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Power control and asymptotic throughput analysis for the distributed cognitive uplink
Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3010, Australia.
Antalya Int Univ, Dept Elect & Elect Engn, Antalya, Turkey.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
2014 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 1, 41-58 p.Article in journal (Refereed) Published
Abstract [en]

This paper studies optimum power control and sum-rate scaling laws for the distributed cognitive uplink. It is first shown that the optimum distributed power control policy is in the form of a threshold based water-filling power control. Each secondary user executes the derived power control policy in a distributed fashion by using local knowledge of its direct and interference channel gains such that the resulting aggregate (average) interference does not disrupt primary's communication. Then, the tight sum-rate scaling laws are derived as a function of the number of secondary users N under the optimum distributed power control policy. The fading models considered to derive sum-rate scaling laws are general enough to include Rayleigh, Rician and Nakagami fading models as special cases. When transmissions of secondary users are limited by both transmission and interference power constraints, it is shown that the secondary network sum-rate scales according to 1/en(h) log log (N), where n(h) is a parameter obtained from the distribution of direct channel power gains. For the case of transmissions limited only by interference constraints, on the other hand, the secondary network sum-rate scales according to 1/e gamma(g) log (N), where gamma(g) is a parameter obtained from the distribution of interference channel power gains. These results indicate that the distributed cognitive uplink is able to achieve throughput scaling behavior similar to that of the centralized cognitive uplink up to a pre-log multiplier 1/e, whilst primary's quality-of-service requirements are met. The factor 1/e can be interpreted as the cost of distributed implementation of the cognitive uplink.

Place, publisher, year, edition, pages
2014. Vol. 64, no 1, 41-58 p.
Keyword [en]
Cognitive radio; multiple access channels; power control; throughput scaling; distributed algorithms
National Category
Signal Processing Telecommunications
Research subject
Electrical Engineering with specialization in Signal Processing
URN: urn:nbn:se:uu:diva-214499DOI: 10.1109/TCOMM.2013.112413.130510ISI: 000330622000004OAI: oai:DiVA.org:uu-214499DiVA: diva2:684930
Available from: 2014-01-08 Created: 2014-01-08 Last updated: 2016-07-19Bibliographically approved

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