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Journal articles on the topic 'Compressive spectrum sensing'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Al-Hussain, Ali Mohammad A., and Maher K. Mahmood Al Azawi. "Spectrum sensing of wideband signals based on cyclostationary detection and compressive sensing." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1361–68. https://doi.org/10.11591/ijeecs.v20.i3.pp1361-1368.

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Compressive sensing is a powerful technique used to overcome the problem of high sampling rate when dealing with wideband signal spectrum sensing which leads to high speed analogue to digital convertor (ADC) accompanied with large hardware complexity, high processing time, long duration of signal spectrum acquisition and high consumption power. Cyclostationary based detection with compressive technique will be studied and discussed in this paper. To perform the compressive sensing technique, discrete cosine transform (DCT) is used as sparse representation basis of received signal and Gaussian

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2

Zhang, Xingjian, Yuan Ma, Yue Gao, and Wei Zhang. "Autonomous Compressive-Sensing-Augmented Spectrum Sensing." IEEE Transactions on Vehicular Technology 67, no. 8 (2018): 6970–80. http://dx.doi.org/10.1109/tvt.2018.2822776.

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3

A. AL-Hussain, Ali Mohammad, and Maher Khudair Mahmood Al Azawi. "Spectrum sensing of wideband signals based on cyclostationary and compressive sensing." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1361. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1361-1368.

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Compressive sensing is a powerful technique used to overcome the problem of high sampling rate when dealing with wideband signal spectrum sensing which leads to high speed analogue to digital convertor (ADC) accompanied with large hardware complexity, high processing time, long duration of signal spectrum acquisition and high consumption power. Cyclostationary based detection with compressive technique will be studied and discussed in this paper. To perform the compressive sensing technique, Discrete Cosine Transform (DCT) is used as sparse representation basis of received signal and Gaussian

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4

Marín Alfonso, Jeison, Jose Martínez Torre, Henry Arguello Fuentes, and Leonardo Agudelo. "Compressive Multispectral Spectrum Sensing for Spectrum Cartography." Sensors 18, no. 2 (2018): 387. http://dx.doi.org/10.3390/s18020387.

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S. Sureshkrishna, S. Varalakshmi, K. Senthil Kumar, A. K. Gnanasekar,. "An Effective Adaptive Threshold Based Compressive Spectrum Sensing in Cognitive Radio Networks." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 1220–24. http://dx.doi.org/10.17762/itii.v9i1.260.

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Spectrum sensing is playing a vital role in Cognitive Radio networks. Wideband spectrum sensing increases the speed of sensing but which in turn requires higher sampling rate and also increases the complexity of hardware and also power consumption. Compression based sensing reduces the sampling rate by using Sub-Nyquist sampling but the compression and the reconstruction problem exists. In compression based spectrum sensing, noise uncertainty is one of the major performance degradation factor. To reduce this degradation, compressive measurements based sensing with adaptive threshold is propose

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Mohammad A. AL-Hussain, Ali, and Maher K. Mahmood. "SPECTRUM SENSING OF WIDE BAND SIGNALS BASED ON ENERGY DETECTION WITH COMPRESSIVE SENSING." Journal of Engineering and Sustainable Development 24, no. 06 (2020): 83–90. http://dx.doi.org/10.31272/jeasd.24.6.7.

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Compressive sensing (CS) technique is used to solve the problem of high sampling rate with wide band signal spectrum sensing where high speed analogue to digital converter is needed to do that. This leads to difficult hardware implementation, large time of sensing and detection with high consumptions power. The proposed approach combines energy-based detection, with CS compressive sensing and investigates the probability of detection, and the probability of false alarm as a function of the SNR, showing the effect of compression to spectrum sensing performance of cognitive radio system. The Dis

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Liu, Jianfeng, Xin-Lin Huang, and Ping Wang. "Compressive Spectrum Sensing with Temporal-Correlated Prior Knowledge Mining." Wireless Communications and Mobile Computing 2021 (April 10, 2021): 1–9. http://dx.doi.org/10.1155/2021/5539697.

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Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue to support heavy wireless services on sub-3GHz. Recently, broadband spectrum sensing becomes a hot topic with the help of compressive sensing technology, which will reduce the high-speed sampling rate requirement of analog-to-digital converter. This paper considers sequential compressive spectrum sensing, where the temporal correlation information between neighboring compressive sensing data will be exploited. Different from conventional compressive sensing, the previous compressive sensing data will be fused into p

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8

Arjoune, Youness, and Naima Kaabouch. "Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach." Sensors 18, no. 6 (2018): 1839. http://dx.doi.org/10.3390/s18061839.

