Energy-Efficient Power Allocation in Cognitive Radio Systems with Imperfect Spectrum Sensing

Gozde Ozcan, M. Cenk Gursoy, Nghi Tran, Jian Tang

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


This paper studies energy-efficient power allocation schemes for secondary users in sensing-based spectrum sharing cognitive radio systems. It is assumed that secondary users first perform channel sensing possibly with errors and then initiate data transmission with different power levels based on sensing decisions. In this setting, the optimization problem is to maximize energy efficiency (EE) subject to peak/average transmission power constraints and peak/average interference constraints. By exploiting the quasi-concave property of the EE maximization problem, the original problem is transformed into an equivalent parameterized concave problem, and an iterative power allocation algorithm based on Dinkelbach's method is proposed. The optimal power levels are identified in the presence of different levels of channel side information (CSI) regarding the transmission and interference links at the secondary transmitter, namely, perfect CSI of both transmission and interference links, perfect CSI of the transmission link, imperfect CSI of the interference link, imperfect CSI of both links, or only statistical CSI of both links. Through numerical results, the impact of sensing performance, different types of CSI availability, and transmit and interference power constraints on the EE of the secondary users is analyzed.

Original languageEnglish (US)
Article number7676309
Pages (from-to)3466-3481
Number of pages16
JournalIEEE Journal on Selected Areas in Communications
Issue number12
StatePublished - Dec 2016


  • Dinkelbach's method
  • energy efficiency
  • imperfect spectrum sensing
  • imperfect/perfect/statistical CSI
  • interference power constraint
  • power allocation
  • probability of detection
  • probability of false alarm
  • transmit power constraint

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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