The aim of this thesis is to investigate the feasibility of applying generative deep learning models on data related to 5G Radio Access Networks (5GRAN). Simulated data is used in order to develop the generative models, and this project serves as a proof of concept for further applications on real data. A Long Short-Term Memory network based Variational Autoencoder (VAE), Regularised Autoencoder (RAE) with a Gaussian Mixture prior and a Gradient Penalty Wasserstein Generative Adversarial Network (GP-WGAN) are fit by using the collected dataset. Their performance is evaluated in their ability to generate samples that resembles the real distribution and characteristics of the training data. Moreover, the performance is also measured in usability. The results indicates that it is indeed feasible to generate synthetic data given the current dataset, where the RAE and VAE seem to outperform the GP-WGAN in all tests, however, there is no clear best performer between RAE and VAE. Finally, whether the current models function on real 5G RAN data is not known and left for future work. Another topic of interest would be to improve the current models with conditional generation or other types of architectures.