Energy efficient integrated resource management for wireless networks

In this dissertation, an integrated radio resource management (RRM) framework for cognitive radio networks is proposed to share network resources adaptively, efficiently and fairly. Our proposed framework broadens the scope of the traditional RRM mechanism by incorporating route configuration with the legacy resource management in terms of power control and spectrum allocation. The scope of RRM is also advanced from node-centric, individual optimization into network-wide optimization for improving the users' common satisfaction over the network. In the proposed framework, the interactions among the network elements are categorized into vertical interactions across physical, MAC and network layers and horizontal interactions among the users contending for network resources. The former motivates our cross-layering design, while the latter reflects the impact of an individual user's behavior on the others, which drives the cognition circle of a cognitive radio network.;We have applied our proposed framework for various practical RRM problems (such as Power Control, Channel Allocation and Route Assignment) and we have investigated the possible implementations for different network scenarios. We have studied cross-layer RRM algorithms in networks where multiple users share one common frequency channel and multiple frequency channels respectively. Game Theory has been employed as our analyzing tool to model the users' interactions and examine the equilibrium of the adaptive resource management algorithms. Our research results illustrate that the selection of the cost and utility definitions is of great importance in the design of an adaptive algorithm. A utility function having appealing mathematical properties will guarantee equilibrium convergence for the adaptation process.;Furthermore, for a practical implementation, we introduce a distributive, three-way handshaking protocol for our adaptive cross-layer channel allocation problem. Finally, we examine the performance gains and the system overhead introduced by cross-layering. Throughout our thesis, we emphasize on performance tradeoffs in terms of complexity versus performance gains, which are illustrated by both analysis and simulation.;This dissertation provides an insight into the design and implementation of RRM algorithms within the new paradigm of cognitive radio networks, emphasizing on cross-layer adaptation design and performance analysis, and on quantifying performance tradeoffs for various network scenarios.