By Sayandev Mukherjee

ISBN-10: 1107050944

ISBN-13: 9781107050945

This self-contained advent indicates how stochastic geometry thoughts can be utilized for learning the behaviour of heterogeneous mobile networks (HCNs). The unified therapy of analytic effects and ways, gathered 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 so much widely-used deployment situations is gifted, including purposes to structures according to the 3GPP-LTE regular, and with implications of those analyses at the layout of HCNs. an overview of the several releases of the LTE ordinary and the positive aspects suitable to HCNs is usually 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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Extra info for Analytical Modeling of Heterogeneous Cellular Networks: Geometry, Coverage, and Capacity

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It can be shown that knowledge of the mean measure of a PPP is equivalent to knowledge of its intensity function, and vice versa. Thus, either the intensity function or its integral, the mean measure, can be used to describe a PPP. e. with a constant density on the plane. It is important to note that the PPP model is not restricted to a finite region, so in effect we are assuming that the BS deployment is infinite. This implies that a user anywhere in the network sees interference from infinitely many BSs.

The set of point patterns generated by this stochastic model is called a point process. In other words, a point process is identified with the set of its events. A basic descriptor of a point process is the function that counts the number of points of the process that lie in a given region. Since the process is random, this number is a random variable. The marginal distribution of this random variable, and the joint distribution of a collection of such random variables, each denoting the number of points of the process in a different region, are important descriptors of the point process.

D. Bin(1, p1 ) random variables. Prove that N1 = N i=1 Xi is Poisson with mean p1 μ. Hint: Show by conditioning on N that the moment generating function φN1 (s) = EsN1 is given by φN1 (s) = φN (φX (s)), where X is Bin(1, p1 ), then use the known forms of φX (s) and φN (s) and the fact that a distribution is uniquely identified by its moment generating function. 3. Prove that, for any n1 and n2 , P{N1 = n1 , N2 = n2 } = P{N1 = n1 } P{N2 = n2 }, where N1 is Poisson with mean p1 μ, N2 is Poisson with mean p2 μ, and p2 = 1 − p1 .

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

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