2020/10/23 by Chaoyun Zhang, Zhang, Chaoyun · 1 citation
Computer Science · Engineering · #Artificial intelligence #Artificial neural network #Big data #Computer science #Context (archaeology) #Data mining #Data science #Deep learning #Deep neural networks #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Feature engineering #IoT-based Smart Home Systems #Machine Learning (cs.LG) #Machine learning #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI) #cs.LG #cs.NI
paper · pdf · doi:10.48550/arxiv.2011.05267
published in arXiv (Cornell University) (Cornell University) · PhD thesis, University of Edinburgh (2020)
arxiv created 2020/10/23 · openalex publication_date 2020/10/23 · arxiv updated 2020/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.