Benedikt Jahnel acquires Junior Group in Leibniz competition

Leibniz Association funds research project on Probabilistic Methods for Dynamic Communication Networks over a period of 5 years (2021-2025).

In our increasingly connected world, featuring the Internet of Things, self-driving cars and interacting smart devices, an overwhelming amount of data must be rapidly transmitted through highly complex networks. This requires a revolution in many aspects of the network architecture. Already with the rollout of the new 5G communication standard, faster connections, higher throughput, and more capacity are envisioned via an enhanced mobile broadband. Additionally, ultra-reliable low-latency communications should support time-critical applications such as car-to-car communication, and massive machine-type systems will hold the key to a successful transition towards industry 4.0.

In light of these ever-growing opportunities, but also demands, of modern communication systems, device-to-device (D2D) communication is becoming a key technology pervading a highly diverse set of use cases. While the envisioned benefits are manifold, ranging from coverage extensions in emerging markets to network robustness and green networking, the systems however become less controllable.

Probabilistic methods have proven helpful in the analysis as well as in the design of D2D systems, with stochastic geometry offering the ideal mathematical framework to model the intrinsic network uncertainties, and to construct novel powerful stochastic algorithms.

Here, at the interface of rigorous applied mathematics and industry-friendly data-driven network engineering, the junior research group supports this revolution through innovative research. The aim is to

• study connectivity improvements in mobile urban D2D augmented networks with the help of dynamic continuum percolation theory,
• investigate data routing in D2D systems with a focus on bottleneck behavior and thereby advance large-deviations theory for space-time point processes, and
• analyze malware propagation in dynamic D2D networks by extending the theory of interacting particle systems to random graphs in the continuum.