The energy consumption of mobile networks, particularly the 5G Radio Access Network (RAN), is becoming a growing concern due to its environmental and economic implications. As the demand for higher data rates and low-latency services intensifies, 5G networks, integrating macro cells and small cells, are emerging as critical infrastructures. Although small cells improve coverage and capacity, their increased deployment could lead to a significant rise in the overall power consumption of 5G systems.
Current small cell selection strategies by User Equipment (UE), although effective in some cases, do not fully account for the dynamic nature of traffic conditions and the specific data requirements of users. Moreover, current techniques such as the maximum Signal-to-Interference-plus-Noise Ratio (max-SINR) and Cell Range Expansion (CRE) purely consider the signal strength of the link between the user and the base station to allocate users to the base station. However, this leads to inefficient utilization of base station resources and uneven distribution of load, causing congestion at some base stations while leaving others underutilized.
In order to address these gaps, this thesis proposes a Traffic Distribution Orchestrator (TDO) to manage the distribution of users between cells dynamically, and optimize energy efficiency without compromising network performance. The proposed cell selection model developed in this thesis also accounts for user mobility and dynamic traffic conditions. The model estimates instantaneous power consumption and informs a real-time algorithm user equipment-base station (UE-BS) association algorithm to dynamically allocate users to the cell which will enhance the energy efficiency of the network while ensuring the required Quality of Service (QoS) requirements. Complementing this, an adaptive sleep mode mechanism puts underutilized small cells in a low power mode and reactivates them when demand rises, using hysteresis to prevent state flapping and reduce idle power.
Through MATLAB simulations, the effectiveness of the model and algorithm is validated, with results indicating a significant reduction in network power consumption in heterogeneous 5G deployments. The proposed UE-BS association algorithm is compared with the max-SINR, CRE and a representative association method from the previous studies, whereas the proposed adaptive sleep mode mechanism is compared with fixed threshold sleep mode mechanism under both bursty and steady traffic. The proposed UE-BS association algorithm combined with the adaptive sleep mode mechanism reduces total network power consumption relative to baseline strategies. This research contributes to the advancement of sustainable 5G network architectures and offers insights into energy efficiency optimization in real-world scenarios.

This thesis investigates methods to enhance the robustness of KPN's network. As reliance on telecom networks grows, improving network resilience is paramount. Two primary strategies exist: minimizing failure occurrence and mitigating failure impact. This research focuses on the latter, specifically increasing network redundancy by transforming existing ring structures into strand structures.

Current ring structures in the network avoid single points of failure (SPOFs) at the link level but fail to address node-level SPOFs, particularly at Metro Core (MC) locations. MC locations are pivotal, as they manage customer sessions and route all household network traffic. Node failures at these locations can disrupt connections for up to 100,000 households. Thus, ensuring that a single node-level failure can be compensated by another node is crucial. This requires considering both equipment and building failures, with geographically distant backup nodes necessary for the latter.

Transforming rings into strands, which inherently include two geographically separated MC locations, addresses these issues but incurs significant costs. While an exact cost estimation is beyond the scope of this research, the study approximates the length of additional cables required, totaling 206 kilometers. Including cost multipliers for tunnels, bridges, and highways, the adjusted length is equivalent to 222 'kilometers' of cable. Despite the substantial cost, node failures at the equipment level, which are more common, can be mitigated with redundant equipment at MC locations. Therefore, a cost-effective alternative might be forming strands only where rings from different access areas are already in proximity.

The demand for computational resources is increasing exponentially due to an increasing amount of digital services. Cloud computing is becoming the standard for enterprises to provide these resources. This resulted in hyperscalers which consist of a large number of servers. Data centers consume more than 1% of the world’s electrical energy. Therefore, many techniques are developed that both help to fulfil the needs of digital services and reduce the energy consumption of data center services. Modern-day servers can switch between different operating states which are often integrated in a power configuration mode of servers known as eco-mode. One of these techniques throttles the clock frequency of a central processing unit (CPU) which enables the possibility to lower the power needed for that CPU. This technique is known as dynamic frequency and voltage scaling (DFVS) and the states it switches between are known as performance states (P-states). A different technique that is integrated with eco-mode is the ability to switch between different idle states of the CPU. These states define whether certain caches of the CPU are flushed or not to conserve energy. These states are known as core states (C-states). A different approach that is focused on conserving energy is virtualisation within data centers. Virtualisation enables one physical server to host multiple virtual instances of servers (virtual machines). This reduces resource wastage and energy consumption of a data center. However, this creates the need for a strategic placement that ensures that the demands of the virtual machines are met and that minimises energy consumption and resource wastage. This thesis analyses four of these techniques: the best fit decreasing (BFD) algorithm, the integer linear programming (ILP) algorithm, the particle swarm optimisation (PSO) algorithm and the genetic algorithm (GA). This thesis provides a framework that uses a holistic approach to provide insights into the effects of using eco-mode of servers within the dynamics of virtual machine placement in data centers. This framework serves as a first step in parameterising the dynamics of a data center regarding its energy consumption and performance. The results show a potential energy reduction of up to approximately 20% with negligible impact on a data center’s performance. This result occurs when applying the best fit decreasing algorithm and having a server with an energy-efficient eco-mode. However, this thesis does not cover all parameters that play a role in the data center’s performance and energy consumption, so more research on this area is recommended.

