Threshold Design for Undersampled Autocorrelation-Based OFDM Spectrum Sensing

Farwa Ahmed; Pat Chambers; Mathini Sellathurai; Paul O’Leary; Darren F. Kavanagh

doi:10.1109/isncc66965.2025.11250393

Threshold Design for Undersampled Autocorrelation-Based OFDM Spectrum Sensing

Farwa Ahmed
, Pat Chambers
, Mathini Sellathurai
, Paul O’Leary
, Darren F. Kavanagh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

An undersampled autocorrelation-based spectrum sensing algorithm is proposed for orthogonal frequency division multiplexing (OFDM) signal detection in channels sampled below the Nyquist rate. The proposed algorithm reduces the complexity of autocorrelation-based detection by minimizing the number of unique multiplications required to compute the autocorrelation function, exploiting the inherent structure of the OFDM signal. This enables low-bandwidth cognitive radios to effectively detect wideband signals. The novelty of this work lies in a threshold design approach, which compensates for the performance loss due to undersampling and maintains reliable detection accuracy. Analytical and simulation results demonstrate that the proposed algorithm attains reasonable detection performance in the mid to high signal-to-noise ratio (SNR) scenarios, while also significantly reducing the false alarm probability under low SNR conditions.

Original language	English
Title of host publication	2025 International Symposium on Networks, Computers and Communications (ISNCC)
Publisher	IEEE
ISBN (Electronic)	9781665457682
DOIs	https://doi.org/10.1109/isncc66965.2025.11250393
Publication status	Published - 21 Nov 2025

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10.1109/isncc66965.2025.11250393

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@inproceedings{2786650030e7464b90f60bb06fddc8d4,

title = "Threshold Design for Undersampled Autocorrelation-Based OFDM Spectrum Sensing",

abstract = "An undersampled autocorrelation-based spectrum sensing algorithm is proposed for orthogonal frequency division multiplexing (OFDM) signal detection in channels sampled below the Nyquist rate. The proposed algorithm reduces the complexity of autocorrelation-based detection by minimizing the number of unique multiplications required to compute the autocorrelation function, exploiting the inherent structure of the OFDM signal. This enables low-bandwidth cognitive radios to effectively detect wideband signals. The novelty of this work lies in a threshold design approach, which compensates for the performance loss due to undersampling and maintains reliable detection accuracy. Analytical and simulation results demonstrate that the proposed algorithm attains reasonable detection performance in the mid to high signal-to-noise ratio (SNR) scenarios, while also significantly reducing the false alarm probability under low SNR conditions.",

author = "Farwa Ahmed and Pat Chambers and Mathini Sellathurai and Paul O{\textquoteright}Leary and Kavanagh, \{Darren F.\}",

year = "2025",

month = nov,

day = "21",

doi = "10.1109/isncc66965.2025.11250393",

language = "English",

booktitle = "2025 International Symposium on Networks, Computers and Communications (ISNCC)",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - Threshold Design for Undersampled Autocorrelation-Based OFDM Spectrum Sensing

AU - Ahmed, Farwa

AU - Chambers, Pat

AU - Sellathurai, Mathini

AU - O’Leary, Paul

AU - Kavanagh, Darren F.

PY - 2025/11/21

Y1 - 2025/11/21

N2 - An undersampled autocorrelation-based spectrum sensing algorithm is proposed for orthogonal frequency division multiplexing (OFDM) signal detection in channels sampled below the Nyquist rate. The proposed algorithm reduces the complexity of autocorrelation-based detection by minimizing the number of unique multiplications required to compute the autocorrelation function, exploiting the inherent structure of the OFDM signal. This enables low-bandwidth cognitive radios to effectively detect wideband signals. The novelty of this work lies in a threshold design approach, which compensates for the performance loss due to undersampling and maintains reliable detection accuracy. Analytical and simulation results demonstrate that the proposed algorithm attains reasonable detection performance in the mid to high signal-to-noise ratio (SNR) scenarios, while also significantly reducing the false alarm probability under low SNR conditions.

AB - An undersampled autocorrelation-based spectrum sensing algorithm is proposed for orthogonal frequency division multiplexing (OFDM) signal detection in channels sampled below the Nyquist rate. The proposed algorithm reduces the complexity of autocorrelation-based detection by minimizing the number of unique multiplications required to compute the autocorrelation function, exploiting the inherent structure of the OFDM signal. This enables low-bandwidth cognitive radios to effectively detect wideband signals. The novelty of this work lies in a threshold design approach, which compensates for the performance loss due to undersampling and maintains reliable detection accuracy. Analytical and simulation results demonstrate that the proposed algorithm attains reasonable detection performance in the mid to high signal-to-noise ratio (SNR) scenarios, while also significantly reducing the false alarm probability under low SNR conditions.

U2 - 10.1109/isncc66965.2025.11250393

DO - 10.1109/isncc66965.2025.11250393

M3 - Conference contribution

BT - 2025 International Symposium on Networks, Computers and Communications (ISNCC)

PB - IEEE

ER -

Threshold Design for Undersampled Autocorrelation-Based OFDM Spectrum Sensing

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