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Cognitive radio (CR) is being envisioned as a promising technology to realize new strategies to combat with coexistence problems in wireless systems. Currently, opportunistic or dynamic spectrum access and transmission power control are popular coexistence optimization strategies among the CR research community. We propose a coexistence optimized cognitive engine (COCE), which combines machine learning with expert knowledge. COCE classifies coexisting radio systems using a fuzzy logic based signal classifier and in turn chooses suitable transmission parameters for the underlying radio platform from its knowledge base. Initially, we implement COCE as a TPC engine, which chooses the optimal transmission power for the underlying radio platform to ensure a desired quality-of-service. We implemented a testbed to demonstrate the performance of COCE using conventional microcontroller (μC) as well as super heterodyne transceivers and present the results in this contribution.
Date of Conference: 13-16 Sept. 2010