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Cognitive Radio and TV White Space (TVWS) Applications

Martin, J. H.; Dooley, L. S. and Wong, K. C. P. (2019). Cognitive Radio and TV White Space (TVWS) Applications. In: Zhang, Wei ed. Handbook of Cognitive Radio. Singapore: Springer Nature Singapore Pte Ltd., pp. 1935–1970.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-981-10-1394-2_62
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Abstract

As more user applications emerge for wireless devices, the corresponding amount of traffic is rapidly expanding, with the corollary that ever-greater spectrum capacity is required. Service providers are experiencing deployment blockages due to insufficient bandwidth being available to accommodate such devices. TV White Space (TVWS) represents an opportunity to supplement existing licensed spectrum by exploiting unlicensed resources. TVWS spectrum has materialised from the unused TV channels in the switchover from analogue to digital platforms. The main obstacles to TVWS adoption are reliable detection of primary users (PU) i.e., TV operators and consumers, allied with specifically, the hidden node problem. This chapter presents a new Generalised Enhanced Detection Algorithm (GEDA) that exploits the unique way Digital Terrestrial TV (DTT) channels are deployed in different geographical areas. GEDA effectively transforms an energy detector into a feature sensor to achieve significant improvements in detection probability of a DTT PU. Furthermore, by framing a novel margin strategy utilising a keep out contour, the hidden node issue is resolved, and a viable secondary user sensing solution formulated. Experimental results for a cognitive radio TVWS model have formalised both the bandwidth and throughput gains secured by TVWS users with this new paradigm.

Item Type: Book Section
Copyright Holders: 2019 Springer Nature Singapore Pte Ltd.
ISBN: 981-10-1393-4, 978-981-10-1393-5
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 55362
Depositing User: Laurence Dooley
Date Deposited: 11 Sep 2018 14:56
Last Modified: 19 Jul 2019 08:11
URI: http://oro.open.ac.uk/id/eprint/55362
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