Explosion of wireless products and innovative use of the ISM bands lead to a very crowded spectrum space. When densely deployed, significant performance degradation may be experienced ranging from higher latency and lower data rate to starvation and service disruption. To tackle the co-existence problems, two key challenges need to be addressed. First, there exists innate uncertainty in channel quality, user location and population as well as coexisting devices and networks. Second, many emerging applications using radio technologies in the ISM bands require high availability and predictable services instead of large access bandwidth. The focus of this project is thus to develop theoretical models and algorithms for robust resource management that target at minimizing the outage and/or disruption of desired service level under varying resource availability in 802.11 like networks. This work will result in i) new methods and measurement procedures for inferring internal and external conditions of broadband wireless networks; ii) novel concept of effective margin as a quantifiable metrics for the robustness of resource management decisions; and iii) design of an optimization framework for robust resource management with both discrete and continuous constraints. The proposed activities serve as fundamental building blocks to address wireless co-existence issues in industry, commercial and medical domains, where severe interferences can possibly cause tremendous economic loss and claim human lives. The interdisciplinary nature of this research lends naturally to a combine engineering and science curriculum development at both undergraduate and graduate levels with a significant experimental component.
. The solutions developed in this project can improve the QoS in operational multi-channel wireless networks to facilitate multimedia applications and/or safe-critical applications. More specially, we have advanced the state-of-the-art in 1) new methods and measurement procedures for inferring internal and external conditions of broadband wireless networks; 2) robust resource management solutions under varying channel conditions. Findings of the project have been reported in 6 journal papers (4 accepted, 2 in submission) and 12 conference papers. Some methodologies developed in the project find applications in other areas of networking including, network tomography, smart grid security, cognitive radio networks, etc. 4 MS students, 2 Ph.D. students and 1 post-doc have been involved in this project. Students have been trained in applying theories (optimization, probability theory, and machine learning) in the design of practical solutions. Implementation and measurement studies using our mesh networks give students hands on experiences in programming embedded systems and experiment design. Results from the project have been disseminated through conference presentation, research seminars, departmental showcase, Research experience for undergraduate presentation etc. Software developed in the project has been made available on our group research website.
