Collaborative Research: EARS: Large-Scale Statistical Learning Based Spectrum Sensing and Cognitive Networking
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abstract
As cognitive radio (CR) research advances to multihop and complex systems over large geographic regions, the spectrum utilization enhancement should be generalized to fully exploit the spectrum usage diversity in three dimensions (3D): time, frequency, and space, with new emphasis on the under-explored spatial dimension. Accordingly, this project focuses on the following three research objectives. The first one is to utilize the recent advancements in statistical learning over big data to develop efficient 3D spectrum sensing schemes, where a hierarchical approach is taken in developing novel finite-bit and single-bit learning techniques to efficiently explore the correlation structure across the three dimensions, with an advanced distributed approach also developed. The second one is to develop two key building blocks in large-scale CR networking based on the 3D spectrum sensing: 1) a novel multi-scale routing scheme to enhance the overall spectrum utilization, with a focus on exploiting the layered spectrum usage correlation structure in the spatial dimension; and 2) a reliable hierarchical common control channel identification scheme. The last research objective is to validate some key aspects in the proposed sensing and networking schemes via both intensive simulations and a concept-proving testbed. Throughout the project, an interdisciplinary approach is taken to combine the methods of statistical learning, signal processing, and wireless networking, with the core built upon the hierarchical treatment of both spectrum usage statistics and CR networking methodologies. The project provides both theories and algorithms for large-scale spectrum sensing and cognitive networking. Through a coherent education plan, the research findings will be incorporated into courses, and disseminated to the community via journal papers and conference presentations.
