  Traffic prediction represents one of the crucial tasks for smartly optimizing
the mobile network. The research in this topic concentrated in making
predictions in a centralized fashion, i.e., by collecting data from the
different network elements. This translates to a considerable amount of energy
for data transmission and processing. In this work, we propose a novel
prediction framework based on edge computing which uses datasets obtained on
the edge through a large measurement campaign. Two main Deep Learning
architectures are designed, based on Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs), and tested under different training
conditions. In addition, Knowledge Transfer Learning (KTL) techniques are
employed to improve the performance of the models while reducing the required
computational resources. Simulation results show that the CNN architectures
outperform the RNNs. An estimation for the needed training energy is provided,
highlighting KTL ability to reduce the energy footprint of the models of 60%
and 90% for CNNs and RNNs, respectively. Finally, two cutting-edge explainable
Artificial Intelligence techniques are employed to interpret the derived
learning models.
