User-centric strategies for resource management in heterogeneous wireless networks with QoS considerations -

Abstract

Demand for mobile applications is increasing at an exponential rate which is loading existing wireless networks. The research community is currently actively involved in the design of new technologies that can enable massive device connections with the needed speeds and reliability. To this end, a major opportunity is to design solutions that facilitate the dynamic utilization and seamless operation of heterogeneous networks where devices can utilize multiple wireless interfaces simultaneously and cooperate with other devices in their vicinity. In this thesis, we propose and evaluate novel solutions to address emerging challenges related to the design of next generation heterogeneous wireless networks. Our research work is divided into two key objectives: the first objective aims at designing effective user-centric resource management techniques in cellular-WiFi heterogeneous networks with quality of experience considerations, and the second objective aims at optimizing traffic offloading in highly dense wireless networks using device-to-device cooperation, local caching, and planned channel allocation. To achieve the first objective, we propose cellular-WiFi resource management strategies for a single-user scenario where a user can take advantage of the coexistence of multiple wireless interfaces to achieve performance gains. We first design a learning-based approach for network selection where a user utilizes one wireless interface at a time to achieve either minimum energy consumption, maximum throughput or energy efficiency based on user preferences. We then formulate the static traffic splitting problem, where a user utilizes both interfaces simultaneously, as a multi-objective optimization approach that captures the tradeoffs between throughput maximization on one hand and device battery energy minimization on the other hand. We then extend our work to address real-time traffic splitting decisions capturing the tradeoff between queue stability, energy consumption, and quality of experience for video stre