ws 20(13): e6

Research Article

Energy Detection Based Spectrum Sensing for Rural Area Networks

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  • @ARTICLE{10.4108/eai.7-4-2020.163923,
        author={Johanna Vartiainen and Heikki Karvonen and Marja Matinmikko-Blue and Luciano Mendes and Harri Saarnisaari and Alexandre Matos},
        title={Energy Detection Based Spectrum Sensing for Rural Area Networks},
        journal={EAI Endorsed Transactions on Wireless Spectrum},
        keywords={signal detection, spectrum utilization, 5G system, overlapping, energy detector},
  • Johanna Vartiainen
    Heikki Karvonen
    Marja Matinmikko-Blue
    Luciano Mendes
    Harri Saarnisaari
    Alexandre Matos
    Year: 2020
    Energy Detection Based Spectrum Sensing for Rural Area Networks
    DOI: 10.4108/eai.7-4-2020.163923
Johanna Vartiainen1,*, Heikki Karvonen1, Marja Matinmikko-Blue1, Luciano Mendes2, Harri Saarnisaari1, Alexandre Matos3
  • 1: Centre for Wireless Communications, University of Oulu, Finland
  • 2: Radiocommunications Research Center, Inatel, Brazil
  • 3: Federal University of Cear√°, Brazil
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Remote and rural areas are a challenge to deploy cost-efficient connectivity solutions. 5G technology needs lower frequencies, which calls for spectrum sharing for local networks. Spectrum sensing could complement traditional database approach for spectrum sharing in these areas. This paper studies a windowing based (WIBA) blind spectrum sensing method and compares its performance to a localization algorithm based on double-thresholding (LAD). Both methods are based on energy detection and can be used in any band for detecting rather narrowband signals. Probabilities of detection and false alarm, relative mean square error, number of detected signals and detection distances were evaluated in multipath, multi-signal and rural area channel conditions. The simulation results show tha the WIBA method is suitable for 5G remote areas, due to its good detection performance in low signal-to-noise ratios (SNR) with low complexity. Results also show importance of the detection window selection.