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 range and pathloss functions.

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

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.

Model	Description	Frequency	Combinations	Limitations
freespace (`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 (`Rain`)	Propagation of a radio wave signal and its path loss in rain. For more information, see [3].	1 GHz to 1000 GHz	Can be combined with any other propagation model	Assumes line of sight
gas (`Gas`)	Propagation of radio wave signal and its path loss due to oxygen and water vapor. For more information, see [5].	1GHz to 1000 GHz	Can be combined with any other propagation model	Assumes line of sight
fog (`Fog`)	Propagation of the radio wave signal and its path loss in cloud and fog. For more information, see [2].	10GHz to 1000 GHz	Can be combined with any other propagation model	Assumes line of sight

Empirical

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.

Model	Description	Frequency	Combinations	Limitations
close-in (`CloseIn`)	Propagation of signals in urban macro cell scenarios. For more information, see [1].	No enforced range	Can be combined with rain, fog, and gas	—

Terrain

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.

Model	Description	Frequency	Combinations	Limitations
longley-rice (`LongleyRice`)	Also known as Irregular Terrain Model (ITM). For more information, see [4].	20 MHz to 20 GHz	Can be combined with rain, fog, and gas	Designed for antenna heights from 0.5 to 3000 m Designed for distances from 1 to 2000 km
tirem (`TIREM`)	Terrain Integrated Rough Earth Model™	1 MHz to 1000 GHz	Can be combined with rain, fog, and gas	Requires access to external TIREM library Antenna height maximum is 30000 m

Ray Tracing

Ray tracing models, represented by RayTracing objects, compute propagation paths using 3-D environment geometry [7][8]. 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 [5][6] at the specified frequency.

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)	Supports calculation of propagation paths for up to 100 path reflections and two edge diffractions. Calculates an approximate number of propagation paths with exact geometric accuracy. Computational complexity increases linearly with the number of reflections and exponentially with the number of diffractions. The SBR method is generally faster than the image method.	100 MHz to 100 GHz	Can be combined with rain, fog, and gas	Does not include effects from corner diffraction, refraction, or diffuse scattering
image	Supports calculation of propagation paths for up to two path reflections. Calculates an exact number of propagation paths with exact geometric accuracy. Computational complexity increases exponentially with the number of reflections.	100 MHz to 100 GHz	Can be combined with rain, fog, and gas	Does not include effects from diffraction, refraction, or diffuse scattering

SBR Method

This figure illustrates the SBR method for calculating propagation paths from a transmitter, Tx, to a receiver, Rx.

$Ray tracing reflection and diffraction using the SBR method$

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 [9][10]. 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 [10].

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.

Image Method

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.

Ray tracing using the image method

References

[1] Sun, Shu, Theodore S. Rappaport, Timothy A. Thomas, Amitava Ghosh, Huan C. Nguyen, Istvan Z. Kovacs, Ignacio Rodriguez, Ozge Koymen, and Andrzej Partyka. “Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications.” IEEE Transactions on Vehicular Technology 65, no. 5 (May 2016): 2843–60. https://doi.org/10.1109/TVT.2016.2543139.

[2] International Telecommunications Union Radiocommunication Sector. Attenuation due to clouds and fog. Recommendation P.840-6. ITU-R, approved September 30, 2013. https://www.itu.int/rec/R-REC-P.840/en.

[3] International Telecommunications Union Radiocommunication Sector. Specific attenuation model for rain for use in prediction methods. Recommendation P.838-3. ITU-R, approved March 8, 2005. https://www.itu.int/rec/R-REC-P.838/en.

[4] Hufford, George A., Anita G. Longley, and William A.Kissick. A Guide to the Use of the ITS Irregular Terrain Model in the Area Prediction Mode. NTIA Report 82-100. National Telecommunications and Information Administration, April 1, 1982.

[5] International Telecommunications Union Radiocommunication Sector. Effects of Building Materials and Structures on Radiowave Propagation Above About 100MHz. Recommendation P.2040. ITU-R, approved August 23, 2023. https://www.itu.int/rec/R-REC-P.2040/en.

[6] International Telecommunications Union Radiocommunication Sector. Electrical Characteristics of the Surface of the Earth. Recommendation P.527. ITU-R, approved September 27, 2021. https://www.itu.int/rec/R-REC-P.527/en.

[7] Yun, Zhengqing, and Magdy F. Iskander. “Ray Tracing for Radio Propagation Modeling: Principles and Applications.” IEEE Access 3 (2015): 1089–1100. https://doi.org/10.1109/ACCESS.2015.2453991.

[8] Schaubach, K.R., N.J. Davis, and T.S. Rappaport. “A Ray Tracing Method for Predicting Path Loss and Delay Spread in Microcellular Environments.” In [1992 Proceedings] Vehicular Technology Society 42nd VTS Conference - Frontiers of Technology, 932–35. Denver, CO, USA: IEEE, 1992. https://doi.org/10.1109/VETEC.1992.245274.

[9] International Telecommunications Union Radiocommunication Sector. Propagation by diffraction. Recommendation P.526-15. ITU-R, approved October 21, 2019. https://www.itu.int/rec/R-REC-P.526/en.

[10] Keller, Joseph B. “Geometrical Theory of Diffraction.” Journal of the Optical Society of America 52, no. 2 (February 1, 1962): 116. https://doi.org/10.1364/JOSA.52.000116.