|PARERA SOTOLONGO CLAUDIA||Cycle: XXXII |
Tutor: RIVA CARLO GIUSEPPE
Advisor: CESANA MATTEO Major Research topic
:Enhancing Anticipatory Networking: A Transfer Learning ApproachAbstract:
Machine learning will play a major role in handling the complexity of future mobile wireless networks by enhancing network management and orchestration capabilities. Due to the large number of parameters that can be configured in the network, collecting and processing high volumes of data is often unfeasible during network runtime. This calls for taking resource management and service orchestration decisions with only a partial view of the network status.
Transfer learning is a machine learning paradigm that aims at improving the prediction performance of a learning task. Motivated by this fact, in this thesis we provide a transfer learning framework that can be applied to anticipate and further adapt network decisions when only a partial network view is available. Predictions can be carry out with improved performance when information in the target network domain is missed and multiple decisions need to be anticipated simultaneously. To this end, we evaluate the proposed framework in three different use cases using data collected from 4G commercial networks. Namely, they are: (i) Tilt-Dependent Radio Map Prediction, (ii) Mobile Radio Networks Key Performance Indicator Anticipation and (iii) Multi-Step Resource Utilization Prediction.
The main contribution of this thesis is the introduction of transfer learning for anticipatory networking in mobile communication networks with improved accuracy and complexity time.