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The Okumura model is a radio propagation model that was built using data collected in the city of Tokyo, Japan. The model is ideal for using in cities with many urban structures but not many tall blocking structures. The model served as a base for the Hata model. The Okumura model was built into three modes: for urban, suburban and open areas ...
The Hata model is a radio propagation model for predicting the path loss of cellular transmissions in exterior environments, valid for microwave frequencies from 150 to 1500 MHz. It is an empirical formulation based on the data from the Okumura model, and is thus also commonly referred to as the Okumura–Hata model. [1]
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The two-rays ground-reflection model is a multipath radio propagation model which predicts the path losses between a transmitting antenna and a receiving antenna when they are in line of sight (LOS). Generally, the two antenna each have different height. The received signal having two components, the LOS component and the reflection component ...
It is the most often cited of the COST 231 models (EU funded research project ca. April 1986 – April 1996), [1] also called the Hata Model PCS Extension. This model is the combination of empirical and deterministic models for estimating path loss in an urban area over frequency range of 800 MHz to 2000 MHz. [2]
The log-distance path loss model is a radio propagation model that predicts the path loss a signal encounters inside a building or densely populated areas over long distance. While the log-distance model is suitable for longer distances, the short-distance path loss model is often used for indoor environments or very short outdoor distances.
An example of a maximum cut. In a graph, a maximum cut is a cut whose size is at least the size of any other cut. That is, it is a partition of the graph's vertices into two complementary sets S and T, such that the number of edges between S and T is as large as possible. Finding such a cut is known as the max-cut problem.
Any random graph model (at a fixed set of parameter values) results in a probability distribution on graphs, and those that are maximum entropy within the considered class of distributions have the special property of being maximally unbiased null models for network inference [2] (e.g. biological network inference).