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University of Ontario Institute of Technology

Spectrum sensing based on capon power spectral density estimation

Abstract

dc:description.abstract

Cognitive radio (CR) technology has evolved to solve the spectrum scarcity problem and improve spectrum utilization. Spectrum sensing is a CR function that allows secondary users to efficiently utilize the spectrum without interfering with primary users. The performance of this function depends on the efficiency of the used detection method. In this thesis, we propose a spectrum sensing based on the Capon Power Spectral Density (PSD) estimation method. The proposed method estimates the received PSD, and uses it to identify free and busy channels. A cooperative spectrum sensing approach is also introduced. The goal is to solve the common hidden node problem and help devices without CR capability to identify free channels. Experimental results show that the proposed method outperforms the spectrum sensing based on the Periodogram method in detecting both busy and free channels. In addition, simulation results show that the cooperative approach improves the spectrum sensing function.

Degree

thesis:*
Name thesis:degree_name
Master of Applied Science (MASc)
Discipline thesis:degree_discipline
Electrical and Computer Engineering
Grantor
University of Ontario Institute of Technology
Year dc:date.issued
2015

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Mohammed, Ola Ashour
Advisors dc:contributor.advisor
  • El-Khatib, Khalil
  • Vargas Martin, Miguel

Subjects

dc:subject × 3

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10155/529
OAI identifier oai:identifier
oai:ontariotechu.scholaris.ca:10155/529

Chain of custody

source
Harvested from
Ontario Institute of Technology
Base URL
ontariotechu.scholaris.ca/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Mohammed, Ola Ashour. Spectrum sensing based on capon power spectral density estimation. University of Ontario Institute of Technology, 2015. https://hdl.handle.net/10155/529