Resource management in wireless networks

Abstract

Following the trend of previous years, the number of devices, and hence the traffic in cellular networks is increasing. Moreover, new applications with stringent requirements are envisioned. Examples of such applications include collaborative learning and coverage extension with drones. To accommodate the traffic with its respective Quality of Service (QoS) requirements and to support new challenging applications in the Radio Access Network (RAN), we need to develop new algorithms and tools for efficient resource management. In this dissertation, resource management in the RAN is considered in three distinct areas.
In Chapter 2 we provide an introduction to the key concepts, which establish the technological context of the following chapters. The first part of this dissertation focuses on serving traffic with diverse requirements in the context of 5G networks. In 5G, RAN slicing has been introduced, to support services with diverse QoS requirements in the same network infrastructure. Moreover, RAN slicing allows the Mobile Network Operators (MNOs) to configure customer-specific slices. In Chapter 3, we assess RAN slicing in terms of the traffic handling capacity for an Industry 4.0-inspired scenario. For the assessment, we compare a network with isolated slices and a non-sliced network. Extensive simulations show that the non-sliced network can serve more traffic than the sliced network while satisfying the same class-specific QoS requirements. Considering that RAN slicing will be adopted by the MNOs, this result highlights that additional radio resource management mechanisms are needed when RAN slicing is configured. To that end, in Chapter 4 we evaluate RAN slicing in combination with allowing slices to use idle resources of other slices, in a realistic smart city environment. The results show that idle resource sharing significantly improves the traffic performance. However, it is not until RAN slicing is further combined with other technology features, i.e. flexible numerology and mini-slots that it provides better traffic performance than non-sliced networks.
The second part of this dissertation focuses on the application of collaborative learning, and more specifically on Federated Learning (FL) in resource-constrained wireless networks. In Chapter 5, we characterise agents by their importance in the learning process and the resource efficiency of their wireless channel. Then, we provide a general agent selection framework to indicate which agents should participate in the learning process. Extensive simulations in various scenarios verify the potential of the proposed framework. Additionally, it is revealed that in scenarios where agents have small data sets or the latency requirement is stringent, it is more beneficial to perform pure learning-based agent selection. In Chapter 6 we extend the previously proposed framework to perform joint agent selection and resource allocation. We describe the problem in resource-constrained vehicular wireless networks with Multi-User Multiple Input Multiple Output (MU-MIMO) capable base stations. To approximate the optimal solution of the problem, we propose the Vehicle-Beam-Iterative (VBI) algorithm. Then, we evaluate the VBI algorithm in scenarios related to vehicular communications. The results show that in scenarios where the vehicles have the same data set sizes, the application-specific accuracy targets are achieved faster than in scenarios where the data set sizes are different. Additionally, it is shown that MU-MIMO improves the convergence time of the global FL model.
In the third part of this dissertation, the deployment of a drone swarm is addressed. In Chapter 7 we study the link density is Random Geometric Graphs (RGGs). Specifically, we very accurately approximate the link density in any two- and three-dimensional rectangular spaces with the Fréchet distribution. Then, we express the minimum number of nodes needed to ensure network connectivity in terms of the link density. Finally, we model a drone swarm with a RGG and we estimate the required size of the swarm such that communication among all drones can be ensured.
The conclusions of this dissertation and the directions for future work are presented in Chapter 8.