Caching in Fog Radio Access Networks: Modeling, Analysis and Optimization
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Online
Topic of the dissertation
Caching in Fog Radio Access Networks: Modeling, Analysis and Optimization
Doctoral candidate
Master of Science Tamoor-ul-Hassan Syed
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, CWC - Radio Technologies
Subject of study
Communication Engineering
Opponent
Professor Dr. Mohammed Elmusrati, University of Vaasa
Custos
Assistant Professor Dr. Sumudu Samarakoon, University of Oulu
Caching in Fog Radio Access Networks: Modeling, Analysis and Optimization
Existing 5G wireless networks comprises of hyper-connected users, machines, devices, Augmented/Extended Reality (AR/XR) requiring enhanced data rates, improved energy efficiency, seamless coverage and Ultra-reliable low-latency communication (URLLC). To cope with the challenges associated with the deployment of 5G wireless networks, power-hungry data-demanding applications such as real time interactive hologram services, immersive media, and virtual augmented reality require adaptive on-the-fly resources for interconnected devices, machines and users in a self-organized manner by using network slicing, artificial intelligence, blockchain and fog computing. Fog Radio Access Network (FRAN) provides an efficient platform to achieve the goals of 6G by incorporating cloud computing, fog computing, edge caching and artificial intelligence.
Cloud computing enables a centralized cloud server to compute and serve a user. Different from cloud computing, fog computing relies on several distributed servers with limited computation capabilities to serve the users. On the other hand, edge caching strategically exploits the low-cost storage elements at edge nodes to offload different network elements and reduces network congestion. The key goal of this thesis is to propose different methodologies to jointly solve the problem of content caching and resource allocation in cloud-aided wireless networks under latency constraints. The problem of edge caching for wireless networks is mainly investigated under three cases: a theoretical analysis of content caching problem with storage-bandwidth trade off in Small Cell Networks (SCNs), a learning-based caching in cloud-aided wireless networks, and a latency-aware radio resource optimization in learning-based cloud-aided wireless networks. Towards achieving these goals, this dissertation makes a number of key contributions including three journal papers and one conference paper which all are published.
Cloud computing enables a centralized cloud server to compute and serve a user. Different from cloud computing, fog computing relies on several distributed servers with limited computation capabilities to serve the users. On the other hand, edge caching strategically exploits the low-cost storage elements at edge nodes to offload different network elements and reduces network congestion. The key goal of this thesis is to propose different methodologies to jointly solve the problem of content caching and resource allocation in cloud-aided wireless networks under latency constraints. The problem of edge caching for wireless networks is mainly investigated under three cases: a theoretical analysis of content caching problem with storage-bandwidth trade off in Small Cell Networks (SCNs), a learning-based caching in cloud-aided wireless networks, and a latency-aware radio resource optimization in learning-based cloud-aided wireless networks. Towards achieving these goals, this dissertation makes a number of key contributions including three journal papers and one conference paper which all are published.
Last updated: 3.12.2024