Smart Spectrum Detection for Next-Generation Wireless Systems: Optimizing Wireless Networks Through Smart Spectrum Sensing - Tapa blanda
Sinopsis
Spectrum sensing plays a vital role in cognitive radio–based wireless communication, as enhanced sensing improves overall network performance. Traditional signal analysis methods like Fast Fourier Transform (FFT) and Wavelet Transform are widely used but have limitations. FFT requires large data samples and high processing time, while Wavelet analysis, though efficient in both time and frequency domains, also demands substantial computation. To address these issues, this study explores the Empirical Mode Decomposition (EMD) technique for spectrum sensing in IEEE 802.22 Wireless Regional Area Networks (WRAN). The performance of FFT, Wavelet, and EMD-based methods is compared in terms of detection probability, false alarm probability, signal-to-noise ratio, and bit error rate. Simulation and experimental results show that EMD significantly enhances spectrum sensing accuracy and efficiency. Thus, implementing EMD in WRAN improves cognitive radio spectrum detection compared to FFT and Wavelet techniques.
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Smart Spectrum Detection for Next-Generation Wireless Systems
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209264190
ISBN 13: 9786209264191
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Smart Spectrum Detection for Next-Generation Wireless Systems
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ISBN 10: 6209264190
ISBN 13: 9786209264191
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Smart Spectrum Detection for Next-Generation Wireless Systems: Optimizing Wireless Networks Through Smart Spectrum Sensing
Publicado por
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209264190
ISBN 13: 9786209264191
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Smart Spectrum Detection for Next-Generation Wireless Systems (Paperback)
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6209264190
ISBN 13: 9786209264191
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Paperback. Condición: new. Paperback. Spectrum sensing plays a vital role in cognitive radio-based wireless communication, as enhanced sensing improves overall network performance. Traditional signal analysis methods like Fast Fourier Transform (FFT) and Wavelet Transform are widely used but have limitations. FFT requires large data samples and high processing time, while Wavelet analysis, though efficient in both time and frequency domains, also demands substantial computation. To address these issues, this study explores the Empirical Mode Decomposition (EMD) technique for spectrum sensing in IEEE 802.22 Wireless Regional Area Networks (WRAN). The performance of FFT, Wavelet, and EMD-based methods is compared in terms of detection probability, false alarm probability, signal-to-noise ratio, and bit error rate. Simulation and experimental results show that EMD significantly enhances spectrum sensing accuracy and efficiency. Thus, implementing EMD in WRAN improves cognitive radio spectrum detection compared to FFT and Wavelet techniques. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9786209264191
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Smart Spectrum Detection for Next-Generation Wireless Systems
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ISBN 10: 6209264190
ISBN 13: 9786209264191
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Smart Spectrum Detection for Next-Generation Wireless Systems (Paperback)
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6209264190
ISBN 13: 9786209264191
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Paperback. Condición: new. Paperback. Spectrum sensing plays a vital role in cognitive radio-based wireless communication, as enhanced sensing improves overall network performance. Traditional signal analysis methods like Fast Fourier Transform (FFT) and Wavelet Transform are widely used but have limitations. FFT requires large data samples and high processing time, while Wavelet analysis, though efficient in both time and frequency domains, also demands substantial computation. To address these issues, this study explores the Empirical Mode Decomposition (EMD) technique for spectrum sensing in IEEE 802.22 Wireless Regional Area Networks (WRAN). The performance of FFT, Wavelet, and EMD-based methods is compared in terms of detection probability, false alarm probability, signal-to-noise ratio, and bit error rate. Simulation and experimental results show that EMD significantly enhances spectrum sensing accuracy and efficiency. Thus, implementing EMD in WRAN improves cognitive radio spectrum detection compared to FFT and Wavelet techniques. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9786209264191
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Smart Spectrum Detection for Next-Generation Wireless Systems (Paperback)
Publicado por
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209264190
ISBN 13: 9786209264191
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Paperback. Condición: new. Paperback. Spectrum sensing plays a vital role in cognitive radio-based wireless communication, as enhanced sensing improves overall network performance. Traditional signal analysis methods like Fast Fourier Transform (FFT) and Wavelet Transform are widely used but have limitations. FFT requires large data samples and high processing time, while Wavelet analysis, though efficient in both time and frequency domains, also demands substantial computation. To address these issues, this study explores the Empirical Mode Decomposition (EMD) technique for spectrum sensing in IEEE 802.22 Wireless Regional Area Networks (WRAN). The performance of FFT, Wavelet, and EMD-based methods is compared in terms of detection probability, false alarm probability, signal-to-noise ratio, and bit error rate. Simulation and experimental results show that EMD significantly enhances spectrum sensing accuracy and efficiency. Thus, implementing EMD in WRAN improves cognitive radio spectrum detection compared to FFT and Wavelet techniques. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9786209264191
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Smart Spectrum Detection for Next-Generation Wireless Systems | Optimizing Wireless Networks Through Smart Spectrum Sensing
Publicado por
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209264190
ISBN 13: 9786209264191
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Taschenbuch. Condición: Neu. Smart Spectrum Detection for Next-Generation Wireless Systems | Optimizing Wireless Networks Through Smart Spectrum Sensing | Rahul Koshti | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786209264191 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Nº de ref. del artículo: 134443025
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Smart Spectrum Detection for Next-Generation Wireless Systems
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LAP LAMBERT Academic Publishing Dez 2025, 2025
ISBN 10: 6209264190
ISBN 13: 9786209264191
Nuevo
Taschenbuch
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Spectrum sensing plays a vital role in cognitive radio-based wireless communication, as enhanced sensing improves overall network performance. Traditional signal analysis methods like Fast Fourier Transform (FFT) and Wavelet Transform are widely used but have limitations. FFT requires large data samples and high processing time, while Wavelet analysis, though efficient in both time and frequency domains, also demands substantial computation. To address these issues, this study explores the Empirical Mode Decomposition (EMD) technique for spectrum sensing in IEEE 802.22 Wireless Regional Area Networks (WRAN). The performance of FFT, Wavelet, and EMD-based methods is compared in terms of detection probability, false alarm probability, signal-to-noise ratio, and bit error rate. Simulation and experimental results show that EMD significantly enhances spectrum sensing accuracy and efficiency. Thus, implementing EMD in WRAN improves cognitive radio spectrum detection compared to FFT and Wavelet techniques.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch. Nº de ref. del artículo: 9786209264191
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Smart Spectrum Detection for Next-Generation Wireless Systems : Optimizing Wireless Networks Through Smart Spectrum Sensing
Publicado por
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209264190
ISBN 13: 9786209264191
Nuevo
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