Deep Neural Mobile Networking
This work aims to improve mobile network management for operators and users by embedding AI to handle diverse performance requirements, though it appears incremental as it applies existing deep learning methods to this domain.
The paper tackles the complexity of next-generation mobile networks by proposing deep learning solutions to automatically extract features from raw data, addressing challenges in monitoring and managing network elements without hand-crafted feature engineering.
The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse performance requirements in terms of throughput, latency, and reliability. This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering. In this context, embedding machine intelligence into mobile networks becomes necessary, as this enables systematic mining of valuable information from mobile big data and automatically uncovering correlations that would otherwise have been too difficult to extract by human experts. In particular, deep learning based solutions can automatically extract features from raw data, without human expertise. The performance of artificial intelligence (AI) has achieved in other domains draws unprecedented interest from both academia and industry in employing deep learning approaches to address technical challenges in mobile networks. This thesis attacks important problems in the mobile networking area from various perspectives by harnessing recent advances in deep neural networks.