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Stochastic geometry modeling and analysis of backhaul-constrained Hyper-Dense Heterogeneous cellular networks

Abstract : Hyper-Dense Heterogeneous (HDH) deployments, which are made of different kinds of Base Stations (BSs), constitute a promising solution to meet the high data rates envisioned for 5G cellular systems. However, the presence of such a large number of BSs, each of which is expected to deliver a large amount of data to the end-users, is pushing the bottleneck of cellular networks from the Radio Access Network (RAN) to the backhaul, which is becoming as critical as the radio infrastructure. Hence, new network topologies and network architectures are under debate, whose objective is to overcome the backhaul cost and capacity crunch. In order to adequately design these emerging network topologies, appropriate abstraction models need to be introduced for system-level analysis. In this context, stochastic geometry has recently emerged as a promising tool for system-level performance evaluation of cellular systems. In this paper, we adopt a Poisson Tree model for analyzing a hierarchical backhaul and the RAN of a HDH network, where the nodes of the tree are traffic concentrators, BSs and users. The proposed model captures the impact of the finite user density (load) on the network performance and the distribution of the Signal-to-Interference-plus-Noise-Ratio (SINR), which is determined by the positions of users and BSs, as well as by the channel conditions and the other-cell interference.
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Contributor : Myriam Baverel Connect in order to contact the contributor
Submitted on : Friday, February 5, 2016 - 11:20:06 AM
Last modification on : Saturday, May 1, 2021 - 3:50:34 AM



Franscisco J. Martin-Vega, Marco Di Renzo, Mari Carmen Aguayo-Torres, Gerardo Gomez, Trung Q. Duong. Stochastic geometry modeling and analysis of backhaul-constrained Hyper-Dense Heterogeneous cellular networks. 17th International Conference on Transparent Optical Networks (ICTON) 2015, Jul 2015, Hungary, Hungary. pp.1-4, ⟨10.1109/icton.2015.7193589⟩. ⟨hal-01269732⟩



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