The previous blog highlighted that the Rayleigh fading channel model and the Poisson deployment model are very similar in terms of their tractability and in how realistic they are. It turns out that Rayleigh fading and the PPP are the neutral cases of channel fading and node deployment, respectively, in the following sense:
- For Rayleigh fading, the power fading coefficients are exponential random variables with mean 1, which implies that the ratio of mean and variance is 1. If the ratio is smaller (bigger variance), the fading is stronger. If the variance goes to 0, there is less and less fading.
- For the PPP, the ratio of the mean number of points in a finite region to its variance is 1. If the ratio is larger than 1, the point process is sub-Poissonian, and if the ratio is less than 1, it is super-Poissonian.
Prominent examples of super-Poissonian point processes are clustered processes, where clusters of points are placed at the points of a stationary parent process, and Cox processes, which are PPPs with random intensity measures. Sub-Poissonian processes include hard-core processes (e.g., lattices or Matérn hard-core processes) and soft-core processes (e.g., the Ginibre point process or other determinantal point processes, or hard-core processes with perturbations).
There is no convenient family of point process where the entire range from lattice to extreme clustering can be covered by tuning a single parameter. In contrast, for fading, Nakagami-m fading represents such a family of models. The power fading coefficients are gamma distributed with parameters m and 1/m, i.e., the probability density function is
with variance is 1/m. The case m =1 is the neutral case (Rayleigh fading), while 0<m <1 is strong (super-Rayleigh) fading, and m >1 is weak (sub-Rayleigh) fading. The following table summarizes the different classes of fading and point process models. NND stands for the nearest-neighbor distance of the typical point.
|rigid||no fading (m → ∞)||lattice (deterministic NND)|
|weakly random||m >1 (sub-Rayleigh)||repulsive (sub-Poissonian)|
|neutral||m =1 (Rayleigh)||PPP|
|strongly random||m <1 (super-Rayleigh)||clustered (super-Poissonian)|
|extremely random||m → 0||clustered with mean NND → 0|
(while maintaining density)
It is apparent that the Rayleigh-PPP model offers a good balance in the amount of randomness – not too weak and not too strong. Without specific knowledge on how large the variances in the channel coefficients and in the number of points in a region are, it is the natural default assumption. The other key reason why the combination of exponential (power) fading and the PPP is so symbiotic and popular is its tractability. It is enabled by two properties:
- with Rayleigh fading in the desired link, the SIR distribution is given by the Laplace transform of the interference;
- the Laplace transform, written as an expected product over the points process, has the form of a probability generating functional, which has a closed-form expression for the PPP.
The fading in the interfering channels can be arbitrary; what is essential for tractability is only the fading in the desired link.