Development of signal processing and machine learning methods for spectrum sensing using autocorrelation features
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| Publicado en: | SN Applied Sciences vol. 7, no. 11 (Nov 2025), p. 1237 |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2523-3963 | ||
| 022 | |a 2523-3971 | ||
| 024 | 7 | |a 10.1007/s42452-025-07787-4 |2 doi | |
| 035 | |a 3262622006 | ||
| 045 | 2 | |b d20251101 |b d20251130 | |
| 100 | 1 | |a Sesham, Srinu |u University of Namibia, Department of Electrical and Computer Engineering, Ongwediva, Namibia (GRID:grid.10598.35) (ISNI:0000 0001 1014 6159) | |
| 245 | 1 | |a Development of signal processing and machine learning methods for spectrum sensing using autocorrelation features | |
| 260 | |b Springer Nature B.V. |c Nov 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The rapid expansion of 5G networks and Internet-connected wireless devices (such as IoT) has led to intensified spectrum congestion in the Fifth Generation New Radio Frequency Range 1 (5G NR FR1). Efficient spectrum utilization through effective spectrum-sharing solutions is crucial for seamless 5G and Next-Generation (Next-Gen) wireless networks. The statistical/signal processing based sensing methods that allow new wireless devices for spectrum sharing are facing challenges such as uncertain thresholds and degraded performance under high noise levels relative to the signal. Alternatively, machine and deep learning-based spectrum sensing models demonstrate better performance which is independent of detection threshold, but requires large datasets for model training. This paper investigates the applications of frequency-domain auto-correlation coefficients to develop novel sensing methods, namely Auto-Correlation Integral-based Sensing (ACIS) and Logistic Regression Model-based Sensing (LRMS). The work also compares their detection performance and computational complexity against other prominent techniques in the literature. Results and analysis show that ACIS achieves the recommended detector performance <inline-graphic specific-use="web" mime-subtype="GIF" xlink:href="42452_2025_7787_Article_IEq1.gif" /> 90% and <inline-graphic specific-use="web" mime-subtype="GIF" xlink:href="42452_2025_7787_Article_IEq2.gif" /> 10%) at a very low signal-to-noise ratio (SNR) value of − 18 dB, using a correlation vector size (N) of 512 with a model complexity of O(NlogN). Whereas LRMS shows superior performance and can detect − 30 dB signals using a correlation vector size of 512 and a model complexity of O(N). The proposed methods outperform most existing signal processing and machine learning-based detectors in the literature.Article highlights<list list-type="bullet"><list-item></list-item>New methods (ACIS & LRMS) help detect weak wireless signals even in very noisy environments.<list-item>LRMS reliably detects signals as low as − 30 dB, using simpler computations than many existing tools.</list-item><list-item>These advances support better sharing of crowded 5G frequencies, helping future wireless networks.</list-item> | |
| 653 | |a Wireless networks | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Internet of Things | ||
| 653 | |a Hypothesis testing | ||
| 653 | |a Regression analysis | ||
| 653 | |a Communication | ||
| 653 | |a 5G mobile communication | ||
| 653 | |a Regression models | ||
| 653 | |a Signal processing | ||
| 653 | |a Frequency ranges | ||
| 653 | |a Noise levels | ||
| 653 | |a Machine learning | ||
| 653 | |a Energy | ||
| 653 | |a Spectrum allocation | ||
| 653 | |a Autocorrelation | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Correlation coefficients | ||
| 653 | |a Correlation coefficient | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Sensors | ||
| 653 | |a Support vector machines | ||
| 653 | |a Methods | ||
| 653 | |a Eigenvalues | ||
| 653 | |a Performance degradation | ||
| 653 | |a Complexity | ||
| 653 | |a Signal to noise ratio | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Suresh, Nalina |u University of Namibia, Department of Computer and Mathematical Sciences, Windhoek, Namibia (GRID:grid.10598.35) (ISNI:0000 0001 1014 6159) | |
| 700 | 1 | |a Chembe, Dickson Kanungwe |u University of Namibia, Department of Electrical and Computer Engineering, Ongwediva, Namibia (GRID:grid.10598.35) (ISNI:0000 0001 1014 6159) | |
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 11 (Nov 2025), p. 1237 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3262622006/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3262622006/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3262622006/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |