5G networks brought significant performance improvements for different service types like augmented reality, virtual reality, online gaming, live video streaming, robotic surgeries, etc., by providing higher throughput, lower latency, higher reliability as well as the possibility to successfully serve a large number of users. However, these improvements do not come without any costs. The main consequence of satisfying the stringent traffic requirements of the aforementioned applications is excessive energy consumption.

Therefore, making the cellular networks sustainable, i.e., constraining their power consumption, is of utmost importance in the next generation of cellular networks, i.e., 6G. This goal is of interest mostly to cellular network operators. Of course, while achieving network sustainability, the satisfaction of all traffic requirements, which is of interest to cellular users, must be ensured at all times. While these are opposing goals, a certain balance has to be achieved.

In this thesis, the focus is on the type of services known as eMBB (enhanced mobile broadband). These are services that are characterized as latency-tolerant to a certain extent, but sensitive to the throughput and its stability. Live video streaming is a use case falling into this category. For these applications, on the one side, higher data rates imply higher energy consumption. On the other side, the users can be satisfied with slightly lower throughput as long as the provided data rate is constant, which corresponds to the flexibility that the network operator can exploit. Hence, the question that needs to be answered in this thesis is what is the optimal trade-off between the data rate and the energy consumption in a cellular network with eMBB users? To answer this question, the entire communication process will be encompassed, i.e., from the transmitting user through the base station and core network to the receiving end. The student will need to formulate an optimization problem to address the related problem, which they will then solve through exact optimization solvers, but also through proposing simpler algorithms (heuristics) that reduce the solution time while not considerably deteriorating the system performance.

Prerequisites