Get Analytical Modeling of Heterogeneous Cellular Networks: PDF

By Sayandev Mukherjee

ISBN-10: 1107050944

ISBN-13: 9781107050945

This self-contained creation indicates how stochastic geometry recommendations can be utilized for learning the behaviour of heterogeneous mobile networks (HCNs). The unified remedy of analytic effects and ways, accumulated for the 1st time in one quantity, contains 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 so much widely-used deployment situations is gifted, including functions to platforms in accordance with the 3GPP-LTE regular, and with implications of those analyses at the layout of HCNs. an overview of the various releases of the LTE ordinary and the gains appropriate to HCNs can also be supplied. A worthwhile 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 info for Analytical Modeling of Heterogeneous Cellular Networks: Geometry, Coverage, and Capacity

Example text

E. H = exp(σ Z − σ 2 /2), Z ∼ N (0, 1). A recent result (Błaszczyszyn, Karray & Keeler, 2013, Thm. 6) with no fading as σ → ∞. , 2013, Thm. 3, Rem. d. log-normal fading on all links to the user, and the standard deviation of the log-normal fading is sufficiently large. , 2013, Thm. 3), the homogeneous PPP model is also sufficient to approximate the distribution of the SINR at the arbitrarily located user even if we only consider the one-dimensional point process of received powers at the user from the BSs of the original deterministic deployment that are at some minimum distance from the user and/or less than some maximum distance from the user.

9) exceed γl with probability 1, they simply drop out of the corresponding joint CCDFs, which therefore no longer describe the distribution of Γjl and Γ(l) , respectively. 6) if γl = 0 we have P{Γj1 > γ1 , . . , Γjk > γk } = P{Γj1 > γ1 , . . , Γjl−1 > γl−1 , Γjl+1 > γl+1 , . . , Γjk > γk }, which is not a descriptor of the distribution of Γjl . e. if x ≥ 0, we say that x is non-negative-valued, whereas if x > 0 we say that x is positive-valued. We also employ the equivalent notation x ∈ R+ in place of x ≥ 0, and x ∈ R++ in place of x > 0.

8) so for any A ⊆ Rk , P{(Γj1 , . . 8) over A. 3) P{(Γj1 , . . , Γjk ) ∈ A} = P{γ l < Γjl ≤ γ l , l = 1, . . 6). 2 Joint CCDF of SINRs from BSs ordered by serving BS selection criterion We stated previously that the serving BS selection criterion yields the serving BS, either directly in the case of a single-tier deployment, or through the serving tier index I if there are multiple tiers. , and then selects the “best” one as the serving BS. e. the “best”) BS, we may also be interested in the joint CCDF of the SINRs at the user when receiving from, say, the serving (“best”) and “second-best” BSs.

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Analytical Modeling of Heterogeneous Cellular Networks: Geometry, Coverage, and Capacity by Sayandev Mukherjee

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