Publication: Collaborative Compressive Spectrum Sensing in a UAV Environment
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Date
2011
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Institute of Electrical and Electronics Engineers
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Citation
Chen, Hsieh-Chung, H.T. Kung, Dario Vlah, Daniel Hague, Michael Muccio, and Brendon Poland. 2011. Collaborative compressive spectrum sensing in a UAV environment. In Proceedings of Military Communications Conference (MILCOM 2011), 142-148. Piscataway, New Jersey: IEEE Communications Society.
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Abstract
Spectrum sensing is of fundamental importance to many wireless applications including cognitive radio channel assignment and radiolocation. However, conventional spectrum sensing can be prohibitively expensive in computation and network bandwidth when the bands under scanning are wide and highly contested. In this paper we propose distributed spectrum sensing with multiple sensing nodes in a UAV environment. The ground nodes in our scheme sense the spectrum in parallel using compressive sensing. Each sensor node transmits compressive measurements to a nearby UAV in the air. The UAV performs decoding on the received measurements; it decodes information with increasing resolution as it receives more measurements. Furthermore, by a property of compressive sensing decoding, frequencies of large magnitude responses are recovered first. In the proposed scheme, as soon as the UAV detects the presence of such high-power frequencies from a sensor, this information is used to aid decoding for other sensors. We argue that such collaboration enabled by UAV will greatly enhance the decoding accuracy of compressive sensing. We use packet-loss traces acquired in UAV flight experiments in the field, as well as field experiments involving software-defined radios, to validate the effectiveness of this distributed compressive sensing approach.
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Keywords
collaboration, compressed sensing, decoding, sensors, time measurement, transmitters, vectors, autonomous aerial vehicles, cognitive radio, decoding, wireless channels, UAV environment, UAV flight, cognitive radio channel assignment, collaborative compressive spectrum sensing, compressive sensing decoding, conventional spectrum sensing, distributed spectrum sensing, high-power frequencies, multiple sensing nodes, network bandwidth, packet-loss traces, radiolocation, sensor, software-defined radios, wireless applications
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