Modelling Multi-Operator Base Station Deployment Patterns in Cellular Networks

Abstract

Abstract-Stochastic models of base station infrastructure deployment by multiple mobile operators can be an invaluable tool for deriving fundamental results about wireless network sharing. In this paper, we study stochastic geometry models for a shared cellular network consisting of base stations deployed by multiple mobile operators, based on real cellular network data coming from three European countries. Relying on a statistical approach as well as the evaluation of wireless network performance metrics, we find that, in the city center areas, operators tend to deploy their antennas in close proximity, while in other areas this is not the case and we find some level of repulsion between antennas of different operators. As we show, the log-Gaussian Cox process provides the most compelling fitness results with real multi-operator base station deployment patterns and a potentially attractive model that offers some degree of analytical tractability. Moreover, we observe that the behaviour which can be modelled with the help of these processes occurs over and over again for similar areas in different countries, which suggests universality of the proposed models.

Infrastructure and spectrum sharing trade-offs in mobile networks

“…We also consider an asymptotic case of clustering, i.e., colocation, where the nearest base stations of two operators are located an arbitrarily small distance apart. The clustering case is of particular interest as it is representative of real radio access network deployments, which tend to be deployed to follow social and geographical features, resulting in clustered deployment patterns, see [13], [14].…”

“…The key motivation is to analyze the network performance over various realizations of a network consisting of base stations which follow a particular spatial structure. This structure may be expressed as a stochastic model so that the spatial distribution of nodes resembles that of a real radio access network, see, for example, [14], [16]. In particular, we apply the superposition of two homogeneous Poisson point processes (hPPP) [15], to model independently distributed networks, and the Gauss-Poisson process (GPP) [15], to model clustered networks.…”

Sensitivity Analysis on Service-Driven Network Planning

“…While a definite answer would require more datasets, the authors of [48] found that base station deployments of different operators in different cities have remarkably similar spatial properties and can be modeled with very similar statistic models. This, along with the fact that we study different demand profiles with different complementarity values, affords to our study a high level of generality.…”

“…Moreover, a few applications dominate others given their relative traffic volume, and applications can be grouped into traffic profiles that describe application usage distribution for any given cell. Other works, e.g., [47], [48] have studied real-world deployments of cellular networks, and found that some spacial features thereof are remarkably similar throughout mobile operators and cities.…”

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

“…Although base station and hotspot infrastructure deployment policies have been widely studied and applied by operators [21], due to the nature of the wireless technologies and their capabilities (e.g., GSM/UMTS/3G/4G and IEEE802.11a,b,g,p), plus the diverse mobility involved (e.g., hotspots for human speed vs. RSUs for vehicular speed), such well-known cellular techniques cannot be applied and/or extrapolated to the RSU domain. Therefore, in this work, we did not consider mobile base station placement models and/or policies.…”