Communication in a Poisson Field of Interferers-Part II: Channel Capacity and Interference Spectrum

Pinto, Pedro; Win, Moe Z.

doi:10.1109/twc.2010.07.071283

Cited by 59 publications

(38 citation statements)

References 34 publications

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“…17 As shown in this section, the Gaussian signaling approximation directly achieves the conditional Gaussian representation for aggregate interference. Hence, the ASEP expressions for 17 Note that if the interfering BSs are coded and operating close to capacity, then the signal transmitted by each is Gaussian [13]. However, we are interested in the Gaussian signaling as an approximation for the interfering symbols which are drawn from the distinct constellation S.…”

Section: Gaussian Signaling Approximationmentioning

confidence: 99%

Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial

ElSawy

Sultan-Salem

Alouini

et al. 2017

IEEE Commun. Surv. Tutorials

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422

346

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This paper presents a tutorial on stochastic geometry (SG) based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. The paper starts by modeling and analyzing the baseband interference in a basic cellular network model. Then, it characterizes signal-tointerference-plus-noise-ratio (SINR) and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and rate analysis is presented. Although the main focus of the paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. The paper then extends the baseline unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. Finally, we point out future research directions. arXiv:1604.03689v1 [cs.IT] 13 Apr 2016 2 paper can be extended to other types of wireless networks that impose interference protection around receivers.A. Using SG for Cellular Networks SG was mostly confined to ad hoc and sensor networks to account for their intrinsic spatial randomness. In contrast, cellular networks were mostly assumed to be spatially deployed according to an idealized hexagonal grid. Motivated by its tractability, attempts to promote SG to model cellular networks can be traced back to the late 90's [34], [35]. However, success was not achieved until a decade later [36]- [38]. The theoretical and statistical studies presented in [36]- [38] revealed that cellular networks deviate from the idealized hexagonal grid structure and follows and irregular topology that randomly changes from one geographical location to another. The authors in [36] show that the signal-to-interferenceplus-noise-ratio (SINR) experienced by users in a simulation with actual base station (BS) locations is upper bounded by the SINR of users in idealistic grid network, and lower bounded by the SINR of users in random network. Interestingly, the random network provides a lower bound that is as tight as the upper bound provided by the idealized grid network. However, the lower bound is preferred due to the tractability provided by SG. The authors in [37] show that the spatial patterns exhibited by actual BS locations in different geographical places can be accurately fitted to random spatial patterns obtained via SG. Furthermore, the results in [37] confirm the tight lower bound provided by the random network to the users' SINR in simulations with actual BS locations. Finally, the authors in [38] show that the SINR in grid network converges to the SINR of random network in a strong shadowing environment.Exploiting the tractability of SG, several notable results are obtained for cellular networks. For instance, the downlink baseline operation of cellular networks is characterized in [36]-[42]. Extensions to mu...

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Section: Gaussian Signaling Approximationmentioning

confidence: 99%

Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial

ElSawy

Sultan-Salem

Alouini

et al. 2017

IEEE Commun. Surv. Tutorials

Self Cite

422

346

View full text Add to dashboard Cite

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“…There exists a rich literature on analyzing outage probability for wireless networks with Poisson distributed transmitters [21]- [24]. Given that outage probability has no closed-form expressions [25], [26], a common analytical approach is to derive bounds on outage probability using probabilistic inequalities [27], which are sufficiently simple and tight for evaluating network performance given specific transmission techniques e.g., bandwidth partitioning [28] and multi-antenna techniques [29], [30].…”

Section: A Modeling Multi-cell Cooperationmentioning

confidence: 99%

An Analytical Framework for Multicell Cooperation via Stochastic Geometry and Large Deviations

Huang

Andrews

2013

IEEE Trans. Inform. Theory

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Abstract-Multi-cell cooperation (MCC) is an approach for mitigating inter-cell interference in dense cellular networks. Existing studies on MCC performance typically rely on either over-simplified Wyner-type models or complex system-level simulations. The promising theoretical results (typically using Wyner models) seem to materialize neither in complex simulations nor in practice. To more accurately investigate the theoretical performance of MCC, this paper models an entire plane of interfering cells as a Poisson random tessellation. The base stations (BSs) are then clustered using a regular lattice, whereby BSs in the same cluster mitigate mutual interference by beamforming with perfect channel state information. Techniques from stochastic geometry and large-deviation theory are applied to analyze the outage probability as a function of the mobile locations, scattering environment and the average number of cooperating BSs per cluster, . For mobiles near the centers of BS clusters, it is shown that outage probability diminishes as O(e − ν 1 ) with 0 ≤ ν1 ≤ 1 if scattering is sparse, and as O( −ν 2 ) with ν2 proportional to the signal diversity order if scattering is rich. For randomly located mobiles, regardless of scattering, outage probability is shown to scale as O( −ν 3 ) with 0 ≤ ν3 ≤ 0.5. These results confirm analytically that cluster-edge mobiles are the bottleneck for network coverage and provide a plausible analytic framework for more realistic analysis of other multi-cell techniques.

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“…Dynamic interference in wireless networks was introduced in [4,5] by considering a fast-varying (symbol-by-symbol) active transmitter set, with locations governed by a homogeneous Poisson point process. In particular, it was shown that the interfering signal in each time slot is α-stable.…”

Section: Introduction Modern Wireless Communication Network Are Imentioning

confidence: 99%

Wireless Communication in Dynamic Interference

Egan

Clavier

Freitas

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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Abstract-Fast varying active transmitter sets are a key feature of wireless communication networks with very short transmissions arising in machine-to-machine communications. A consequence is that the interference is dynamic, leading to nonGaussian statistics. In this paper, we study the behavior of largescale communication networks in the presence of isotropic α-stable interference, which forms a model for dynamic interference. We first characterize the achievable rate of each link by considering a non-Gaussian input distribution, which is shown to outperform a Gaussian input. Moreover, we analyze the area spectral efficiency, which is the total rate per square meter. Our analysis suggests that analogously to the common model of slowly varying active transmitter sets, dense networks maximize the area spectral efficiency.

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Communication in a Poisson Field of Interferers-Part II: Channel Capacity and Interference Spectrum

Cited by 59 publications

References 34 publications

Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial

Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial

An Analytical Framework for Multicell Cooperation via Stochastic Geometry and Large Deviations

Wireless Communication in Dynamic Interference

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