Transfer Learning Based Diagnosis for Configuration Troubleshooting in Self-Organizing Femtocell Networks

Diagnosis for configuration troubleshooting in femtocell networks is extremely important for end users and network operators. However, because the small-size femtocell only serves several users, the historical data are very scarce. The data scarcity makes traditional cellular troubleshooting solutions which require a large amount of historical data not applicable. In this paper, we propose a new framework based on transfer learning technology to address the data scarcity so as to enhance the accuracy of the diagnosis model. The proposed framework extracts additional diagnosis knowledge by transferring data information from other femtocells. Based on this framework, we design a Cell-Aware Transfer scheme (CAT), which splits data for each femtocell to further enhance the diagnosis accuracy. Extensive evaluations show that our approach can achieve higher accuracy than traditional methods in self-organizing femtocell network scenarios.