In the aftermath of severe flooding, earthquakes, or warfare, the failure of ground-based cellular infrastructure often leaves affected populations and first responders without vital communications connectivity. While Unmanned Aerial Vehicles (UAVs) and 5G NR Device-to-Device (D2D) relaying offer a promising solution for rapid network restoration, a significant gap exists between terrestrial relaying standards and a functional, deployable aerial network. This thesis bridges this gap by designing a 3GPP-aligned operational model for UAV-based relaying.

By adapting mature 5G terrestrial relaying standards to the unique constraints of an aerial network, the proposed operational model introduces specific architecture enhancements to directly address the critical needs of these network-denied environments. For localised use cases through UE-to-UE (U2U) architectures, the operational model helps stranded individuals contact emergency services and supports local coordination. Additionally, for core-connected use cases through UE-to-Network (U2N) architectures, the operational model restores public network access, bridging the isolated disaster zone back to the core network.

The proposed operational model prescribes specific protocol procedures for synchronisation, discovery and connection establishment tailored for aerial networks. To overcome the high collision rates caused by standard decentralised Mode 2 protocols in high-density environments, the operational model introduces a critical architectural enhancement: UAV-governed scheduling, which leverages centralised coordination to ensure reliable out-of-coverage connectivity. Furthermore, the operational model incorporates the specific signalling exchanges required to retrieve real-time radio environment data, enabling the UAV to dynamically optimise its 3D position to maximise network throughput.

The effectiveness of the proposed operational model is systematically evaluated through system-level simulations. The simulations reveal that while standard Mode 2 resource allocation suffers from severe half-duplex and physical collisions under high network loads, the proposed UAV-governed scheduling eliminates steady-state collisions entirely. This coordination guarantees near-perfect packet delivery ratios and cuts end-to-end latency by more than half compared to the uncoordinated baseline. Ultimately, the results demonstrate that UAV-based relaying provides a highly effective 3GPP-aligned solution for rapid network restoration in an emergency scenario.

https://doi.org/10.4121/625a1cd3-63b8-40eb-895d-a20bdbdc0416 Repository link ... Read more

The increasing demand for real-time applications such as cloud gaming, augmented/virtual reality (AR/VR), remote control, and industrial automation, has placed stringent requirements on mobile networks to deliver ultra-low latency and high reliability. As 5G networks evolve, ensuring consistently low delays, even during congestion periods, is critical for these real-time applications.

This thesis investigates two network-assisted rate adaptation mechanisms: Low Latency Low Loss Scalable Throughput (L4S) and Access Network Bitrate Recommendation (ANBR). Both mechanisms aim to reduce latency and packet loss while maximizing throughput during periods of congestion. L4S, standardized by 3GPP and IETF, uses Explicit Congestion Notification (ECN) marking in the IP header of the packets, where the base station marks packets to signal early signs of congestion. This allows the sender to react promptly and adjust its transmission rate using a scalable congestion control algorithm. ANBR, also standardized by 3GPP, takes a different approach by providing rate recommendations from the base station to the user equipment (UE) using MAC layer messages.

While both technologies share similar goals, L4S has seen significant industry interest in recent times, whereas ANBR remains relatively underexplored. Despite their potential and similarities, the coexistence of these two technologies and suitability for different scenarios have not been thoroughly investigated.

This research done in collaboration with KPN, addresses this gap by evaluating the comparative performance, suitability, and coexistence of L4S and ANBR for different network scenarios. The research combines theoretical and practical analysis. The units of research include literature and standards reviews, simulations using ns-3, and practical experiments conducted at KPN's test lab. Latency, packet loss, and throughput are analyzed for each experiment.

The findings provide insights into the advantages and disadvantages of L4S and ANBR, and highlight the applications for which they are most suitable. Based on the findings, recommendations are proposed to guide the effective adoption and integration of L4S and/or ANBR in KPN.
A key finding from the research is that L4S is better suited for applications requiring ultra-low latency, while ANBR is more appropriate for applications with higher throughput sensitivity. With L4S, telecom operators can have better control over latency and define queueing thresholds at which rate adaptation should begin for the applications, enabling them to better ensure that the Quality of Service (QoS) requirements of each application are met. In contrast, ANBR does not directly target queueing delay; instead, it uses a window mechanism to send rate recommendations to the UE, which limits its ability to control latency. ... Read more

