Institute of Telecommunications
E389 Seminarraum 2 (CG0402)
Talk by Marco Di Renzo:
“Stochastic Geometry Modeling and Optimization of Cellular Networks – Bridging Accuracy and Complexity”
In the past few years, there have been many efforts to develop analytical methodologies for optimizing very ultra-dense networks, especially by using the mathematical tools of stochastic geometry and point processes. At the time of writing, however, we have understood that many proposed approaches have (at least one of the) two main limitations that make them unsuitable for optimizing cellular networks:
– Limitation 1: Due to the analytical complexity of the problem at hand, key system approximations need to be applied, which make the resulting analytical frameworks unsuitable for system optimization (relevant design parameters are not taken into account).
– Limitation 2: Realistic network models result in analytical frameworks that are too complex to gain any insights on the fundamental properties of the networks and to perform large-scale optimization (the objective functions are non-convex and have multiple integrals).
In this talk, I will describe two recent approaches that I have recently proposed to overcome the two limitations mentioned above:
M. Di Renzo et al., “System-Level Modeling and Optimization of the Energy Efficiency in Cellular Networks – A Stochastic Geometry
Framework”, IEEE Transactions on Wireless Communications, Vol. 17, No. 4, pp. 2539-2556, April 2018.
M. Di Renzo et al., “Inhomogeneous Double Thinning – Modeling and Analysis of Cellular Networks by Using Inhomogeneous Poisson Point
Processes”, IEEE Transactions on Wireless Communications, Vol. 17, No. 8, pp. 5162-5182, August 2018.
In the first paper, I have introduced a new analytical formulation of the coverage probability that I proved to be accurate and suitable for system-level optimization. In the second paper, I have introduced a new approach based on the theory of inhomogeneous Poisson point processes for modeling and analyzing communication networks with spatial correlations (either attractive or repulsive).
More Information about Marco DI RENZO