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.

基于边缘计算的新型预测框架使用通过大规模测量活动在边缘获取的数据集，设计了基于卷积神经网络（CNN）和循环神经网络（RNN）的两种主要深度学习架构，并在不同训练条件下进行了测试。使用知识迁移学习（KTL）技术改进模型性能并降低计算资源需求，模拟结果表明CNN架构优于RNN。提供了对所需训练能量的估计，并应用两种前沿可解释人工智能技术来解释得出的学习模型。

在边缘通过分布式和迁移学习进行移动流量预测