Accurate capacity planning is essential to ensure uninterrupted services and network stability through peak hours for the transport core network of KPN. This involves a trade-off between minimizing the risks of capacity shortages and costs of capacity expansions. High network loads are occurring more frequently and their magnitude is increasing. This necessitates measures to foresee high load situations before network capacity is surpassed. Currently, planning is based on manual predictions that lack substantiation. This research aims to improve network capacity planning by development of a forecast for the next year.
An analysis of the daily maximum traffic data of the transport core is performed, to determine the most suitable models for the prediction of network traffic. The data analysis, employing time series decomposition, revealed non-stationary trends and annual seasonality; traffic decreases throughout the summer and increases in the winter. An upward trend in the frequency and intensity of traffic peaks, highlights the growing demand and shifts in usage behavior. The extreme traffic peaks in the historical data were correlated to F1 race days and other anticipated events.
Two algorithms that integrate exogenous variables were assessed to predict the extreme values. The models either yielded inaccurate traffic predictions or encountered challenges in interpretability and pattern recognition, with the limited amount of data available. In response to these limitations, a decomposed forecast was created that predicts the trend and seasonality. Furthermore, Extreme Value Analysis (EVA) was implemented to address the extreme values in the data.
The final prediction framework combines the decomposed forecast with EVA for the next six quarters and outperforms the other models. The model effectively captures extreme values and provides insights into the maximum expected peaks and risk levels. The substantiated forecasts of the EVA model and the manual predictions yielded comparable results. However, the EVA model provides better insights into the likelihood of exceeding specific traffic values, which enhances capacity calculations and precision.
The prediction framework has been integrated into the business interface of KPN, which marks the initial step in the automatization of short-term capacity planning. The research insights emphasize the intricate nature of accurate prediction of future demand and advocate for scalable solutions beyond building new capacity. These solutions range from short-term mitigation to long-term strategies designed to alleviate high network loads. They underscore the importance of the implementation and integration of
dynamic decision-making within a digital twin of the network to ensure sustained effectiveness. ... Read more

The exponential growth in mobile network traffic, driven by the rapid deployment of 5G technologies and the proliferation of new services, presents significant challenges for telecommunication operators. This thesis addresses these challenges by developing a predictive capacity management solution for 4G and 5G cellular networks. The primary objective is to forecast network traffic and identify potential congestion points up to one year in advance, enabling proactive network management and optimizing resource allocation, particularly through the use of spectral efficiency as a key predictive measure.

This study utilizes data from KPN’s Operations Support System (OSS), comprising 67 days of hourly data across the entire network, with a focus on predicting future traffic and network performance up to one year ahead. The methodology integrates historical data analysis, time series forecasting, and machine learning techniques. The approach combines Cumulative Distribution Function (CDF) modeling for traffic volume prediction with supervised machine learning algorithms, including Linear Regression, Lasso Regression, Random Forest, and CatBoost, to forecast Physical Resource Block (PRB) utilization and spectral efficiency at the sector level.

The detailed analysis identifies Lasso Regression as the most effective model for predicting spectral efficiency, with the lowest Mean Absolute Percentage Error (MAPE). Lasso’s ability to handle extrapolation beyond observed data ranges makes it particularly well-suited for long-term capacity management when combined with CDF-based traffic prediction. The findings demonstrate significant improvements in the accuracy of congestion predictions and the efficiency of resource utilization.

The study also revealed that, without additional resources, the number of congested sectors is expected to increase as traffic demand continues to grow. This highlights the critical need for new spectrum allocation to maintain service quality. Additionally, the research evaluated the impact of deploying new spectrum resources, such as the 3.5 GHz band, in specific sectors. The results showed that the deployment of the 3.5 GHz band significantly reduced congestion and improved network performance and user experience during the forecast period. ... Read more

This thesis’ research concerns the time-dynamics of a complex geographical network of municipalities, i.e. the Dutch Municipality Network over the period 1830-2019. By analysing 190 years of socioeconomic statistical data and applying contemporary tooling from network science and geographic information systems (GIS), the findings from this research can provide a new approach and supportive methods for policymakers, statistical offices, researchers and businesses (to decide when and where to invest). ... Read more

Multi-access Edge Computing (MEC) is a concept brought up by ETSI and it places computing, storage, processing and network resources into MEC hosts and places these MEC hosts as close as needed to the telecom network edge in order to reduce service latency and bandwidth usage. For self-driving vehicles, streaming video and real-time gaming, the devices involved (e.g. vehicles, cellphones, etc.) might not have enough capabilities to perform all the computations and might not have sufficient storage capacity; MEC can be used here for offloading data computations and content caching. To enhance service quality and user experience, MEC hosts and MEC applications should be located close(r) to the end-users, which increases the number of handovers between MEC hosts to maintain MEC service continuity for mobile end-users as well as the costs for the telecom operators. Therefore, a balance needs to be found. Consider the fact that mobile UEs need MEC service handovers to maintain service continuity and handovers may cause service interruptions which can cause severe degradation to MEC service qualities and user experience, hence the number of handovers between MEC hosts experienced by end-users should be minimized. To find a suitable deployment of MEC hosts and MEC applications in order to minimize the number of handovers, three greedy algorithms and two heuristic algorithms are introduced, implemented, tested, compared and analyzed in this thesis to see which identifies the deployment mechanism that has the smallest number of handovers. When it is time for a mobile UE to connect to a new MEC host and there are multiple potential choices of the new MEC host, the most suitable one for the UE needs to be determined dynamically according to the real-time condition of each possible MEC host. To achieve this, reinforcement learning is considered. Three different reinforcement learning algorithms based on SARSA learning and Deep Q Network are introduced, implemented, tested, compared and analyzed in this thesis. Furthermore, a decision-making mechanism is designed to cope with exceptional situations where the required service quality cannot be guaranteed. ... Read more