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My current research interests are in the areas of statistical signal processing and stochastic analysis, and their applications in several fields, notably wireless networks.
A major contemporary issue in the design and deployment of wireless networks is the dramatic increase in demand for new capacity and higher performance. The development of these capabilities is limited severely by the scarcity of two of the principal resources in wireless networks, namely energy and bandwidth. Consequently, the community has turned to a third principal resource, the deployment of intelligence at all layers of the network, in order to exploit increases in processing power afforded by Moore's Law type improvements in microelectronics. My recent work has focused on two basic aspects of this effort. The first of these is the use of advances in physical-layer communication methodology to improve the higher-layer performance of wireless communication networks, notably energy efficiency, throughput and delay. Here, techniques from game theory and optimization have been applied to integrate advanced physical-layer signal processing into the design of higher-layer functionalities such as admission control, resource allocation, etc. The second basic aspect of this work is the use of advanced statistical signal processing principles to improve the efficiency and efficacy of wireless sensor networks. Contributions in this latter area have included energy-saving techniques based on collaboration among sensors, inference-optimal sensor scheduling, and distributed learning.
Preprints of my recent and forthcoming publications describing this research can be found on the ArXiv website. Other publications describing recent research progress in areas such as statistical change detection, communications and information theory, and mathematical finance, can be found there as well.
Model for distributed learning in a wireless sensor network.