Closed-Form Path-Loss Predictor for Gaussianly Distributed Nodes

Abdulla, Mouhamed; Shayan, Yousef R.

doi:10.1109/icc.2010.5502200

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Despite various conjectures for reconstructing a network based on inhomogeneous techniques, for example , the random uniform distribution assumption has been considered in analytical research, for example . Essentially, if we consider

A_{0} \in {double-struckR}^{+}

to be the surface area of a particular network lattice, and

n_{0} \in {double-struckN}^{*}

to represent the scale of the architecture, then the uniform areal density will be given by

ρ_{0} ≜ n_{0} / A_{0}

.…”

Section: Characteristics Of the Network Modelmentioning

confidence: 99%

Large-scale fading behavior for a cellular network with uniform spatial distribution

Abdulla

Shayan

2014

Wirel. Commun. Mob. Comput.

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Large-scale fading (LSF) between interacting nodes is a fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel-losses in general, and the inevitability of random spatial geometry in real-life wireless networks, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the LSF indicator. Therefore, in exact closed-form notation, we generically derived the LSF distribution between a prepositioned reference base-station and an arbitrary node for a multi-cellular random network model. In fact, we provided an explicit and definitive formulation that considered at once: the lattice profile, the users' random geometry, the effect of the far-field phenomenon, the path-loss behavior, and the stochastic impact of channel scatters. The veracity and accuracy of the theoretical analysis were also confirmed through Monte Carlo simulations.

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A_{0} \in {double-struckR}^{+}

to be the surface area of a particular network lattice, and

n_{0} \in {double-struckN}^{*}

to represent the scale of the architecture, then the uniform areal density will be given by

ρ_{0} ≜ n_{0} / A_{0}

.…”

Section: Characteristics Of the Network Modelmentioning

confidence: 99%

Large-scale fading behavior for a cellular network with uniform spatial distribution

Abdulla

Shayan

2014

Wirel. Commun. Mob. Comput.

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show abstract

“…The outage probability based on the PDF of path losses for uniformly distributed UEs within the circle is investigated in [16]. For Gaussianly distributed UEs in cellular networks, the closedform PDF of path losses is derived in [17] through the Taylor series expansion. Nevertheless, it is difficult to see the impact of the parameters of the BS, cell radius and deployment scenarios on the PDF of path losses.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Path Losses for Sectorized Wireless Networks

Yan

Zhu

et al. 2017

IEEE Trans. Commun.

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In modern mobile communication networks, such as 3G and 4G networks, sectorized antennas have been widely used to divide each cell into multiple sectors in order to improve coverage, spectrum efficiency and quality of service. Large-scale path loss from a transmitting antenna to a receiving antenna should include (1) propagation attenuation that depends on transmission distance, (2) shadowing that depends on surrounding environment, and (3) antenna loss that depends on a sectorized antenna pattern and transmission angle. An in-depth analysis of statistical characteristics of large-scale path losses involving with these three factors is crucial for the design, operation, evaluation and optimization of modern sectorized wireless networks. In this paper, a sectorized antenna pattern is for the first time considered in the derivation of a closed-form expression of a probability density function (PDF) of large-scale path losses. Specifically, we first discover that the normalized PDF of propagation attenuation plus shadowing, which can be approximated by the Gaussian mixture model (GMM) with all system parameters, is fully determined by our newly-defined metric 10 ln 10 β σs , namely the attenuation exponent β to standard deviation of shadowing σs ratio (ASR). The convolution of GMM and antenna loss statistics is elaborately transformed to a series of differential equations. A closed-form PDF of large-scale path losses with sectorized antenna pattern can be obtained by solving these differential equations. To reduce the computational complexity, we further prove that the exciting sources of these differential equations can be tightly approximated by weighted Gaussian functions and, thus, the final solutions (i.e. PDF of path losses) can be derived in the form of Gaussian and Dawson functions. Our analytical results are verified by extensive numerical computation and Monte Carlo simulation results, e.g. the impact of ASR on the shape of PDF of propagation attenuation plus shadowing. Compared with traditional Gaussian-fitting approach, our newlyderived PDF of large-scale path losses with sectorized antenna patterns is at least two orders of magnitude more accurate in terms of Kullback-Leibler (KL) divergence under typical

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The Dirac-Delta functions for signal path-loss modeling at mid-sized urban areas

Nieto-Chaupis

2014

2014 IEEE Brasil RFID

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A study of the Dirac-Delta functions with polynomial argument for modeling the signal path-loss in mid-sized urban areas is presented. From the fundamental "spike" property of the Dirac-Delta functions, or simply Delta, the formulation of nonlinear input/output (I/O) integrals with unknown parameters applied to a scheme of urban zone containing buildings and delimited areas was empathized. The modeling assumes that the Delta function contains a polynomial argument with unknown parameters. The extracted parameters and compared to the ones obtained from Monte Carlo steps, in order to measure the resulting signal intensity degradation at the mid-sized urban areas. For a case of application, the obtained results are compared with the well-known models like Okumura-Hata thereby yielding averaged errors ranging between 2.6% and 12%, roughly.

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Closed-Form Path-Loss Predictor for Gaussianly Distributed Nodes

Cited by 4 publications

References 31 publications

Large-scale fading behavior for a cellular network with uniform spatial distribution

Large-scale fading behavior for a cellular network with uniform spatial distribution

Statistical Analysis of Path Losses for Sectorized Wireless Networks

The Dirac-Delta functions for signal path-loss modeling at mid-sized urban areas

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