Outage Analysis of Spectrum-Sharing over M-Block Fading with Sensing Information

AbdulRahman Alabbasi, Zouheir Rezki, Basem Shihada

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Future wireless technologies, such as, 5G, are expected to support real-time applications with high data throughput, e.g., holographic meetings. From a bandwidth perspective, cognitive radio is a promising technology to enhance the system’s throughput via sharing the licensed spectrum. From a delay perspective, it is well known that increasing the number of decoding blocks will improve the system robustness against errors, while increasing the delay. Therefore, optimally allocating the resources to determine the tradeoff of tuning the length of decoding blocks while sharing the spectrum is a critical challenge for future wireless systems. In this work, we minimize the targeted outage probability over the block-fading channels while utilizing the spectrum-sharing concept. The secondary user’s outage region and the corresponding optimal power are derived, over twoblocks and M-blocks fading channels. We propose two suboptimal power strategies and derive the associated asymptotic lower and upper bounds on the outage probability with tractable expressions. These bounds allow us to derive the exact diversity order of the secondary user’s outage probability. To further enhance the system’s performance, we also investigate the impact of including the sensing information on the outage problem. The outage problem is then solved via proposing an alternating optimization algorithm, which utilizes the verified strict quasiconvex structure of the problem. Selected numerical results are presented to characterize the system’s behavior and show the improvements of several sharing concepts.
Original languageEnglish (US)
Pages (from-to)3071-3087
Number of pages17
JournalIEEE Transactions on Vehicular Technology
Volume66
Issue number4
DOIs
StatePublished - Jul 14 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

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