Next Article in Journal
An Ultrafast Active Quenching Active Reset Circuit with 50% SPAD Afterpulsing Reduction in a 28 nm FD-SOI CMOS Technology Using Body Biasing Technique
Next Article in Special Issue
ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs
Previous Article in Journal
Lack of Thermogram Sharpness as Component of Thermographic Temperature Measurement Uncertainty Budget
Previous Article in Special Issue
Performance Analysis of Underlay Cognitive Radio System with Self-Sustainable Relay and Statistical CSI
Article

Spectrum Occupancy Model Based on Empirical Data for FM Radio Broadcasting in Suburban Environments

1
Center of Excellence in Sustainable Disaster Management, Department of Electrical Engineering, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80161, Thailand
2
OASYS Research Group, Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
3
Department of Electrical Engineering, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80161, Thailand
*
Authors to whom correspondence should be addressed.
Academic Editor: Ingrid Moerman
Sensors 2021, 21(12), 4015; https://doi.org/10.3390/s21124015
Received: 10 May 2021 / Revised: 2 June 2021 / Accepted: 8 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)
It is well-known that the analog FM radio channels in suburban areas are underutilized. Before reallocating the unused channels for other applications, a regulator must analyze the spectrum occupancy. Many researchers proposed the spectrum occupancy models to find vacant spectrum. However, the existing models do not analyze each channel individually. This paper proposes an approach consisting (i) a spectrum measurement strategy, (ii) an appropriate decision threshold, and (iii) criteria for channel classification, to find the unused channels. The measurement strategy monitors each channel’s activity by capturing the power levels of the passband and the guardband separately. The decision threshold is selected depending on the monitored channel’s activity. The criteria classifies the channels based on the passband’s and guardband’s duty cycles. The results show that the proposed channel classification can identify 42 unused channels. If the power levels of wholebands (existing model) were analyzed instead of passband’s and guardband’s duty cycles, only 24 unoccupied channels were found. Furthermore, we propose the interference criteria, based on relative duty cycles across channels, to classify the abnormally used channels into interference sources and interference sinks, which have 16 and 15 channels, respectively. This information helps the dynamic spectrum sharing avoid or mitigate the interferences. View Full-Text
Keywords: spectrum measurement; spectrum sharing; public safety band; cognitive radio; dynamic spectrum access spectrum measurement; spectrum sharing; public safety band; cognitive radio; dynamic spectrum access
Show Figures

Graphical abstract

MDPI and ACS Style

Chantaveerod, A.; Woradit, K.; Pochaiya, C. Spectrum Occupancy Model Based on Empirical Data for FM Radio Broadcasting in Suburban Environments. Sensors 2021, 21, 4015. https://doi.org/10.3390/s21124015

AMA Style

Chantaveerod A, Woradit K, Pochaiya C. Spectrum Occupancy Model Based on Empirical Data for FM Radio Broadcasting in Suburban Environments. Sensors. 2021; 21(12):4015. https://doi.org/10.3390/s21124015

Chicago/Turabian Style

Chantaveerod, Ajalawit, Kampol Woradit, and Charernkiat Pochaiya. 2021. "Spectrum Occupancy Model Based on Empirical Data for FM Radio Broadcasting in Suburban Environments" Sensors 21, no. 12: 4015. https://doi.org/10.3390/s21124015

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop