Path loss model that uses a dual-slope approach with different path loss exponents for different distance ranges. Models signal attenuation with a breakpoint distance that divides propagation into two regions, each with its own path loss characteristics. Particularly useful for environments where propagation behavior changes significantly at a certain distance, such as indoor/outdoor transitions or urban scenarios with varying obstacle densities.

Implements the free space path loss model, which models signal attenuation over distance according to the Friis transmission equation. Calculates signal power loss as a function of distance, wavelength, and configurable parameters. The alpha parameter controls the path loss exponent (default 2), while the systemLoss parameter accounts for hardware inefficiencies. Provides an ideal baseline model for wireless propagation in environments with line-of-sight and no obstacles.

Path loss model that combines free space path loss with log-normal shadowing. Extends the deterministic free space model by adding a random component that follows a normal distribution with zero mean and configurable standard deviation (sigma). This accounts for signal variations due to obstacles and environmental factors, providing more realistic wireless signal propagation modeling than pure deterministic models.

Implements the Nakagami fading model for wireless signal propagation. This statistical model extends the free space path loss model by adding multipath fading effects. The shapeFactor parameter determines the severity of fading, with lower values representing more severe fading conditions. When shapeFactor equals 1, the Nakagami distribution reduces to the Rayleigh distribution, modeling severe multipath with no line-of-sight component.

Models Rayleigh fading in wireless channels, which occurs when signals propagate through multipath environments with no dominant line-of-sight path. This model extends free space path loss with stochastic variations based on the Rayleigh distribution, making it suitable for dense urban and indoor environments. Represents a special case of Rician fading with no dominant signal component, providing realistic signal strength fluctuations for non-line-of-sight wireless communication scenarios.

Models Rician fading in wireless channels, which occurs when a signal reaches the receiver through both direct line-of-sight and multipath propagation. The k parameter (in dB) controls the ratio between the direct and scattered signal components, with higher values indicating stronger line-of-sight conditions. This model extends free space path loss with stochastic variations based on the Rician distribution, making it suitable for indoor and urban environments where a dominant signal path exists alongside weaker multipath components.

Implements the Stanford University Interim (SUI) path loss model, an empirical model used to predict radio signal attenuation over various terrain types. Path loss is calculated based on distance, frequency, and antenna heights, with parameter sets defined for different environments.

Implements the two-ray ground reflection path loss model for wireless communications. This model accounts for both the direct line-of-sight path and a ground-reflected path between transmitter and receiver. For distances less than the crossover distance, it uses the free space model; beyond that, it applies the two-ray formula which considers transmitter and receiver heights above ground. (The crossover distance is computed from the heights and the frequency.) This model provides more accurate predictions than free space models for medium to long distances in environments with significant ground reflections.

Path loss model that accounts for interference between direct and ground-reflected signal paths. Considers phase differences, polarization effects (horizontal or vertical), and ground permittivity to accurately model constructive and destructive interference patterns, yielding a more accurate approximation than ~TwoRayGroundReflection even on short distances.

This is a statistical path-loss model for UWB propagation in residential environments, for both line-of-sight (LOS) and non-line-ofsight (NLOS) cases. It implements the Ghassmezadeh stochastic UWB channel path loss model.