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Sensing Time Optimization and Power Control for Energy Efficient Cognitive Small Cell with Imperfect Hybrid Spectrum Sensing

Research output: Contribution to journalArticle

Haijun Zhang, Yani Nie, Julian Cheng, Victor C.M. Leung, Arumugam Nallanathan

Original languageEnglish
Pages (from-to)730-743
JournalIEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume16
Issue number2
DOIs
Publication statusPublished - 1 Feb 2017

Documents

  • Sensing Time Optimization_ZHANG_AcceptedNovember2016_GREEN AAM

    Haijun_TWC_16.pdf, 237 KB, application/pdf

    4/11/2016

    Accepted author manuscript

    “© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted.

King's Authors

Abstract

Cognitive radio enabled small cell network is an emerging technology to address the exponential increase of mobile traffic demand in the next generation mobile communications. Recently, many technological issues such as resource allocation and interference mitigation pertaining to cognitive small cell network have been studied, but most studies focus on maximizing spectral efficiency. Different from the existing works, we investigate the power control and sensing time optimization problem in a cognitive small cell network, where the cross-tier interference mitigation, imperfect hybrid spectrum sensing, and energy efficiency are considered. The optimization of energy efficient sensing time and
power allocation is formulated as a non-convex optimization problem. We solve the proposed problem in an asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed by considering imperfect hybrid spectrum sensing, cross-tier interference
mitigation, minimum data rate requirement and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in the cognitive small cell network.

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