Optimal spectrum sensing interval in cognitive radio networks

Xiaoshuang Xing, Tao Jing, Hongjuan Li, Yan Huo, Xiuzhen Cheng, Taieb Znati

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)

Abstract

Traditional spectrum sensing methods require that a secondary user (SU) senses the spectrum at the beginning of each time slot. A closer look at the network activities of a cognitive radio network reveals that the access pattern of a primary user (PU) typically consists of a succession of transmission periods, alternating with idle periods, each of which lasts a number of time slots. Based on this observation, it becomes clear that forcing the SU to sense the channel at the beginning of each time slot is unnecessary and may lead to considerable waste of energy. The main objective of this paper is to investigate new approaches for spectrum sensing by exploring the tradeoffs between energy consumption and secondary network throughput. To this end, we propose a stochastic, energy-aware model to derive the optimal spectrum sensing interval an SU can use to dynamically determine when the next spectrum sensing should be performed. The proposed model allows an SU to adaptively derive the sensing interval based on its required quality of service and current network state, including the PU's network activities and traffic load. Extensive simulation study is performed to assess the effectiveness of our proposed approach in achieving high accuracy with reduced energy consumption. The analysis of the results show that careful tuning of key parameters leads to improved energy efficiency and increased secondary network throughput.

Original languageEnglish
Article number6522414
Pages (from-to)2408-2417
Number of pages10
JournalIEEE Transactions on Parallel and Distributed Systems
Volume25
Issue number9
DOIs
Publication statusPublished - Sep 2014
Externally publishedYes

Keywords

  • Optimal spectrum sensing interval
  • cognitive radio networks
  • hidden Markov models

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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