Intelligent selection of threshold in covariance-based spectrum sensing for cognitive radio networks

Charan, Chhagan; Pandey, Rajoo

doi:10.1007/s11276-017-1533-y

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…The wideband sensing methods focus on analysing the number of frequencies at a time whereas narrowband methods focus on analysing one frequency channel at a time. several narrowband spectrum sensing methods have been introduced such as energy detection [5,6], matched filter detection [7], cyclostationary feature detection [8,9], covariance based detection [10,11], and machine learning based sensing approach [12], etc. The energy detection method relies on power of incoming signal and compares it to previously estimated threshold to estimate the presence of PUs.…”

Section: Introductionmentioning

confidence: 99%

DRLNet: A Deep Reinforcement Learning Network for Hybrid Features Extraction and Spectrum Sensing in Cognitive Radio Networks

M A,

C R

2023

JAIT

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Over the past two decades, communication technologies have advanced significantly, but the growing use of various communication methods has led to a shortage of available spectrum. Cognitive Radio (CR) emerges as a solution to this challenge. A crucial aspect of CR is spectrum sensing, which detects available spectrum gaps. However, current spectrum sensing methods have limitations, including insufficient signal representation, inefficiency, and sensitivity to noise. To address these issues, this study embraces a deep learning approach and introduces an innovative spectrum detection architecture for cognitive radio networks. The method combines deep learning and reinforcement learning, leveraging deep learning for energy and energy correlation feature extraction. Additionally, a Recurrent Neural Network (RNN) module is used to capture time-shifted signal correlation. To enhance feature extraction, Short-Time Fourier Transform (STFT) feature extraction is incorporated. The combined feature vector is processed through a reinforcement learning module. Finally, these features are used to train the deep learning classifier which uses residual blocks for better representation of feature while learning. The highest prediction score is considered as the decision threshold in this work. The outcome of this work is compared with other deep learning methods in terms of 𝑷 𝒅 , 𝑷 𝒇 and sensing error for varied sample size and modulation schemes. The comparative analysis shows the robustness of proposed approach by achieving the 𝑷 𝒇 as 0.32 and 0.06 for QAM16 and QPSK modulations.

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Section: Introductionmentioning

confidence: 99%

DRLNet: A Deep Reinforcement Learning Network for Hybrid Features Extraction and Spectrum Sensing in Cognitive Radio Networks

M A,

C R

2023

JAIT

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“…22 In the cooperative spectrum sensing (CSS) scenario, the sensing results of each CU are sent to the fusion center (FC), where FC applies different fusion rules (OR, AND, MAJORITY, and K out of M) to take final global decision about the status of sensed channel. Several researchers 14,16,30 have also explored the selection of threshold to enhance the throughput or reduce the sensing error of CU; however, they have emphasized only on the AWGN channel in noncooperative scenario. In the double thresholding approach, each CU employs two thresholds, λ 1 and λ 2 (λ 2 > λ 1 ), and when the energy of test statistics of received signal (T(x)) at each CU is ≥λ 2 (/ ≤ λ 1 ), the decision is in favor of PU presence (\absence) on the channel.…”

Section: Introductionmentioning

confidence: 99%

“…Further, they have illustrated that the majority rule provides least sensing error when the false alarm and misdetection have nearly the same probabilities; however, the throughput analysis was missing. Several researchers 14,16,30 have also explored the selection of threshold to enhance the throughput or reduce the sensing error of CU; however, they have emphasized only on the AWGN channel in noncooperative scenario. Verma and Sahu 16 have employed simultaneously both CFAR and CDR threshold selection approaches on AWGN channel in the noncooperative environment to maximize the throughput.…”

Section: Introductionmentioning

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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“…This approach also improved the energy detection performance and hence the spectrum sensing in cognitive radio. An adaptive threshold approach for the detection of spectrum under channel interference is outlined in [6]. This work presented a covariance approach for channel sensing where an adaptive threshold is proposed to reduce the error probability in spectrum sensing.…”

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confidence: 99%

Resource Allocation in Spectrum Deployment for Cognitive Third-party Users

Lakshmi¹,

Swamy²,

Prasad³

2021

IJACSA

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Spectrum scarcity is a major challenge in wireless communication for next generation applications. The spectrum sharing and utilization of other user available spectrum is an optimal solution, which has outcome with a new mode of communication called cognitive network. Cognitive devices are capable of requesting and sharing free spectrum among each other in a communication range. The spectrums are shared among primary and secondary user (PU, SU). To extend the range of spectrum utilization, users from other clusters are requested in sharing of spectrum which is called as third party user (TSU). In detection of free spectrum a jamming based approach is proposed in recent work. Jamming approach generates a periodic jamming signal for TSU in sensing of free spectrum. The repetitive requesting of spectrum availability result in large jamming probability for engaged TSU users resulting in large delay. In minimizing the delay observed, in this paper, a new monitored jamming approach is proposed. Proposed Monitored jamming approach is develop in recent to the engagement of each TSU for communication and governing the jamming signal based on the spectrum engagement. The proposed approach minimizes the delay due to jamming signaling in existing system. The Experimental results obtained shows an enhancement to the system throughput, fairness factor and minimizes the delay metric for proposed approach.

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Intelligent selection of threshold in covariance-based spectrum sensing for cognitive radio networks

Cited by 13 publications

References 29 publications

DRLNet: A Deep Reinforcement Learning Network for Hybrid Features Extraction and Spectrum Sensing in Cognitive Radio Networks

DRLNet: A Deep Reinforcement Learning Network for Hybrid Features Extraction and Spectrum Sensing in Cognitive Radio Networks

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

Resource Allocation in Spectrum Deployment for Cognitive Third-party Users

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