Line-of-sight channels normally contain many propagation paths, whereof one is the direct path and the others are paths were the signals are scattered on different objects. The interaction between these paths lead to fading phenomena, which is often modeled statistically using Rician fading (sometimes written as Ricean fading). The main assumption is that the complex-valued channel coefficient in the complex baseband can be divided into two parts:
where is the magnitude of the direct path between the transmitter and receiver and is the corresponding phase shift. The second part, , represents all the scattered paths. This part is separated from the direct path since it consists of many paths, each being of roughly the same strength but substantially weaker than the direct path. It is modeled by Rayleigh fading, which implies . The complex Gaussian distribution is motivated by the central limit theorem, which says that the sum of many independent and identically distributed random variables is approximately Gaussian.
Under these assumptions, the magnitude of the channel coefficient is Rice/Rician distributed, which is why it is called Rician fading. More precisely, , which depends on the magnitude and the variance of the scattering.
Interestingly, the distribution does not depend on the phase , because the magnitude removes phases and and are equally distributed. Hence, it is common to omit in the performance analysis of Rician fading channels. As long as the channel is perfectly known at the receiver, it will not make any difference when quantifying the SNR or capacity.
The common misunderstanding
We cannot neglect the phase when analyzing practical systems where the receiver needs to estimate the channel. The value of varies at the same pace as , and for exactly the same reason: The transmitter or receiver moves, which induces small phase shifts in every path. Since contains a large number of paths with approximately the same magnitude but random phases, the sum of the many terms with random phases give rise to the Gaussian distribution. The phase-shift of the direct path must be treated separately since this path is substantially stronger.
Unfortunately, my experience is that the vast majority of paper on Rician fading channels ignores this fact by simply treating as a deterministic constant that is perfectly known at the receiver. I have done this myself in several papers, including this one from 2010 that has received 200+ citations. Unfortunately, the results obtained with that simplified model are practically questionable. If we don’t know in advance, how can we know ? At best, the results obtained with a perfectly known can be interpreted as an upper bound on what is practically achievable.
We analyzed the importance of correctly modeled random phases in a recent paper on cell-free massive MIMO. We compared the performance when using an ideal phase-aware MMSE estimator and a phase-unaware LMMSE estimator. The spectral efficiency loss due to a lack of knowing ranges from 2% to 50% in different simulations, depending on the pilot length and interference situation. Hence, there are cases where it is very important to know the phase correctly.