Choose a Propagation Model
Propagation models allow you to predict the propagation and attenuation of radio
signals as the signals travel through the environment. You can simulate different models
by using the
propagationModel function. Additionally, you can determine the range and
path loss of radio signals in these simulated models by using the
The following sections describe various propagation and ray tracing models. The tables
in each section list the models that are supported by the
propagationModel function and compare, for each model, the
supported frequency ranges, model combinations, and limitations.
Atmospheric propagation models predict path loss between sites as a function of distance. These models assume line-of-sight (LOS) conditions and disregard the curvature of the Earth, terrain, and other obstacles.
|freespace (||Ideal propagation model with clear line of sight between transmitter and receiver||No enforced range||Can be combined with rain, fog, and gas||Assumes line of sight|
|rain (||Propagation of a radio wave signal and its path loss in rain. For more information, see .||1 GHz to 1000 GHz||Can be combined with any other propagation model||Assumes line of sight|
|gas (||Propagation of radio wave signal and its path loss due to oxygen and water vapor. For more information, see .||1GHz to 1000 GHz||Can be combined with any other propagation model||Assumes line of sight|
|fog (||Propagation of the radio wave signal and its path loss in cloud and fog. For more information, see .||10GHz to 1000 GHz||Can be combined with any other propagation model||Assumes line of sight|
Like atmospheric propagation models, empirical models predict path loss as a function of distance. Unlike atmospheric models, the close-in empirical model supports non-line-of-sight (NLOS) conditions.
Terrain propagation models assume that propagation occurs between two points over a slice of terrain. Use these models to calculate the point-to-point path loss between sites over irregular terrain, including buildings.
Terrain models calculate path loss from free-space loss, terrain and obstacle diffraction, ground reflection, atmospheric refraction, and tropospheric scatter. They provide path loss estimates by combining physics with empirical data.
|longley-rice (||Also known as Irregular Terrain Model (ITM). For more information, see .||20 MHz to 20 GHz||Can be combined with rain, fog, and gas||
|tirem (||Terrain Integrated Rough Earth Model™||1 MHz to 1000 GHz||Can be combined with rain, fog, and gas||
Ray tracing models, represented by
RayTracing objects, compute propagation paths
using 3-D environment geometry . They determine the path
loss and phase shift of each ray using electromagnetic analysis, including tracing
the horizontal and vertical polarizations of a signal through the propagation path.
The path loss calculations include free-space loss, reflection losses, and
diffraction losses. For each reflection and diffraction, the model calculates loss
on the horizontal and vertical polarizations by using the Fresnel equation, the
Uniform Theory of Diffraction (UTD), the relevant angles, and the real relative
permittivity and conductivity of the interaction materials  at the specified
While the other supported models compute single propagation paths, ray tracing models compute multiple propagation paths.
These models support both 3-D outdoor and indoor environments.
|Ray Tracing Method||Description||Frequency||Combinations||Limitations|
|shooting and bouncing rays (SBR)||
||100 MHz to 100 GHz||Can be combined with rain, fog, and gas||Does not include effects from corner diffraction, refraction, or diffuse scattering|
||100 MHz to 100 GHz||Can be combined with rain, fog, and gas||Does not include effects from diffraction, refraction, or diffuse scattering|
This figure illustrates the SBR method for calculating propagation paths from a transmitter, Tx, to a receiver, Rx.
The SBR method launches many rays from a geodesic sphere centered at Tx. The geodesic sphere enables the model to launch rays that are approximately uniformly spaced.
Then, the method traces every ray from Tx and can model different types of interactions between the rays and surrounding objects, such as reflections, diffractions, refractions, and scattering. Note that the current implementation of the SBR method considers only reflections and edge diffractions.
When a ray hits a flat surface, shown as R, the ray reflects based on the law of reflection.
When a ray hits an edge, shown as D, the ray spawns many diffracted rays based on the law of diffraction . Each diffracted ray has the same angle with the diffracting edge as the incident ray. The diffraction point then becomes a new launching point and the SBR method traces the diffracted rays in the same way as the rays launched from Tx. A continuum of diffracted rays forms a cone around the diffracting edge, which is commonly known as a Keller cone .
For each launched ray, the SBR method surrounds Rx with a sphere, called a reception sphere, with a radius that is proportional to the distance the ray travels and the average number of degrees between the launched rays. If the ray intersects the sphere, then the model considers the ray a valid path from Tx to Rx. The SBR method corrects the valid paths so that the paths have exact geometric accuracy.
When you increase the number of rays by decreasing the number of degrees between rays, the reception sphere becomes smaller. As a result, in some cases, launching more rays results in fewer or different paths. This situation is more likely to occur with custom 3-D scenarios created from STL files or triangulation objects than with scenarios that are automatically generated from OpenStreetMap® buildings and terrain data.
The SBR method finds paths using double-precision floating-point computations.
This figure illustrates the image method for calculating the propagation path of a single reflection ray for the same transmitter and receiver as the SBR method. The image method locates the image of Tx with respect to a planar reflection surface, Tx'. Then, the method connects Tx' and Rx with a line segment. If the line segment intersects the planar reflection surface, shown as R in the figure, then a valid path from Tx to Rx exists. The method determines paths with multiple reflections by recursively extending these steps. The image method finds paths using single-precision floating-point computations.
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