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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.


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CR systems and SU with M antenna array elements.
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sensors-15-16105-f001: CR systems and SU with M antenna array elements.

Mentions: The main aim of the cognitive radio (CR) network is to improve the spectrum utilization efficiency, by introducing an opportunistic use of unemployed frequency band by the primary user (PU) (see Figure 1). The spectrum sensing is needed to define the frequency holes that could be allocated for the secondary user (SU). The spectrum sensors search continuously an availability of frequency holes and assign them to SU without causing harmful interference to the PU. Fundamental limitations in practice are involved in spectrum sensing process [1,2,3]. The sensitivity to noise power uncertainty, for example, variations in the noise variance as a function of real time, is one of the most common and serious problems among the well-known spectrum sensors such as the energy detector (ED), matched filter (MF), and even the cyclostationary detector under some conditions at the low signal-to-noise ratio (SNR) [4]. The impact of noise power uncertainty is quantified by SNR wall location, i.e., if the SNR value is less than the SNR wall, the PU signal detector will fail to achieve the desired performance and maintain a robustness against power noise uncertainty independently of how long the sensing time is [3,4,5]. Both theoretical and experimental analysis confirmed the SNR wall phenomenon existence under the noise power uncertainty conditions. This phenomenon negatively effects the receiver operation characteristic (ROC). Other uncertainties also can be considered as SNR wall generators, for example, the noise power estimation error, assumptions made under the white and stationary noise, fading process, shadowing, non-ideal filters, non-precise analog-to-digital (A/D) converters, quantization noise, aliasing effect caused by imperfect front-end filters, and interference between the PU and SU. An alternative presentation for the SNR wall is given by the number of samples N as a function of SNR, the probability of false alarmand probability of miss, i.e.,. The PU signal detector should minimize the number of samples N required to achieve the desired detection performance. The lowest SNR satisfying the probability of false alarmand the probability of missconstraints is called the detector sensitivity [3].


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

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

CR systems and SU with M antenna array elements.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16105-f001: CR systems and SU with M antenna array elements.
Mentions: The main aim of the cognitive radio (CR) network is to improve the spectrum utilization efficiency, by introducing an opportunistic use of unemployed frequency band by the primary user (PU) (see Figure 1). The spectrum sensing is needed to define the frequency holes that could be allocated for the secondary user (SU). The spectrum sensors search continuously an availability of frequency holes and assign them to SU without causing harmful interference to the PU. Fundamental limitations in practice are involved in spectrum sensing process [1,2,3]. The sensitivity to noise power uncertainty, for example, variations in the noise variance as a function of real time, is one of the most common and serious problems among the well-known spectrum sensors such as the energy detector (ED), matched filter (MF), and even the cyclostationary detector under some conditions at the low signal-to-noise ratio (SNR) [4]. The impact of noise power uncertainty is quantified by SNR wall location, i.e., if the SNR value is less than the SNR wall, the PU signal detector will fail to achieve the desired performance and maintain a robustness against power noise uncertainty independently of how long the sensing time is [3,4,5]. Both theoretical and experimental analysis confirmed the SNR wall phenomenon existence under the noise power uncertainty conditions. This phenomenon negatively effects the receiver operation characteristic (ROC). Other uncertainties also can be considered as SNR wall generators, for example, the noise power estimation error, assumptions made under the white and stationary noise, fading process, shadowing, non-ideal filters, non-precise analog-to-digital (A/D) converters, quantization noise, aliasing effect caused by imperfect front-end filters, and interference between the PU and SU. An alternative presentation for the SNR wall is given by the number of samples N as a function of SNR, the probability of false alarmand probability of miss, i.e.,. The PU signal detector should minimize the number of samples N required to achieve the desired detection performance. The lowest SNR satisfying the probability of false alarmand the probability of missconstraints is called the detector sensitivity [3].

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