IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Iterative Optimization of Energy Detector Sensing Time and Periodic Sensing Interval in Cognitive Radio Networks

Iterative Optimization of Energy Detector Sensing Time and Periodic Sensing Interval in Cognitive Radio Networks
View Sample PDF
Author(s): Mohamed Hamid (University of Gävle, The Royal Institute of Technology (KTH), Sweden), Niclas Björsell (University of Gävle, Sweden)and Abbas Mohammed (Blekinge Institute of Technology, Sweden)
Copyright: 2013
Pages: 17
Source title: Self-Organization and Green Applications in Cognitive Radio Networks
Source Author(s)/Editor(s): Anwer Al-Dulaimi (Brunel University, UK), John Cosmas (Brunel University, UK)and Abbas Mohammed (Blekinge Institute of Technology, Sweden)
DOI: 10.4018/978-1-4666-2812-0.ch003

Purchase


Abstract

In this chapter the authors propose a new approach for optimizing the sensing time and periodic sensing interval for energy detectors in cognitive radio networks. The optimization of the sensing time depends on maximizing the summation of the probability of right detection and transmission efficiency, while the optimization of periodic sensing interval is subject to maximizing the summation of transmission efficiency and captured opportunities. Since the optimum sensing time and periodic sensing interval are dependent on each other, an iterative approach to optimize them simultaneously is proposed and a convergence criterion is devised. In addition, the probability of detection, probability of false alarm, probability of right detection, transmission efficiency, and captured opportunities are taken as performance metrics for the detector and evaluated for various values of channel utilization factors and signal-to-noise ratios.

Related Content

Raquel Sánchez Ruiz, Isabel López Cirugeda. © 2024. 22 pages.
Rocío Luque-González, Inmaculada Marín-López, Mercedes Gómez-López. © 2024. 22 pages.
Bima Sapkota, Xuwei Luo, Muna Sapkota, Murat Akarsu, Emmanuel Deogratias, Daphne Fauber, Rose Mbewe, Fidelis Mumba, Ram Krishna Panthi, Jill Newton, JoAnn Phillion. © 2024. 34 pages.
Karen Collett, Alina Slapac, Sarah A. Coppersmith, Jingxin Cheng. © 2024. 29 pages.
Maria Ines Marino, Stephanie Tadal, Nurhayat Bilge. © 2024. 25 pages.
Jaqueline Naidoo, Noah Borrero. © 2024. 19 pages.
Crystal Machado, Tami Seifert. © 2024. 20 pages.
Body Bottom