Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques
The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new tec...
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Taylor & Francis Group
2025-04-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2460879 |
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| author | Arun Kumar Aziz Nanthaamornphong |
| author_facet | Arun Kumar Aziz Nanthaamornphong |
| author_sort | Arun Kumar |
| collection | DOAJ |
| description | The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new technologies require faster data rates and reduced latency. This study explores hybrid spectrum sensing techniques, combining matched filter (MF) energy detection (ED) and an equal-gain combining-based energy detection Neyman-Pearson threshold estimation technique (EGC-ED-PTh) to enhance waveform detection accuracy in complex environments. The proposed method offers an enhanced signal-to-noise ratio (SNR) by optimizing the detection performance, particularly in low-SNR environments, thereby improving the signal reliability. The proposed algorithms are evaluated in comparison with traditional SS methods, including ED, MF, and cyclostationary feature detection (CFD). Additionally, characteristics including bit error rate (BER), power spectral density (PSD), probability of detection (pd), and probability of false alarm (pfa) were researched and evaluated for 500 and 1000 samples. The simulation findings show that the projected algorithms perform better than the traditional algorithms with minimum sidelobes of – 3024 and pfa effects and achieve a throughput gain of 5 and 4.7 dB compared with the conventional algorithms. |
| format | Article |
| id | doaj-art-383910c3769545daa6d3b965cc5bc9d2 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-383910c3769545daa6d3b965cc5bc9d22025-02-03T03:56:27ZengTaylor & Francis GroupAutomatika0005-11441848-33802025-04-0166213315310.1080/00051144.2025.2460879Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniquesArun Kumar0Aziz Nanthaamornphong1Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, IndiaCollege of Computing, Prince of Songkla University, Phuket, ThailandThe rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new technologies require faster data rates and reduced latency. This study explores hybrid spectrum sensing techniques, combining matched filter (MF) energy detection (ED) and an equal-gain combining-based energy detection Neyman-Pearson threshold estimation technique (EGC-ED-PTh) to enhance waveform detection accuracy in complex environments. The proposed method offers an enhanced signal-to-noise ratio (SNR) by optimizing the detection performance, particularly in low-SNR environments, thereby improving the signal reliability. The proposed algorithms are evaluated in comparison with traditional SS methods, including ED, MF, and cyclostationary feature detection (CFD). Additionally, characteristics including bit error rate (BER), power spectral density (PSD), probability of detection (pd), and probability of false alarm (pfa) were researched and evaluated for 500 and 1000 samples. The simulation findings show that the projected algorithms perform better than the traditional algorithms with minimum sidelobes of – 3024 and pfa effects and achieve a throughput gain of 5 and 4.7 dB compared with the conventional algorithms.https://www.tandfonline.com/doi/10.1080/00051144.2025.2460879Spectrum sensingbeyond 5Gequal-gain combininghybrid algorithmspfaPd |
| spellingShingle | Arun Kumar Aziz Nanthaamornphong Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques Automatika Spectrum sensing beyond 5G equal-gain combining hybrid algorithms pfa Pd |
| title | Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques |
| title_full | Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques |
| title_fullStr | Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques |
| title_full_unstemmed | Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques |
| title_short | Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques |
| title_sort | analysis of 6g and b5g waveforms using hybrid mf ed and ecg ed spectrum sensing techniques |
| topic | Spectrum sensing beyond 5G equal-gain combining hybrid algorithms pfa Pd |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2025.2460879 |
| work_keys_str_mv | AT arunkumar analysisof6gandb5gwaveformsusinghybridmfedandecgedspectrumsensingtechniques AT aziznanthaamornphong analysisof6gandb5gwaveformsusinghybridmfedandecgedspectrumsensingtechniques |