rician fading model
Fading model for narrowband fading channel gain.
Here, there is a dominant line of sight path.
Recall that the channel being studied is of the form
\begin{equation}
\beta(t) = \alpha_0(t)e^{j\phi_0(t)} + \sum_{n = 1}^{N} \alpha_n(t)e^{j\phi_n(t)}
\end{equation}
The first part is complex gaussian, but the sum follows the rayleigh fading model.
We know the variance (power) of the rayleigh part is \(2\sigma^2\), this is the expected power of the sum of all the NLOS parts of the channel.
The variance (power) of the line of sight channel component is \(s^2\)
The Rician K-factor is \(s^2/(2\sigma^2)\).
- When K-factor is high, the distribution behaves like a gaussian
(mostly LOS)
- When K-factor is low, the distribution behaves like the rayleigh distribution