Heterogeneous Cellular Network Models With Dependence

Deng, Na; Zhou, Wuyang; Haenggi, Martin

doi:10.1109/jsac.2015.2435471

Cited by 94 publications

(56 citation statements)

References 19 publications

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“…A heterogeneous small cell network with overlay small cells and macrocell is a promising solution to enhance service survivability of mobile networks in large-scale failure scenarios [16]. Modeling BS deployment patterns in multiple-tier heterogeneous cellular network scenarios have been studied in [17], [18]. Most of the stochastic geometry works on HCNs focus on the spatial 80 average performance of the network, but neglects the temporal dynamics due to disaster failure.…”

Section: Related Workmentioning

confidence: 99%

“…G is a constant to merge constant parameters together. More specifically, the COST Walfisch-Ikegami model is taken for path As suggested in [18], the radius D of the exclusion region of MBS at x is chosen as D = ζx(…”

Section: Overviewmentioning

confidence: 99%

“…By extending the results in [18] based on the features of the PPP and PHP, we conduct mathematical analysis and evaluation of the conditional coverage probability, per-user coverage and service unavailability of the above HCN. We present the equations and the detailed derivations for these measures and show how they depend on the system parameters.…”

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confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

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

2017

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“…The authors in [11] have compared to the results of PCP with PPP and concluded that in PPP capacity increases linearly with increase in number of tiers; whereas in PCP the coverage and capacity degrades as number of tiers grow beyond certain threshold. In [12], spatial dependence for BSs deployment have been considered for two tier network i.e., Macro and Pico, where the locations of Macro BSs have been drawn from a PPP and for Pico BSs Poisson hole process (PHP) has been used. The authors have verified results through simulation and claimed that proposed numerical results are closely fitting to the simulation results.…”

Section: Related Workmentioning

confidence: 99%

“…The approaches proposed by [7]- [9] are pessimistic; however, they are tractable under especial cases. On the other hand, approaches proposed in [10]- [12] involve numerical integration but provide better approximation to actual scenarios.…”

Section: Related Workmentioning

confidence: 99%

Theoretical Insights into Coverage Analysis of Cellular Networks

Marvi¹,

Khurram²

2017

SJCMS

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Abstract:Recently tools from stochastic geometry have gained much attention for modeling and analysis of dense cellular networks. Although extensive studies are available in literature in this respect, the approaches are generalized and often lack significance towards practical scenarios where network conditions vary dynamically. The main objective of this research is to provide some insights into the stochastic geometry based analysis of cellular networks through suitable modifications so that it can be used to estimate parameters of interest i.e., intensity and coverage probability for different tiers and scenarios under consideration. The main definition for probability of coverage has been re-defined and complete closed form expression is derived. The intensity for different tiers have been estimated with the help of proposed approach, beyond which no more gain in coverage probability can be achieved. Monte-Carlo simulations have been performed for validation of proposed approach and results are also compared with stateof-the-art approach.

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An Overview of Cognitive Radio Networks

Kusaladharma

Tellambura

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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Radio spectrum needed for applications, such as mobile telephony, digital video broadcasting (DVB), wireless local area networks (WiFi), wireless sensor networks (ZigBee), and Internet of things, is enormous and continues to grow exponentially. Since spectrum is limited and the current usage can be inefficient, the cognitive radio paradigm has emerged to exploit the licensed and/or underutilized spectrum much more effectively. In this article, we present the motivation for and details of cognitive radio. A critical requirement for cognitive radio is the accurate, real‐time estimation of spectrum usage. We thus review various spectrum sensing techniques, propagation effects, interference modeling, spatial randomness, upper layer details, and several existing cognitive radio standards.

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Heterogeneous Cellular Network Models With Dependence

Cited by 94 publications

References 19 publications

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

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

Theoretical Insights into Coverage Analysis of Cellular Networks

An Overview of Cognitive Radio Networks

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