When stochastic geometry applications in wireless networking were still in their infancy or youth, I was frequently asked “Do you believe in the PPP model?”. I usually answered with a counter-question:“Do you believe in the Rayleigh fading model?”. This “answer” was motivated by the high likelihood that the person asking
- familiar with the idea of modeling the effects of multi-path propagation using Rayleigh fading;
- found it not only acceptable but quite natural to use a model with obvious shortcomings and limitations, for the sake of analytical tractability and design insight.
It usually turned out that the person quickly realized that the apparent shortcomings of the PPP model are quite comparable to those of the Rayleigh fading model, and that, conversely, they both share a high level of tractability.
Surely if one can accept that wireless signals propagate along infinitely many paths of comparable propagation loss with independent phases, resulting in a random received power with infinite support, one can accept a point process model with infinitely many points that are, loosely speaking, independently placed. If one can accept that at 0 dBm transmit power, there is a positive probability that the power received over a 1 km distance exceeds 90 dBm (1 MW), then surely one can accept that there is a positive probability that two points are separated by only 1 cm.
So why is it that Rayleigh fading was (and perhaps still is) more acceptable than the PPP? Is it just that Rayleigh fading has been used for wireless channel modeling for much longer than the PPP? Perhaps. But maybe part of the answer lies in what prompts us to use stochastic models in the first place.
Fundamentally there is no randomness in wireless propagation. If we know the characteristics of the antennas and the locations and properties of all objects, we can calculate the channel parameters exactly (say by raytracing) – and if there is no mobility, the channel stays fixed forever. So why introduce randomness where there is none? There are two reasons:
- Raytracing is computationally expensive
- The results obtained only apply to one very specific scenario. If a piece of furniture is moved a bit, we need to start from scratch.
Often the goal is to design a communication architecture, but such design cannot be based on the layout of a specific room. So we need a model that captures the characteristics of the channels in many rooms in many buildings, but obtaining such a large data set would be very expensive, and it would be hard to derive any useful insight from it. In contrast, a random model offers simplicity and superior tractability.
Similarly, in a network of transceivers, we could in principle assume that all their locations (and mobility vectors) are known, plus their transmit powers. Then, together with the (deterministic) channels, the interference power would be a deterministic quantity. This is very impractical and, as above, we do not want to decide on the standards for 7G cellular networks based on a given set of base station and user (and pet and vacuum robot and toaster and cactus) locations. Instead we aim for the robust design that a random spatial model (i.e., a point process) offers.
Another aspect here is that the channel fading process is often perceived (and modeled) as a random process in time. Although any temporal change in the channel is but a consequence of a spatial change, it is convenient to disregard the purely spatial nature of fading and assume it to be temporal. Then we can apply the standard machinery for temporal random processes in the performance analysis of a link. This includes, in particular, ergodicity, which conveniently allows us to argue that over some time period the performance will be close to that predicted by the ensemble average. The temporal form of ergodicity appears to be much more ingrained in our thinking than its spatial counterpart, which is at least as powerful: in an ergodic point process, the average performance of all links in each realization corresponds to that of the typical link (in the sense of the ensemble average). In the earlier days of stochastic geometry applications to wireless networks this key equivalence was not well understood – in particular by reviewers. Frequently they pointed out that the PPP model (or any point process model for that matter) is only relevant for networks with very high mobility, believing that only high mobility would justifiy the ensemble averaging. Luckily this is much less of an issue nowadays.
So far we have discussed Rayleigh fading and the PPP separately. The true strength of these simple models becomes apparent when they are combined into a wireless network model. In fact, most of the elegant closed-form stochastic geometry results for wireless networks are based on (or restricted to) this combination. There are several reason for this symbiotic relationship between the two models, which we will explore in a later post.