Analytical Modeling of Heterogeneous Cellular Networks: by Sayandev Mukherjee

By Sayandev Mukherjee

This self-contained creation exhibits how stochastic geometry ideas can be utilized for learning the behaviour of heterogeneous mobile networks (HCNs). The unified therapy of analytic effects and methods, amassed for the 1st time in one quantity, comprises the mathematical instruments and strategies used to derive them. A unmarried canonical challenge formula encompassing the analytic derivation of sign to Interference plus Noise Ratio (SINR) distribution within the such a lot widely-used deployment eventualities is gifted, including purposes to platforms in keeping with the 3GPP-LTE commonplace, and with implications of those analyses at the layout of HCNs. an overview of different releases of the LTE common and the gains proper to HCNs can be supplied. A invaluable reference for practitioners trying to enhance the rate and potency in their community layout and optimization workflow, and for graduate scholars and researchers looking tractable analytical effects for functionality metrics in instant HCNs.

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By Sayandev Mukherjee

This self-contained creation exhibits how stochastic geometry ideas can be utilized for learning the behaviour of heterogeneous mobile networks (HCNs). The unified therapy of analytic effects and methods, amassed for the 1st time in one quantity, comprises the mathematical instruments and strategies used to derive them. A unmarried canonical challenge formula encompassing the analytic derivation of sign to Interference plus Noise Ratio (SINR) distribution within the such a lot widely-used deployment eventualities is gifted, including purposes to platforms in keeping with the 3GPP-LTE commonplace, and with implications of those analyses at the layout of HCNs. an overview of different releases of the LTE common and the gains proper to HCNs can be supplied. A invaluable reference for practitioners trying to enhance the rate and potency in their community layout and optimization workflow, and for graduate scholars and researchers looking tractable analytical effects for functionality metrics in instant HCNs.

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Additional resources for Analytical Modeling of Heterogeneous Cellular Networks: Geometry, Coverage, and Capacity

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4 to apply only to continuous-valued Z. 5. 11). e. the derivative of the unit step function ⎧ ⎨1, x ≥ 0, U(x) = ⎩0, x < 0. 5 Applicability of PPP to real-world deployments We shall develop tools for analyzing the distribution of the SINR in multi-tier HCNs where the locations of the BSs in the tiers are modeled as points of independent homogeneous PPPs. However, as we have seen, the essential feature of the PPP is its modeling of complete spatial randomness, and this is exactly opposed to the goals of network design.

5. 11). e. the derivative of the unit step function ⎧ ⎨1, x ≥ 0, U(x) = ⎩0, x < 0. 5 Applicability of PPP to real-world deployments We shall develop tools for analyzing the distribution of the SINR in multi-tier HCNs where the locations of the BSs in the tiers are modeled as points of independent homogeneous PPPs. However, as we have seen, the essential feature of the PPP is its modeling of complete spatial randomness, and this is exactly opposed to the goals of network design. In particular, a network operator most definitely does not want to sprinkle BSs over the deployment region independently and at random.

E. with constant density λ) generalize easily to the inhomogeneous PPP, where the number of points of the process in a region A is not λ × area(A) but A λ(x, y)dx dy, where the function λ(x, y) of the coordinates (x, y) is now called the intensity function (or just intensity, for short) of the PPP. Note that if the PPP is homogeneous, its intensity function is constant, and the density of the PPP equals its intensity. More formally, the definition of a (possibly inhomogeneous) PPP is the following (Møller & Waagepetersen, 2004, Defn.

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