A rapid coarse-grained blind wideband spectrum sensing method for cognitive radio networks

Feng, Peng; Bai, Yuebin; Gu, Yuhao; Huang, Jun; Wang, Xiaolin; Liu, Chang

doi:10.1016/j.comcom.2020.11.015

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…This pattern makes enable and then use a compressive sample model to resolve channel estimation issue in a MIMO system. Results display that the present channel estimation exceeds the regular CS‐mainly based on the channel estimation design in respect of the NMSE show at elevated SNRs 7 . Moreover, it decreases the computational complexity of the estimation channel compared to conventional techniques.…”

Section: Related Workmentioning

confidence: 82%

“…Results display that the present channel estimation exceeds the regular CS-mainly based on the channel estimation design in respect of the NMSE show at elevated SNRs. 7 Moreover, it decreases the computational complexity of the estimation channel compared to conventional techniques. Additionally, the given technique significantly decreases overhead pilot is differentiated to channel estimation model with NMSE performance gain.…”

Section: Related Workmentioning

confidence: 99%

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A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

Perumal¹,

Nagarajan²

2022

Int J Communication

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Summary In recent times, evolution of communication technology and standard has grown in leaps and bounds from a conventional 1G communication technology towards the recent 5G and 6G technologies in a very short span of time. However, due to increasing scarcity of spectrum for these devices, cognitive radio networks (CRNs) have emerged to be promising solutions to allocate the required spectrum to the users in an intelligent manner. The method of compressive sensing‐based cyclo‐stationary feaure detection method is implemented based on a powerful CNN classifier to detect the presence or absence of PU activity. Detection probability and MSE have been improved, and the optimal detection has been reformulated for minimizing the possibility of error. Performance metrics have been compared against benchmark methods and superior performance reported. The sensing conduction and the accuracy of the proposed design are increased as 98.5%.

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Section: Related Workmentioning

confidence: 82%

Section: Related Workmentioning

confidence: 99%

A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

Perumal¹,

Nagarajan²

2022

Int J Communication

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“…Recently, several spectrum sensing approaches have been established to mitigate the spectrum scarcity issues. Generally, these methods are classified as wideband and narrowband sensing methods [4]. 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.…”

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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“…Similar distributed processing methods for power spectral estimation can be found in [20]. The work in [21] proposes a sub-Nyquist wideband spectrum sensing method based on CCS for rapid wideband spectrum sensing that is suitable for fast spectral detection for CR. However, accurate power spectral density estimation is not the primary requirement in this course-grained sensing method.…”

Section: Introductionmentioning

confidence: 99%

Compressive Covariance Sensing-Based Power Spectrum Estimation of Real-Valued Signals Subject to Sub-Nyquist Sampling

Alwan

2021

Modelling and Simulation in Engineering

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In this work, an estimate of the power spectrum of a real-valued wide-sense stationary autoregressive signal is computed from sub-Nyquist or compressed measurements in additive white Gaussian noise. The problem is formulated using the concepts of compressive covariance sensing and Blackman-Tukey nonparametric spectrum estimation. Only the second-order statistics of the original signal, rather than the signal itself, need to be recovered from the compressed signal. This is achieved by solving the resulting overdetermined system of equations by application of least squares, thereby circumventing the need for applying the complicated ℓ 1 -minimization otherwise required for the reconstruction of the original signal. Moreover, the signal need not be spectrally sparse. A study of the performance of the power spectral estimator is conducted taking into account the properties of the different bases of the covariance subspace needed for compressive covariance sensing, as well as different linear sparse rulers by which compression is achieved. A method is proposed to benefit from the possible computational efficiency resulting from the use of the Fourier basis of the covariance subspace without considerably affecting the spectrum estimation performance.

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A rapid coarse-grained blind wideband spectrum sensing method for cognitive radio networks

Cited by 10 publications

References 44 publications

A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

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

Compressive Covariance Sensing-Based Power Spectrum Estimation of Real-Valued Signals Subject to Sub-Nyquist Sampling

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