As digital communication networks become integral to society and the economy—driving everything from personal communication to business operations and smart city infrastructure—the capacity of these networks must continuously expand to accommodate higher data volumes. The Ericsson Mobility Report demonstrates the growing need for higher throughput and increased network capacity to meet end-user expectations. One of the viable approaches to increase network capacity is through the deployment of small cells.
In general, the related axioma is: the higher the offered bandwidth (higher radio frequencies), the shorter the radio ranges will be. Small cells provide additional capacity (preferably) by means of high-frequency bands such as 3.5 GHz and 26 GHz offloading the macro network in particular short-range hotspots. Small cells can deliver higher data rates to end users, making their deployment essential at hotspots in densely populated urban areas where additional capacity will be required. This master’s thesis evaluates Radio Access Network (RAN) architecture options for combining macro cells and small cells from both theoretical and practical perspectives.

The main finding from the Radio Access Network-related theoretical analysis is that the RAN architecture split option 7.2x recommended by the O-RAN Alliance is the optimal solution for indoor and outdoor deployment of small cells. Split option 7.2x offers significant benefits, such as minimising the impact on transport bandwidth while enhancing the virtualisation capabilities of the gNB Central Unit (CU) and Distributed Unit (DU), and enabling a cost-effective design of the Radio Unit (RU).


To gain an understanding of the practicalities involved in small cell deployment, this master thesis, through the Utrecht practical case study, examines potential locations for the installation of RUs, DUs, and CUs using a combination of expert interviews, Google Street View analysis, QGIS visualisations, and site visits. Proposed locations for RUs include three tall lamp posts and two security camera poles, while wharf cellars managed by Stedin and a macro cell base station are recommended for the installation of CUs and DUs. These recommendations are based on an analysis of power availability, transport, site accessibility and expert interviews. From the research it is concluded that small cell deployment in the Utrecht researched area is feasible, provided that specific challenging boundary conditions are met such as collaboration among MNOs and the use of existing poles.

The practical part of this thesis research also delves into the complex interplay of eight socioeconomic sectors and their roles in small cell deployment, providing insights into the trans-sector nature of this project.
The main results from this practical part of the research concern the conclusions from the Utrecht practical case study about realizing small cells:
The main results from this practical part of the research concern the conclusions from the Utrecht practical case study about realizing small cells:
1. The rollout of small cells is a complex multi-actor value case with substantially more actors to collaborate in comparison with rolling out macro cells by primarily telecom operators and municipalities (issuing licenses).
2. Municipalities are best suited to fulfil the role of orchestrator in the rollout of small cells because they can coordinate diverse stakeholders to ensure seamless integration with existing infrastructure, maintain city aesthetics and establish a direct line of communication with residents for feedback and service improvement.
Based on some representative calculations of small cell deployments in the city of Utrecht we obtained good insight into the complexity as well as cost of small cells. A few of the quantitative results are mentioned below:
•The price of a small cell is roughly around €60,000 per small cell site, including digging for transmission and power costs.
•In case the fronthaul is replaced by a wireless connection, roughly 30% of cost savings can be achieved.

A synchromodal transport network is a transportation technique which aims to create more efficient and sustainable transportation plans, which utilizes different modes of transport, i.e., roadways, railways, and waterways synchronously. Synchromodal transport network is a complex network with interdependence. The aim of this thesis was to analyze the robustness and identify critical infrastructure in synchromodal transport network. In order to achieve the goal of the thesis. First, the synchromodal transport network is analyzed on the basis of its transportation characteristics and network topology. The analysis of transportation characteristics helped us to define a notion of node criticality. The node criticality quantifies the effect on the robustness of a transportation system due to the perturbation of an infrastructure. Then, a relationship is established between the node criticality and topological centrality metrics in order to quickly identify critical infrastructures in the synchromodal transport network. Lastly, a systematic framework is proposed and applied to the Dutch synchromodal transport network. The results from the case study are quite insightful. First, we observe that the distribution of node criticality exhibits a power-law distribution which implies that the Dutch synchromodal transport network tends to robustness against node perturbation. Second, we observe that the performance of the dutch synchromodal transport network is quite sensitive to the perturbation in the road network. Lastly, we identify that the weighted degree centrality, eigenvector centrality shows a high correlation with node criticality thus these centrality metrics can be used to identify critical infrastructure in the synchromodal transport network.