Synthetic 5G Traffic Generation: A Machine Learning Approach

Bachelor Thesis (2025)
Author(s)

K.C. van der Deijl (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Nitinder Mohan – Mentor (TU Delft - Networked Systems)

M. Colocrese – Mentor (TU Delft - Networked Systems)

Guohao Guohao – Graduation committee member (TU Delft - Embedded Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2025
Language
English
Graduation Date
24-06-2025
Awarding Institution
Delft University of Technology
Project
['CSE3000 Research Project']
Programme
['Computer Science and Engineering']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

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.

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