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Optimal power policies and throughput scaling analyses in fading cognitive broadcast channels with primary outage probability constraint
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
2014 (English)In: EURASIP Journal on Wireless Communications and Networking, ISSN 1687-1472, E-ISSN 1687-1499, 35- p.Article in journal (Refereed) Published
Abstract [en]

This paper focuses on a spectrum-sharing-based fading cognitive radio broadcast channel (BC) with a single-antenna secondary base station (SBS) and M single-antenna secondary receivers (SRs) utilizing the same spectrum band with a delay-sensitive primary user (PU). The service-quality requirement for the primary user is set by an outage probability constraint (POC). We address the optimal power allocation problem for the SBS ergodic sum capacity (ESC) maximization in the secondary BC network subject to POC and a transmit power constraint at SBS specified by either a long-term or a short-term power constraint. The optimality conditions reveal that in each joint channel state, the SBS allocates transmission power to the only one selected SR with the highest value of a certain metric consisting of the ratio of the SR's direct channel power gain and the sum of interference power and noise power at the SR. Then, the secondary network throughput scaling analysis as the number of SRs becomes large, is also investigated, showing that if PU applies a truncated channel inversion (TCI) power policy, the SBS ESC scales like epsilon(p) log(log M) where epsilon(p) is the PU outage probability threshold. To reduce the amount of channel side information (CSI) transferred between the two networks, we propose a suboptimal transmission scheme which requires only 1-bit feedback from the delay-sensitive PR (partial CSI). We show that the new power control policy is asymptotically optimal, i.e. the SBS ESC under this reduced feedback scheme still scales like epsilon(p) log(log M).

Place, publisher, year, edition, pages
2014. 35- p.
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
URN: urn:nbn:se:uu:diva-235000DOI: 10.1186/1687-1499-2014-35ISI: 000347397500001OAI: diva2:758696
Available from: 2014-10-28 Created: 2014-10-28 Last updated: 2015-02-11Bibliographically approved

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Dey, Subhrakanti
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