SNR wall for cooperative spectrum sensing using generalized energy detector

Captain, Kamal M.; Joshi, Manjunath V.

doi:10.1109/comsnets.2018.8328191

Cited by 8 publications

(13 citation statements)

References 25 publications

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“…Besides, they numerically calculate the SNRw of GED under lognormally distributed NU. Derivations of the SNRw for the GED can also be found in [18], where it is considered a CSS under additive white Gaussian noise (AWGN) channels and uniformly distributed NU in SD and HD fusion. The AND, OR, and MAJ decision fusion rules for particular numbers of SUs were considered, subsequently being generalized to any value of k and M in the k-out-of-M rule.…”

Section: A Related Researchmentioning

confidence: 99%

On the Signal-to-Noise Ratio Wall of Energy Detection in Spectrum Sensing

2022

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In this paper, a comprehensive analysis of the signal-to-noise ratio wall (SNRw) of cognitive radio (CR)based non-cooperative spectrum sensing (nCSS) and cooperative spectrum sensing (CSS) using energy detection (ED) is presented. The analysis considers a novel realistic noise uncertainty (NU) model in which it is assumed that the estimated noise variance used to determine the decision threshold is unbiased and follows a truncated-Gaussian random distribution with configurable limits. Expressions are derived for the individual detection performances at CRs and global detection performances at the fusion center in terms of probability of false alarm and probability of detection and the SNRw of ED in nCSS and CSS in hard-decision fusion under the k-out-of-M rule, and soft-decision fusion, considering the proposed NU model, respectively. Empirical SNRw algorithms are also proposed, allowing for the SNRw computation of any detector, including the ED, in nCSS and CSS. All theoretical findings are verified through computer simulations or empirical results. INDEX TERMSCongitive radio; energy detection; noise uncertainty; signal-to-noise ratio wall; spectrum sensing.

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Section: A Related Researchmentioning

confidence: 99%

On the Signal-to-Noise Ratio Wall of Energy Detection in Spectrum Sensing

2022

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“…To protect the primary signal against interference, all SUs sense the spectrum during a specific time duration(𝑇 𝑠 ). The detection threshold (𝜆) is determined in such a way that the SU can sense the occupancy of a PU under a specific zone of noise uncertainty even at low SNR [17], [18]. The uncertainty of noise is randomly distributed in a single interval as…”

Section: System Modelmentioning

confidence: 99%

“…Hence, the sensing process in such environment is considered as the first critical process in CR systems. Because of this phenomenon, there exists a so called 𝑆𝑁𝑅 𝑤𝑎𝑙𝑙 which is defined as the SNR value below which the targeted detection performance cannot be achieved regardless of the sensing time [18]. As such, the performance parameters of spectrum sensing should be chosen carefully to accommodate a targeted performance metrics and to reduce the influence of noise uncertainty.…”

Section: Proposed Schemementioning

confidence: 99%

Utilization of idle time slot in spectrum sensing under noise uncertainty

Tumar

Arar

Saleh

2022

IJECE

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<p>Spectrum sensing in cognitive radio (CR) is a critical process as it directly influences the accuracy of detection. Noise uncertainty affects the reliability of detecting vacant holes in the spectrum, thus limiting the access of that spectrum by secondary users (SUs). In such uncertain environment; SUs sense the received power of a primary user (PU) independently with different measures of signal-to-noise ratio (SNR). Long sensing time serves in mitigating the effect of noise uncertainty, but on the cost of throughput performance of CR system. In this paper, the scheme of an asynchronous and crossed sensing-reporting is presented. The scheme reduces energy consumption during sensing process without affecting the detection accuracy. Exploiting the included idle time (𝑇𝑖) in sensing time slot; each SU collects power samples with higher SNR directly performs the reporting process to a fusion center (FC) consecutively. The FC terminates the sensing and reporting processes at a specific sensing time that corresponds to the lowest SNR (𝑆𝑁𝑅𝑤𝑎𝑙𝑙). Furthermore, this integrated scheme aims at optimizing the total frame duration (𝑇𝑓). Mathematical expressions of the scheme are obtained. Analytical results show the efficiency of the scheme in terms of energy saving and throughput increment under noise uncerainty.</p>

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“…Therefore, Hu et al 44 have optimized the SE and EE separately by jointly optimizing the sensing time and decision threshold. Further, Zeng et al and Captain and Joshi 47,48 have illustrated that the adverse effect of NU can be reduced with user cooperation but it is difficult to eliminate the SNR wall with inadequate number of cooperative users. Tuan and Koo 37 selected the threshold value based on particle swarm optimization (PSO) while considering that CU is operating in full-duplex mode and have maximized the throughput under noncooperative and CSS scenario.…”

Section: Related Workmentioning

confidence: 99%

“…46 Here, γ wall represents the minimum γ level for reliable detection by the EDSS and is computed as follows: 46 where ρ is the NU parameter. Further, Zeng et al and Captain and Joshi 47,48 have illustrated that the adverse effect of NU can be reduced with user cooperation but it is difficult to eliminate the SNR wall with inadequate number of cooperative users. Moreover, Verma and Sahu 14,16 have employed the selection of threshold for AWGN channel with CFAR and CDR and have improved the throughput of CU with sufficient protection to PU from CU.…”

Section: Related Workmentioning

confidence: 99%

Intelligent threshold selection in fading environment of cognitive radio network: Advances in throughput and total error probability

Kumar

Thakur

Pandit

et al. 2019

Int J Communication

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One of the main challenges of spectrum sensing (SS) in the cognitive radio network (CRN) is selection of threshold for captivating the sensing decision, and such an issue becomes more challenging especially at low signal-to-noise ratio (SNR) because it affects the sensing performance and throughput of cognitive user (CU). However, the sensing decision of individual CU may not be accurate due to fading effects in the sensing environment, and significant improvement is accomplished with the cooperation of users. Therefore, in this paper, we have investigated the performance of energy detector SS (EDSS) technique in the CRN under fading environment with cooperative and noncooperative SS scenario. Further, we have introduced the concept of critical SNR (γ c ) to select the appropriate threshold and based on which the algorithms are proposed to enhance the throughput and reduce the total error (sensing error) probability of CU at different SNR (γ). From the results, we have accomplished that at the low SNR region (γ ≤ γ c ), throughput is maximum with constant false-alarm rate (CFAR) threshold selection approach in the cooperative scenario while in the high SNR region (γ > γ c ), its value is maximum with minimizing the error probability (MEP) threshold selection approach in the noncooperative scenario. Moreover, the sensing error is reduced with cooperation, and threshold selection with MEP approach outperforms CFAR approach for γ ≤ γ c . Further, the proposed approach outperforms the other approaches in terms of throughput and sensing error (total error) probability in the Rayleigh and Nakagami-m fading environments.

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SNR wall for cooperative spectrum sensing using generalized energy detector

Cited by 8 publications

References 25 publications

On the Signal-to-Noise Ratio Wall of Energy Detection in Spectrum Sensing

On the Signal-to-Noise Ratio Wall of Energy Detection in Spectrum Sensing

Utilization of idle time slot in spectrum sensing under noise uncertainty

Intelligent threshold selection in fading environment of cognitive radio network: Advances in throughput and total error probability

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