The sixth generation (6G) of mobile networks promises transformative capabilities in terms of, amount others, higher data rates, lower latency and ubiquitous coverage, but achieving these goals sustainably poses significant challenges. A promising solution lies in Cell-Free massive Multiple-Input Multiple-Output (CF-mMIMO) networks, where a dense deployment of geographically distributed Access Points (APs), coordinated by one or more Central Processing Units (CPUs), cooperatively serve User Equipments (UEs). While CF-mMIMO networks offer improved spectral efficiency and more spatially uniform service, their dense deployments can result in high energy consumption. As MNOs typically design their networks such that Quality of Service (QoS) targets are met during peak hours, the network is left significantly over-dimensioned during off-peak hours. This thesis addresses this inefficiency by proposing a low-complexity, heuristic Sleep Mode Management (SMM) algorithm that reduces energy usage by switching off unneeded APs while maintaining acceptable QoS and without compromising coverage.

The proposed SMM algorithm supports multiple AP power states: active, light sleep and deep sleep. It incorporates realistic transition times between these states. Relying solely on practically available information such as long-term Channel State Information (CSI) and previously achieved data rates, the algorithm dynamically decides which APs can be temporarily put into a sleep mode. Importantly, it ensures population coverage is maintained and it preserves UE QoS based on a 10th UE throughput percentile target.

The proposed SMM algorithm is evaluated using a system-level simulator that models a realistic scenario based on the city center of Amsterdam, including lamppost-based AP deployments and a realistic basis for the spatial traffic distribution and daily traffic fluctuations. Simulation results demonstrate that the proposed SMM algorithm reduces the daily energy consumption of a CF-mMIMO network by up to 17.11\% with the best overall configuration. If the parameters of the SMM algorithm are allowed to be adaptively tuned to the traffic load, the daily energy consumption can be reduced by up to 21.54%. This thesis not only contributes a novel SMM algorithm but also provides guidance for AP deployment strategies in 6G CF-mMIMO networks, determining that a higher number of lower-antenna-count APs can yield better QoS than fewer, more higher-antenna-count APs at the cost of energy efficiency.

This thesis explores Radio Resource Management (RRM) techniques for Joint Communication and Sensing (JCAS) in beyond-5G/6G networks. JCAS integrates communication and sensing functionalities into a unified network, promising enhanced spectral efficiency, reduced system costs, and improved performance in various applications such as autonomous vehicles and smart cities. However, efficiently managing the dual requirements of communication and sensing in a shared network is a significant challenge, especially in dense, multi-cell environments.
The work presents a novel approach to JCAS by developing advanced resource management algorithms aimed at optimizing communication throughput and sensing accuracy, with a focus on target detection as the primary sensing task. The proposed algorithms encompass topology selection, dynamic node contribution management, and joint scheduling of sensing and communication tasks. Through extensive simulations, we analyze the trade-offs between communication and sensing performance, considering metrics such as the average and 10th percentile user throughput and probability of detection. The results demonstrate the effectiveness of the proposed strategies in managing interference and improving system performance in a cooperative multi-cell JCAS network.
This study contributes to the growing body of research on JCAS by addressing key limitations in existing works, such as the lack of cooperative sensing and the need for real-time dynamic resource allocation algorithms. The findings provide practical insights for network operators and lay the groundwork for future research into more complex JCAS applications and further optimization of resource management techniques.

In this thesis, we design and assess a multi-slice resource allocation framework that is based on machine learning techniques (subset of artificial intelligence techniques). The proposed framework employs two machine learning techniques namely, artificial neural networks and reinforcement learning for resource management in sliced RAN. Alternative multi-slice resource allocation methods that involve only artificial neural networks but not reinforcement learning are also defined.

Narrowband Internet-of-Things (NB-IoT), recently introduced by 3GPP, is a relevant Radio Access Technology (RAT) solution for deployment within smart grids, the electricity grids of the future, due to the need to provide low-cost connectivity to a large number of smart meters installed in households. Outage Restoration and Management (ORM), a smart grid use case, involves the smart meter User Equipment (UE) sending a notification message to the utility upon the detection of a loss or restoration of power. ORM is an effective way for utilities to quickly detect, localise and restore a power outage. However, depending on the extent of the power outage, a near simultaneous network access by multiple UEs may occur, leading to resource congestion, particularly of the so-called ‘random access channel’. This may impact the reliability, i.e. the percentage of total notifications successfully delivered within a certain transfer delay target, and in turn, the accuracy of the power outage localisation. Consequently, the maximisation of the ORM reliability performance for a technology like NB-IoT becomes a challenge, given that such use cases, though relevant, were not considered in its design phase.
The main goal of this thesis is the optimisation of the NB-IoT network configuration, with a focus on packet scheduling, in order to maximise the ORM reliability performance. To this extent, a system-level simulation model is developed and implemented, incorporating realistic characteristics of energy distribution and mobile networks in four different environments (rural, suburban, urban and dense urban), the traffic characteristics of ORM and the relevant 3GPP specifications of NB-IoT. Additionally, a set of candidate time-frequency domain packet schedulers are proposed. A sensitivity analysis of key network configuration components is performed for a set of power outage scenarios i.e. network loads, the associated optimisation trade-offs are highlighted and a near-optimal network configuration is derived.
Based on the sensitivity analysis, a proposed scheduler which prioritises UEs based on a combination of the Earliest Due Date First (EDDF) and Shortest Processing Time First (SPTF) principles, and assigns each UE a single uplink subcarrier with a subcarrier spacing of 3.75 kHz, performs best amongst all the candidate schedulers. Furthermore, the achieved reliability performance is close to 100% for all the considered power outage scenarios in the rural and dense urban environments. In the suburban and urban environments, close to 100% reliability is achieved for the majority of the power outage scenarios.