I describe here a couple of the main research themes that my work has covered, but this is of course not a complete list.
Wireless signals appear Poisson
Under the standard statistical propagation model, researchers have mathematically shown that wireless networks can appear (in terms of received signal powers) to any single observer as an inhomogeneous Poisson point process on the real line, provided there are sufficiently random propagation effects, such as multi-path fading or shadowing. In other words, the signal powers form a point process on the positive real line, which is statistically close to a Poisson point process. This has been proven for a quite general propagation model with independent propagation effects and, more recently, correlated shadowing. These results imply, through the Poisson mapping theorem, that network transmitter layouts that do not appear Poisson, such as lattices or other configurations, can be modelled as realizations of Poisson processes, as a Poisson network with matching intensity measure would produce the same inhomogeneous Poisson point process on the positive real line.
The source of randomness that makes a non-Poisson network appear (or behave stochastically) more Poisson is the random propagation effects of fading, shadowing, randomly varying antenna gains, and so on, or some combination of these. These Poisson results were originally derived for independent log-normal shadowing, but then they were greatly extended so they still hold true under a propagation model with a general path loss function and (sufficiently large and independent) propagation effects, such as Rayleigh or Nakagami fading, and this behavior is more likely for the stronger signals. In the case of log-normal shadowing and a power-law path-loss function, this Poisson behaviour still holds true if there is correlation between the random propagation effects. More specifically, signals can still appear Poisson when the propagation effects are modelled with a correlated Gaussian field.
For mobile (or cellular) phone networks and other wireless networks, a very popular stochastic geometry model consists of base stations (that is, transmitters) as a Poisson process, a simple power law as the path loss function, and iid random variables as the random propagation effects such as fading and shadowing. Borrowing an expression from physics, this Poisson model could be called the “standard model” due to its popular use. Researchers have used it to derive closed-form expressions, sometimes with surprisingly simple forms, for the probability distributions of the signal-to-interference ratio (SIR) in the downlink, which gives the probability of a user being covered in the network. Under various models, these coverage probability expressions have been derived, where the model assumptions depend on considerations such as whether a user connects to the closest base station or the base station with the strongest signal.
Under the standard model, an expression was derived for the probability that a user can connect to k base stations in a single-tier Poisson network. Using point process techniques, these k-coverage results were extended to the case of multi-tier Poisson network models, which are often used to model heterogeneous networks. The results can also be used to calculate the coverage probabilities under certain signal management schemes, such as successive interference cancellation, so generalizing previous results based on point process theory.
The main idea behind the k-coverage results is using the Schuette-Nesbitt formula and recognizing that the symmetric sums are a simple function of the factorial moment measures of the SINR process.
The STIR process is a Poisson-Dirichlet process
Some of the aforementioned work in the engineering literature, namely that of deriving probability expressions for SIR-based coverage, had already been discovered years earlier in the field of probability, which is pointed out in this short article. Under the Poisson network model with (singular) power law path loss function, the SINR process of a single user is a simple transform of the STIR process, which is an example of a Poisson-Dirichlet process.
The two-parameter Poisson-Dirichlet process, also called the Pitman-Yor process, gives the STIR process when one of the parameters is set to zero. In other words, the STIR process is a special case. Note there are two different point processes both called the Poisson-Dirichlet process. The much more well-known one was introduced by Kingman, which is not the STIR process, and covered in Chapter 9 of his great monograph Poisson Processes. But these two point processes are the two special cases of the two-parameter Poisson-Dirichlet/Pitman-Yor process.
The above special case of the Poisson-Dirichlet (that is, the STIR) point process is also the same point process used in Ruelle’s energy model, which is studied in relation to the Sherrington-Kirkpatrick spin glass model; see Chapter 2 of Panchenko (Springer Verlag, 2013) or Chapter 4 of Contucci and Giardiná (Cambridge University Press, 2013).
I originally studied physics and electronic engineering before changing over to applied mathematics and completing an honours thesis in fluid mechanics. I completed my PhD in applied mathematics at the University of Melbourne under the supervision of Peter G. Taylor whose detailed websites can be found here and here. I then spent two years, partly being funded by Orange Labs, in Paris as a post-doc in the Inria (formerly INRIA) group DYOGENE * headed by Marc Lelarge (the group was known as TREC, headed by François Baccelli, during my first few months there). Then I became a member of the Weierstrass Institute (or WIAS), Berlin, in a group headed by Wolfgang König.
* DYOGENE is actually a reference to ancient Greek philosopher Diogenes of Sinope (who famously lived in a giant jar) as his name in French is pronounced the same as DYOGENE, all of which had to be explained to me.