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Statistical characterization of the signal-to-interference-plus-noise ratio(SINR) via its cumulative distribution function (CDF) is ubiquitous in a vastmajority of technical contributions in the area of cellular networks since itboils down to averaging the Laplace transform of the aggregate interference, abenefit accorded at the expense of confinement to the simplistic Rayleighfading. In this work, to capture diverse fading channels that appear inrealistic outdoor/indoor wireless communication scenarios, we tackle theproblem differently. By exploting the moment generating function (MGF) of theSINR, we succeed in analytically assessing cellular networks performance overthe shadowed {\kappa}-{\mu}, {\kappa}-{\mu} and {\eta}-{\mu} fading models. Thelatter offer high flexibility by capturing diverse fading channels includingRayleigh, Nakagami-m, Rician, and Rician shadow fading distributions. Thesechannel models have been recently praised for their capability to accuratelymodel dense urban environments, future femtocells and device-to-device (D2D)shadowed channels. In addition to unifying the analysis for different channelmodels, this work integrates, for the first time, the coverage, the achievablerate, and the bit error probability (BEP), which are largely treated separatelyin the literature. The developed model and analysis are validated over a broadrange of simulation setups and parameters.

Unified Stochastic Geometry Modeling and Analysis of Cellular Networks in LOS/NLOS and Shadowed Fading