user model. The analytical results are validated using Monte‐Carlo simulations. Our results show that the proposed HCNet model of mixed user distribution outperforms the uniformly distributed user model in terms of outage probability and rate coverage.