A Self-Management Approach to Configuring Wireless Infrastructure Networks
Wireless infrastructure networks provide high-speed wireless connectivity over a small geographical area. The rapid proliferation of such networks makes their management not only more important but also more difficult. Denser network deployments lead to increased wireless contention and greater opportunities for RF interference, thereby decreasing performance. <br /><br /> In the past, wireless site surveys and simplified wireless propagation models have been used to design and configure wireless systems. However, these techniques have been largely unsuccessful due to the dynamic nature of the wireless medium. More recently, there has been work on dynamically configurable systems that can adapt to changes in the surrounding environment. These systems improve on previous approaches but are still not adequate as their solutions make unrealistic assumptions about the operating environment. Nevertheless, even with these simplified models, the network design and configuration problems are inherently complex and require tradeoffs among competing requirements. <br /><br /> In this thesis, we study a self-management system that can adjust system parameters dynamically. We present a system that does not impose any restrictions on the operating environment, is incrementally deployable, and also backwards compatible. In doing so, we propose, (i) framework for modeling system performance based on utility functions, (ii) novel approach to measuring the utility of a given set of configuration parameters, and (iii) optimization techniques for generating and refining system configurations to maximize utility. Although our utility-function framework is able to capture a variety of optimization metrics, in this study, we focus specifically on maximizing network throughput and minimizing inter-cell interference. Moreover, although many different techniques can be used for optimizing system performance, we focus only on transmit-power control and channel assignment. We evaluate our proposed architecture in simulation and show that our solution is not only feasible, but also provides significant improvements over existing approaches.