The global adoption of 5G technology is rapidly accelerating and 5G traffic is growing exponentially. This increase in demand compels network operators to evaluate whether their current and upcoming 5G infrastructure can effectively accommodate the growing data traffic. A key component within the 5G network is the User Plane Function (UPF), which connects the end-devices to the data networks. Therefore, it is vital for both equipment manufacturers and service providers to analyze the performance of existing UPF implementations. This paper presents an initial approach to assess the Open5GS UPF performance by conducting stress testing in virtualized environment. We focus on finding the optimal UPF configuration by measuring the resource consumption and latency of the UPF under varying intensity of generated traffic, and providing a simple queueing model for the UPF. Numerical results show that a CPU load of 70-80% balances between latency and throughput while ensuring that 99% of the packets are forwarded within 150 ms.

This paper investigates the latency and resilience of user-plane anchor reconfiguration in a fully virtualized 5G core environment using Open5GS and UERANSIM. The experiment spans five VirtualBox virtual machines, each hosting a key component of the 5G core or radio stack: 5G-core gNB, UPF1, UPF2, and a single UE. All nodes communicate over a shared internal network, ensuring controland user-plane traffic remains isolated from external variability. The UE is initially anchored to UPF 1 via DNN “internet.” After the initial tunnel is established and validated, a re-anchoring procedure is triggered by calling the SMF’s REST API. Although the endpoint is intended to perform a PFCP Session Modification, Open5GS tears down the session and creates a new one on UPF 2 instead. By analyzing timestamped UE logs—capturing tunnel setup, session release, and re-establishment—we measure the latency of user-plane reattachment. Our results reveal high variability in recovery times, ranging from sub-second to over 50 seconds. These inconsistencies are attributed to limitations in Open5GS’s session handling, the lack of true migration support, and hardware limitations of the used machine. Despite these challenges, the study offers insights into the practical behavior of PFCP-driven anchor reconfiguration and the operational gaps that remain in open-source 5G core implementations.

Academic research in 5G networking faces a lack of accessible, realistic packet-level datasets, limiting innovation and reproducibility. This paper evaluates two state-of-the-art machine learning approaches, PAC-GAN and TabularARGN, for generating synthetic 5G TCP/IP packet headers. Using a real 5G packet-capture dataset, we adapt both models to include inter-packet timing and rigorously assess them on protocol validity, marginal distribution alignment, and joint distribution fidelity. Results show that PAC-GAN produces highly valid and statistically faithful synthetic packets, effectively modeling complex header dependencies and temporal patterns. While TabularARGN ensures strict protocol compliance, it struggles to capture higher-order correlations and traffic diversity. Our findings establish convolutional generative models like PAC-GAN as practical tools for producing realistic, protocol-compliant synthetic 5G traffic, broadening access to datasets for benchmarking and security testing.

Modern mobile networks must adapt to rapidly changing traffic patterns and increasing user demands. A key challenge is understanding where user traffic terminates and how these destinations vary over time. This thesis addresses this challenge by introducing an open-source, modular analysis framework that analyzes passive Internet traffic traces, enriches them with geolocation and organizational metadata, and infers latency stability and routing dynamics, in order to characterize the infrastructures that terminate user traffic and assess their performance and reliability over time. The results show a long-term shift towards content-centric traffic, highlight geographic and temporal variations in performance, and demonstrate that content networks typically offer greater stability than enterprise or research destinations. These findings support adaptive traffic management strategies in 5G and future 6G networks.

With the exponential growth of mobile traffic in 5G networks, accurate forecasting is essential for efficient resource management. This research provides a comparative analysis of time series forecasting models for predicting near-future network traffic. Using a public dataset from a 5G base station in Barcelona, this study evaluates the performance of a traditional statistical model, against deep learning models: a Recurrent Neural Network (RNN), a Long Short-Term Memory (LSTM) and a Google timesFM model. The results demonstrate that while the SARIMAX model struggles to capture near-future traffic demand, the deep learning approaches yield significantly higher predictive accuracy. Specifically, a simple LSTM architecture shows great results, outperforming even a more complex one. However, the timesFM model, in particular, shows the most robust generalization capabilities. Additionally, the models trained on data from one base station do not generalize well to others, highlighting significant differences in traffic characteristics even between geographically close locations. This suggests that while locally trained LSTMs are a powerful tool, future work should focus on developing more adaptive and transferable models, such as those using federated learning or graph neural networks.^p