Zhanwei Yu: Selected Topics on Optimal Allocation and Configuration in Mobile Computing for 5G and Beyond

Abstract

This dissertation explores optimal allocation and configuration in mobile computing for 5G and beyond. As wireless technologies rapidly evolve, emerging technologies such as integrated terrestrial, aerial, and satellite networks (ITASNs), integrated sensing and communication (ISAC), reconfigurable intelligent surfaces (RIS), edge computing, AI-driven networking, cell-free multiple-input multiple-output (MIMO), movable antenna systems, and passively cooled base stations (PCBS) are reshaping network design. These innovations promise significant improvements in capacity, energy efficiency, and sustainability, but also introduce challenges in resource allocation and configuration.

The bulk of this dissertation comprises five research papers that address key resource allocation and configuration problems for some of the evolving technologies. Paper I presents a novel framework for jointly optimizing RIS configuration and resource allocation in multi-cell networks. Paper II investigates content caching in edge computing, proposing a column generation-based approach for balancing cost and data freshness. Paper III examines renewable energy management in edge computing networks to minimize the carbon footprint while maintaining performance. Papers IV and V focus on thermal management in PCBS, with Paper IV developing an online reinforcement learning method for dynamic load allocation in a single base station and Paper V extending this approach to multi-cell scenarios with inter-cell interference and resource coupling.