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SNR Wall Effect Alleviation by Generalized Detector Employed in Cognitive Radio Networks.

Shbat MS, Tuzlukov V - Sensors (Basel) (2015)

Bottom Line: The most commonly used spectrum sensing techniques in cognitive radio (CR) networks, such as the energy detector (ED), matched filter (MF), and others, suffer from the noise uncertainty and signal-to-noise ratio (SNR) wall phenomenon.These detectors cannot achieve the required signal detection performance regardless of the sensing time.The simulation results confirm our theoretical issues and effectiveness of GD implementation in CR networks based on antenna array.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea. modboss80@gmail.com.

ABSTRACT
The most commonly used spectrum sensing techniques in cognitive radio (CR) networks, such as the energy detector (ED), matched filter (MF), and others, suffer from the noise uncertainty and signal-to-noise ratio (SNR) wall phenomenon. These detectors cannot achieve the required signal detection performance regardless of the sensing time. In this paper, we explore a signal processing scheme, namely, the generalized detector (GD) constructed based on the generalized approach to signal processing (GASP) in noise, in spectrum sensing of CR network based on antenna array with the purpose to alleviate the SNR wall problem and improve the signal detection robustness under the low SNR. The simulation results confirm our theoretical issues and effectiveness of GD implementation in CR networks based on antenna array.

No MeSH data available.


Related in: MedlinePlus

The probabilities of error andas a function of the normalized threshold,.
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sensors-15-16105-f009: The probabilities of error andas a function of the normalized threshold,.

Mentions: Comparison between the probability of errorfor the ED and GD as a function of the normalized optimal detection threshold, whereis the normalization factor, is presented in Figure 9. The probability of erroris evaluated for both detectors in two cases: there is the noise power uncertainty and there is no noise power uncertainty at thedB,,,anddB. As shown in Figure 9, the GD can achieve the lower probability of errorin comparison with the ED for both cases. For example, if there is no noise power uncertainty the minimal probability of erroris equal to 0.13 in the GD case and 0.25 in the ED case. If there is the noise power uncertainty withdB, the lowest probability of errorfor the GD is equal to 0.24 and 0.33 for the ED. In a general case, the noise power uncertainty affects negatively on the ED and GD probability of error. Thus, we can make the following conclusion: increasing in the noise power uncertainty leads to increasing in the probability of error.


SNR Wall Effect Alleviation by Generalized Detector Employed in Cognitive Radio Networks.

Shbat MS, Tuzlukov V - Sensors (Basel) (2015)

The probabilities of error andas a function of the normalized threshold,.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541870&req=5

sensors-15-16105-f009: The probabilities of error andas a function of the normalized threshold,.
Mentions: Comparison between the probability of errorfor the ED and GD as a function of the normalized optimal detection threshold, whereis the normalization factor, is presented in Figure 9. The probability of erroris evaluated for both detectors in two cases: there is the noise power uncertainty and there is no noise power uncertainty at thedB,,,anddB. As shown in Figure 9, the GD can achieve the lower probability of errorin comparison with the ED for both cases. For example, if there is no noise power uncertainty the minimal probability of erroris equal to 0.13 in the GD case and 0.25 in the ED case. If there is the noise power uncertainty withdB, the lowest probability of errorfor the GD is equal to 0.24 and 0.33 for the ED. In a general case, the noise power uncertainty affects negatively on the ED and GD probability of error. Thus, we can make the following conclusion: increasing in the noise power uncertainty leads to increasing in the probability of error.

Bottom Line: The most commonly used spectrum sensing techniques in cognitive radio (CR) networks, such as the energy detector (ED), matched filter (MF), and others, suffer from the noise uncertainty and signal-to-noise ratio (SNR) wall phenomenon.These detectors cannot achieve the required signal detection performance regardless of the sensing time.The simulation results confirm our theoretical issues and effectiveness of GD implementation in CR networks based on antenna array.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea. modboss80@gmail.com.

ABSTRACT
The most commonly used spectrum sensing techniques in cognitive radio (CR) networks, such as the energy detector (ED), matched filter (MF), and others, suffer from the noise uncertainty and signal-to-noise ratio (SNR) wall phenomenon. These detectors cannot achieve the required signal detection performance regardless of the sensing time. In this paper, we explore a signal processing scheme, namely, the generalized detector (GD) constructed based on the generalized approach to signal processing (GASP) in noise, in spectrum sensing of CR network based on antenna array with the purpose to alleviate the SNR wall problem and improve the signal detection robustness under the low SNR. The simulation results confirm our theoretical issues and effectiveness of GD implementation in CR networks based on antenna array.

No MeSH data available.


Related in: MedlinePlus