SINR-based k-coverage probability in cellular networks with arbitrary shadowing

Keeler, Paul; Błaszczyszyn, Bartłomiej; Karray, Mohamed Kadhem

doi:10.1109/isit.2013.6620410

Cited by 92 publications

(129 citation statements)

References 21 publications

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“…Previous studies are modified to suit the proposed system model. The distribution of the received signal is following the Poisson process on

R^{+}

for the first and second hops, respectively, as below

Θ_{1} = \frac{1}{Y_{1}} = ({}, \frac{P_{X 1} S_{X 1}}{ℓ_{1} false| X_{1} false|}, X_{1} \in Φ_{1}),

Θ_{2} = \frac{1}{Y_{2}} = ({}, \frac{P_{X 2} S_{X 2}}{ℓ_{2} false| X_{2} false|}, X_{2} \in Φ_{2}) .

…”

Section: Heterogeneous Cellular Network Modelmentioning

confidence: 99%

Multitier heterogeneous network using stochastic geometry and factorial moment approaches

Fadoul

2019

Int J Communication

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Summary The rapid growth of small cells is driving cellular network toward randomness and heterogeneity. Usually, cellular networks are modeled by placing each tier (eg, macro, pico, and relay nodes) deterministically on a grid. In such a heterogeneous cellular network, the rational approach to characterize the base stations (BSs), user, and relay locations is by using random spatial models. When calculating the metric performances such as coverage probability, these networks are idealized without consideration of interference. Therefore, interference modeling remains the key issue for the deployment of small cells. This paper developed a single and multitier cellular network model that captures the downlink heterogeneous cellular network with variable parameters such as the target signal‐to‐interference ratio (SIR), transmitted power, and deployment density. In particular, we model scriptK‐tier transmission and compare it with a single‐tier and traditional grid model to obtain tractable coverage probability using stochastic geometry and factorial moment. The obtained results demonstrate the effectiveness and analytical tractability to study the heterogeneous performance.

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“…Previous studies are modified to suit the proposed system model. The distribution of the received signal is following the Poisson process on

R^{+}

for the first and second hops, respectively, as below

Θ_{1} = \frac{1}{Y_{1}} = ({}, \frac{P_{X 1} S_{X 1}}{ℓ_{1} false| X_{1} false|}, X_{1} \in Φ_{1}),

Θ_{2} = \frac{1}{Y_{2}} = ({}, \frac{P_{X 2} S_{X 2}}{ℓ_{2} false| X_{2} false|}, X_{2} \in Φ_{2}) .

…”

Section: Heterogeneous Cellular Network Modelmentioning

confidence: 99%

Multitier heterogeneous network using stochastic geometry and factorial moment approaches

Fadoul

2019

Int J Communication

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“…Table 2 lists the default value of system parameters. Consider one urban area A 0 with 2.5 * 1.9(/km * km) covered by a two- (which corresponds to the COST Walfisch-Ikegami model for urban environment) [24]. It is a natural assumption to say that the transient failure/recovery rates are larger than the network-level full restoration rate.…”

Section: Validity Of the Decomposition Assumptionmentioning

confidence: 99%

Survivability analysis of a two-tier infrastructure-based wireless network

2017

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Network design and operation of a mobile network infrastructure, especially its base station systems, need to consider survivability as a fundamental requirement. To this end, quantifiable approaches to survivability analysis of such infrastructures are crucial. The objective of this paper is to propose a model for quantification of the survivability of wireless communication networks subject to massive failures, e.g., caused by natural disasters, common mode hardware and software failures, and security attacks. This means to analyze the transient behavior of the recovery phases. We use a Markov model approach, and apply this in a case study of a two-tier infrastructure-based wireless network. To take location information of base stations into consideration, the spatial average network performance is estimated by means of a stochastic geometry based approach. Further, in order to avoid state space explosion while addressing large networks, an approximate product-form analysis approach is also presented, where the two base stations tiers are decoupled such that their survivability analysis can be studied independently. The assumptions used in the proposed models, including Poisson point process (PPP) assumption and product-form decomposition assumption, are validated on real data. Numerical experiments are also performed to investigate the approximation accuracy and computational efficiency of the product-form analysis approach, as well as to examine the effect of different parameters on the network's survivability. The results show that 1 Lang Xie is the corresponding author with email address langxie 77@163.com. Preprint submitted to Journal of L A T E X TemplatesDecember 12, 2016 the approximate product-form approach is more scalable with reasonably good accuracy and hence may be more preferred for analysis of large size networks.

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“…We model the SeNBs with a homogeneous Poisson point process [26] and denote the SINR by γ. In co-channel deployments, we define the SINR at a typical UE as…”

Section: A Cell-clusteringmentioning

confidence: 99%

“…The metrics are denoted by ρ m,k and ρ s,i,k for MUEs and SUEs, respectively. We calculate the metric using the model of homogeneous Poisson point process [26]. Based on this model, we give the definition of relative interference for different UEs as listed in Table II, if Neither cell i nor cell j has been assigned to any cluster then a ′ := min(C(i), C(j)).…”

Section: B Ue-clusteringmentioning

confidence: 99%

A Dynamic Graph-Based Scheduling and Interference Coordination Approach in Heterogeneous Cellular Networks

Zhou

Ngai

et al. 2016

IEEE Trans. Veh. Technol.

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In order to meet the demand of increasing mobile data traffic and provide better user experience, heterogeneous cellular networks (HCNs) have become a promising solution to improve both the system capacity and coverage. However, due to dense self-deployment of small cells in a limited area, serious interference from nearby base stations may occur, which results in severe performance degradation. To mitigate downlink interference and utilize spectrum resources more efficiently, we present a novel graph-based resource allocation and interference management approach in this paper. Firstly, we divide small cells into cell-clusters considering their neighborhood relationships in the scenario. Then, we develop another graph clustering scheme to group user equipment (UE) in each cell-cluster into UE-clusters with minimum intra-cluster interference. Finally, we utilize a proportional fairness scheduling scheme to assign subchannels to each UE-cluster and allocate power using waterfilling method. In order to show the efficacy and effectiveness of our proposed approach, we propose a dual-based approach to search for optimal solutions as the baseline for comparisons. Furthermore, we compare the graph-based approach with the state of the arts and a distributed approach without interference coordination. The simulation results show that our graph-based approach reaches more than 90 percent of the optimal performance, and achieves a significant improvement in spectral efficiency compared to the state of the arts and the distributed approach both under cochannel and orthogonal deployments. Moreover, the proposed graph-based approach has a low computation complexity, making it feasible for real-time implementation.

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SINR-based k-coverage probability in cellular networks with arbitrary shadowing

Cited by 92 publications

References 21 publications

Multitier heterogeneous network using stochastic geometry and factorial moment approaches

Multitier heterogeneous network using stochastic geometry and factorial moment approaches

Survivability analysis of a two-tier infrastructure-based wireless network

A Dynamic Graph-Based Scheduling and Interference Coordination Approach in Heterogeneous Cellular Networks

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