Cognitive radio networks: System optimization and smart grid applications
This thesis presents the Cognitive Radio framework for wireless networks. The proposed Cognitive Radio framework is a complete model for Cognitive Radio that describes the decision and sharing procedures in wireless networks by introducing Queued Markov Chain method.
Queued Markov Chain method is capable of considering waiting time and is very well generalized for unlimited number of secondary users. It includes the sharing aspect of Cognitive Radio.
The proposed approach in this thesis uses pervasive smart grid systems (i.e. cloud data centers) as the central communication and optimization infrastructure supporting metropolitan area based smart meter infrastructure. In this thesis, we investigate the performance of various scheduling algorithms in context with CR units to provide a satisfactory tradeoff between maximizing the system capacity, achieving fairness among cognitive users.
This thesis also addresses improvements in the multiuser capacity in unplanned networks with high levels of co-channel interference. For this, a novel opportunistic interference aware scheduling protocol ideally suited for maximum channel reuse in unplanned networks. In this thesis, we analyze the application of SISO and MIMO interference aware scheduling to maximize the capacity and number of scheduled smart meters.
Moreover, a Fuzzy Logic-based framework and Particle Swarm Optimization are proposed for control of Battery Storage Unit in Micro-Grid Systems to achieve Efficient Energy Management. Typically, a Micro-Grid system operates synchronously with the main grid and also has the ability to operate independently from the main power grid in an islanded mode. The goal here is to control the amount of power delivered to/taken from the storage unit in order to improve a cost function, defined based on summation of payment required for purchasing power from main grid or profit obtained by selling power to the main grid and distribution power loss.