This project involves two subjects, namely, mean-field theory and optimal transport, with engineering application.

The mean-field theory is a powerful tool to efficiently approximate the behavior of a complex system that includes several agents. In this approximation, the mean-field, i.e., the average effect of all agents, replaces the individual agents' interactions and serves as the basis of analysis. The mean-field theory finds applications in several domains, including network economics and optimization.

The theory of optimal transport has deep connections with several research fields, e.g., efficient resource allocation in wireless communications and domain adaptation in machine learning. Moreover, it stands as a powerful tool to study flows and to analyze energy functionals on the space of probability measures.

The main objective of this project is to optimize the performance of ultra-dense and resource-constrained networks in different settings, using the mean-field theory and optimal transport theory. The target settings include networks that involve heterogeneous agents or agents with constrained interactions.

This project is in cooperation with Dr. Marc Sedjro at AIMS South Africa, also Prof. Giuseppe Caire and Dr. Peter Jung at the Technical University of Berlin.  The project receives financial support from the German Academic Exchange Service (DAAD) and German Ministry of Education and Research (BMBF). The duration is 2019-2023.