Analytical Modeling of Uplink Cellular Networks

By Thomas David Novlan, Harpreet S. Dhillon, Jeffrey G. Andrews. IEEE Transactions on Wireless Communications, 2012

475 citations recorded by the source corpus.

Cellular uplink analysis has typically been undertaken by either a simple approach that lumps all interference into a single deterministic or random parameter in a Wyner-type model, or via complex system level simulations that often do not provide insight into why various trends are observed. This paper proposes a novel middle way using point processes that is both accurate and also results in easy-to-evaluate integral expressions based on the Laplace transform of the interference. We assume mobiles and base stations are randomly placed in the network with each mobile pairing up to its closest base station. Compared to related recent work on downlink analysis, the proposed uplink model differs in two key features. First, dependence is considered between user and base station point processes to make sure each base station serves a single mobile in the given resource block. Second, per-mobile power control is included, which further couples the transmission of mobiles due to location-dependent channel inversion. Nevertheless, we succeed in deriving the coverage (equivalently outage) probability of a typical link in the network. This model can be used to address a wide variety of system design questions in the future. In this paper we focus on the implications for power control and show that partial channel inversion should be used at low signal-to-interference-plus-noise ratio (SINR), while full power transmission is optimal at higher SINR.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e3dafa52e4029034d38328b64d9f6547bf7b5ed8

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Citations in the corpus

A Primer on Cellular Network Analysis Using Stochastic Geometry (2016, methodology): “It has been shown through simulations that this approximation, although conceptually dubious, does not effect the coverage probability result too much [2], [4].”
Modeling and Analyzing Millimeter Wave Cellular Systems (2016, background): “different distributions of interferers between downlink and uplink [166], [187].”
On Stochastic Geometry Modeling of Cellular Uplink Transmission With Truncated Channel Inversion Power Control (2014, background): “However, both the simulation and analysis in [17] do not vary with Pu because the maximum transmit power constraint is ignored.”
Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial (2016, methodology): “9 By virtue of the PPP approximation, tractable analysis is conducted for several types of repulsive PPs in cellular networks domain [55], [57], [70], [75], [124], [125], [154] and ad hoc network domain [155]–[161].”
Analytical Modeling of Mode Selection and Power Control for Underlay D2D Communication in Cellular Networks (2014, background): “Note that, as discussed in [22], the dependence between the neighboring Voronoi cells imposes a weak correlation among the cellular link distances, and hence, transmit powers of the UEs are correlated due to the power control policy.”
Spectrum Sharing for Device-to-Device Communication in Cellular Networks (2013, background): “This makes the analysis intractable and some simpliﬁed assumptions are needed (see e.g. [37]).”
Enabling Wireless Power Transfer in Cellular Networks: Architecture, Modeling and Deployment (2012, background): “These drawbacks can be overcome by using stochastic geometry for modeling the cellular-network architecture [20]– [23].”