Stability analysis of new paradigms in wireless networks

Fading in wireless channels, the limited battery energy available in wireless handsets, the changing user demands and the increasing demand for high data rate and low delay pose serious design challenges in the future generations of mobile communication systems. It is necessary to develop efficient transmission policies that adapt to changes in network conditions and achieve the target delay and rate with minimum power consumption. In this thesis, a number of new paradigms in wireless networks are presented. Dynamic programming tools are used to provide dynamic network stabilizing resource allocation solutions for virtualized data centers with clouds, cooperative networks and heterogeneous networks. Exact dynamic programming is used to develop optimal resource allocation and topology control policies for these networks with queues and time varying channels. In addition, approximate dynamic programming is also considered to provide new sub-optimal solutions. Unified system models and unified control problems are also provided for both secondary service provider and primary service provider cognitive networks and for conventional wireless networks. The results show that by adapting to the changes in queue lengths and channel states, the dynamic policy mitigates the effects of primary service provider and secondary service provider cognitive networks on each other. We investigate the network stability and provide new unified stability regions for primary service provider and secondary service provider cognitive networks as well as for conventional wireless networks. The K-step Lyapunov drift is used to analyse the performance and stability of the proposed dynamic control policies, and new unified stability analysis and queuing bound are provided for both primary service provider and secondary service provider cognitive networks and for conventional wireless networks. By adapting to the changes in network conditions, the dynamic control policies are shown to stabilize the network and to minimize the bound for the average queue length. In addition, we prove that the previously proposed frame based does not minimize the bound for the average delay, when there are shared resources between the terminals with queues.