To satisfy the ever-increasing demand for high data rates, a promising solution is to converge existing networks, also technologies and services, to ensure continuous quality of service (QoS) guarantee while maintaining efficiency in resource usage.

Despite the great potential for improving the users’ satisfaction level, network convergence is challenging, in particular with regards to control, management and planning. That includes, but is not limited to, mode- and network selection, scheduling, resource allocation, interference management, and the like. Although such challenges appear in the control and planning of almost every networking paradigm, the concept of network convergence aggravates some difficulties. In particular, each included network might have its PHY/MAC protocols, as well as a time scale. Moreover, each network has its interests due to a difference in available radio resources, subscribed users, contents (files) in high demand, and the like.

In this project, we focus on self-organization for the mobile machine to machine (M2M) communications underlying a converged (broadcast-wireless-cellular) infrastructure. The problems to address include resource allocation and resource sharing, also transmission mode selection, and hand-off, taking into account the mobility and lack of information. To solve the formulated problems, we model each user as an intelligent agent and develop decision-making policies that guarantee some optimality conditions in terms of network key performance indicators and convergence. To this end, we leverage machine learning concepts.  

This project receives financial support from the Einstein Center for Digital Future (ECDF) in cooperation with Deutsche Telekom AG. The duration is 05.2018-04.2022. The responsible researcher for this project is Mr. Saeed Ghoorchian.