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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Rebeccah Oluwafunmilayo Omowaiye
Ladoke Akintola University of Technology, Ogbomoso
Computer Science & IT Electrical & Electronics Engineering

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Autocorrelation Based White Space Detection in Energy Harvesting Cognitive Radio Network Samson Iyanda Ojo; Zachaeus Kayode Adeyemo; Rebeccah Oluwafunmilayo Omowaiye; Oluwatobi Omolola Oyedokun
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3179

Abstract

Accurate detection of White Space (WS) is of paramount importance in a Cognitive Radio Network (CRN) to prevent authorized users from harmful interference. However, channel impairment such as multipath fading and shadowing affects accurate detection of WS resulting in interference. The Existing Feature Detection (EFD) technique used to address the problem is faced with computational complexity and synchronization resulting in long sensing time, bandwidth inefficiency, energy constrain and poor detection rate. Hence, this paper proposes autocorrelation based multiple antenna with energy harvesting for WS detection in a CRN using Radio Frequency (RF) energy harvesting and autocorrelation of the received signal with a modified Equal Gain Combiner (mEGC). Antenna Switching (AS) RF energy harvesting with mEGC are used to harvest energy and information from the received PU signal in a multiple antenna configuration. Autocorrelation is then obtained and compared with the set threshold of zero to determine the presence or absence of WS. The proposed technique is evaluated using Spectral Efficiency (SE), Probability of Detection (PD) and Sensing Time (ST) by comparing with EFD technique. The results obtained revealed that the proposed technique shows better performance than EFD.
Co-Authors Oluwatobi Omolola Oyedokun Samson Iyanda Ojo Zachaeus Kayode Adeyemo