Document Type dissertation Author Name guo, wenxuan Email Address wenxuanguo at gmail.com URN etd-042611-192503 Title Resource Allocation and Performance Optimization in Wireless Networks Degree PhD Department Electrical & Computer Engineering Advisors Xinming Huang, Advisor Wenjing Lou, Committee Member Andrew G. Klein, Committee Member Donald R. Brown III, Committee Member Youjian Liu, Committee Member Keywords wireless networks resource allocation performance optimization Date of Presentation/Defense 2011-04-26 Availability unrestricted
As wireless networks continue streaking through more aspects of our lives, it is seriously constrained by limited network resources, in terms of time, frequency and power. In order to enhance performance for wireless networks, it is of great importance to allocate resources smartly based on the current network scenarios. The focus of this dissertation is to investigate radio resource management algorithms to optimize performance for different types of wireless networks.
Firstly, we investigate a joint optimization problem on relay node placement and route assignment for wireless sensor networks. A heuristic binary integer programming algorithm is proposed to maximize the total number of information packets received at the base station during the network lifetime. We then present an optimization algorithm based on binary integer programming for relay node assignment with the current node locations. Subsequently, a heuristic algorithm is applied to move the relay nodes to the locations iteratively to better serve their associated edge nodes.
Secondly, as traditional goal of maximizing the total throughput can result in unbalanced use of network resources, we study a joint problem of power control and channel assignment within a wireless mesh network such that the minimal capacity of all links is maximized. This is essentially a fairness problem. We develop an upper bound for the objective by relaxing the integer variables and linearization. Subsequently, we put forward a heuristic approach to approximate the optimal solution, which tries to increase the minimal capacity of all links via setting tighter constraint and solving a binary integer programming problem. Simulation results show that solutions obtained by this algorithm are very close to the upper bounds obtained via relaxation, thus suggesting that the solution produced by the algorithm is near-optimal.
Thirdly, we study the topology control of disaster area wireless networks to facilitate mobile nodes communications by deploying a minimum number of relay nodes dynamically. We first put forward a novel mobility model for mobile nodes that describes the movement of first responders within a large disaster area. Secondly, we formulate the square disk cover problem and propose three algorithms to solve it, including the two-vertex square covering algorithm, the circle covering algorithm and the binary integer programming algorithm.
Fourthly, we explore the joint problem of power control and channel assignment to maximize cognitive radio network throughput. It is assumed that an overlaid cognitive radio network (CRN) co-exists with a primary network. We model the opportunistic spectrum access for cognitive radio network and formulate the cross-layer optimization problem under the interference constraints imposed by the existing primary network. A distributed greedy algorithm is proposed to seek for larger network throughput. Cross-layer optimization for CRN is often implemented in centralized manner to avoid co-channel interference. The distributed algorithm coordinates the channel assignment with local channel usage information. Thus the computation complexity is greatly reduced.
Finally, we study the network throughput optimization problem for a multi-hop wireless network by considering interference alignment at physical layer. We first transform the problem of dividing a set of links into multiple maximal concurrent link sets to the problem of finding the maximal cliques of a graph. Then each concurrent link set is further divided into one or several interference channel networks, on which interference alignment is implemented to guarantee simultaneous transmission. The network throughput optimization problem is then formulated as a non-convex nonlinear programming problem, which is NP-hard generally. Thus we resort to developing a branch-and-bound framework, which guarantees an achievable performance bound.
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