  Towards the network innovation, the Beyond Five-Generation (B5G) networks
envision the use of machine learning (ML) methods to predict the network
conditions and performance indicators in order to best make decisions and
allocate resources. In this paper, we propose a new ML approach to accomplish
predictions in B5G networks. Instead of handling the time-series in the network
domain of values, we transform them into image thus allowing to apply advanced
ML methods of Computer Vision field to reach better predictions in B5G
networks. Particularly, we analyze different techniques to transform
time-series of network measures into image representation, e.g., Recurrence
Plots, Markov Transition Fields, and Gramian Angular Fields. Then, we apply
deep neural networks with convolutional layers to predict different 5G radio
signal quality indicators. When comparing with other ML-based solutions,
experimental results from 5G transmission datasets showed the feasibility and
small prediction errors of the proposed approach.
