Semantic Driven Optimization for Mission Critical Communications

In mission critical communications (MCC), different performance metrics which are beyond to the classical communication models need to be considered in order to manage the reliability, timeliness constraints and communication bottlenecks and thus obtain the desirable objectives. These include the value of information (VoI), the age of information (AoI) and the semantics of information (SoI).
The goal of this research is to introduce novel schemes in the access network that can be used to optimize the SoI. Specifically, the project will address and explore the access network scheduling in these new performance dimensions, and in the context of MCC: (i) analytical framework will be developed to model the SoI, (ii) access network protocols will be designed with the goal to optimize the SoI, e.g., to prioritize and give preference to those uplink packets having the high meaningful data as compared to the ones having the less meaningful data, and (iii) the use of machine learning in the context of the SoI will also be explored to optimize the performance of the access network scheduling.