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9

Odejide, Olusegun. "Dynamic Spectrum Detection Via Compressive Sensing." International journal of Computer Networks & Communications 4, no. 2 (2012): 101–16. http://dx.doi.org/10.5121/ijcnc.2012.4207.

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10

Sun, Biao, Qian Chen, Xinxin Xu, Yun He, and Jianjun Jiang. "Permuted&Filtered Spectrum Compressive Sensing." IEEE Signal Processing Letters 20, no. 7 (2013): 685–88. http://dx.doi.org/10.1109/lsp.2013.2258464.

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11

Sabahi, Mohamad Farzan, Maliheh Masoumzadeh, and Amir Reza Forouzan. "Frequency-domain wideband compressive spectrum sensing." IET Communications 10, no. 13 (2016): 1655–64. http://dx.doi.org/10.1049/iet-com.2015.0718.

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12

Fyhn, Karsten, Tobias L. Jensen, Torben Larsen, and Soren Holdt Jensen. "Compressive Sensing for Spread Spectrum Receivers." IEEE Transactions on Wireless Communications 12, no. 5 (2013): 2334–43. http://dx.doi.org/10.1109/twc.2013.032113.120975.

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13

Nagah, Mohammed, Shimaa Mostafa, Mohamed Megahed, and Mohamed K. Salama. "Application of Compressive Sensing (CS) to Wide-Band Cognitive Radio signals." International Uni-Scientific Research Journal 4, no. 1 (2023): 15–22. http://dx.doi.org/10.59271/s44685-023-2206-3.

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Compressive Sensing (CS) is a digital signal processing developed theory that encloses the signal sampling and compression, based on the sparsity characteristics of signal. This can decrease sampling rate, so reduce computational complexity of the system without degrading the performance of the system. This paper describes the theoretical frame and a few key technical, then illustrates the application of compressed sensing theory to wide-band cognitive radio signals. Spectrum sensing is a critical issue in wide-band Cognitive Radio (CR) networks as it faces hard challenges such as high hardwar

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14

Qiang Guo, Qiang Guo, Minghua Chen Minghua Chen, Yunhua Liang Yunhua Liang, Hongwei Chen Hongwei Chen, Sigang Yang Sigang Yang, and and Shihong Xie and Shihong Xie. "Photonics-assisted compressive sampling system for wideband spectrum sensing (Invited Paper)." Chinese Optics Letters 15, no. 1 (2017): 010012–10017. http://dx.doi.org/10.3788/col201715.010012.

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15

Benazzouza, Salma, Mohammed Ridouani, Fatima Salahdine, and Aawatif Hayar. "Chaotic Compressive Spectrum Sensing Based on Chebyshev Map for Cognitive Radio Networks." Symmetry 13, no. 3 (2021): 429. http://dx.doi.org/10.3390/sym13030429.

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Recently, the chaotic compressive sensing paradigm has been widely used in many areas, due to its ability to reduce data acquisition time with high security. For cognitive radio networks (CRNs), this mechanism aims at detecting the spectrum holes based on few measurements taken from the original sparse signal. To ensure a high performance of the acquisition and recovery process, the choice of a suitable sensing matrix and the appropriate recovery algorithm should be done carefully. In this paper, a new chaotic compressive spectrum sensing (CSS) solution is proposed for cooperative CRNs based o

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16

Hussein, Amr, Hossam Kasem, and Mohamed Adel. "Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques." International Journal of Engineering & Technology 6, no. 1 (2016): 1. http://dx.doi.org/10.14419/ijet.v6i1.6672.

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Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on e

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17

Luo, Ying, Qun Zhang, Guozheng Wang, and Youqing Bai. "Exact CS Reconstruction Condition of Undersampled Spectrum-Sparse Signals." Journal of Applied Mathematics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/715848.

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Compressive sensing (CS) reconstruction of a spectrum-sparse signal from undersampled data is, in fact, an ill-posed problem. In this paper, we mathematically prove that, in certain cases, the exact CS reconstruction of a spectrum-sparse signal from undersampled data is impossible. Then we present the exact CS reconstruction condition of undersampled spectrum-sparse signals, which is valuable for digital signal compression.

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18

Astaiza-Hoyos, Evelio, Pablo Jojoa, and Héctor Bermúdez. "Local Wideband Spectrum Sensing Dynamic Algorithm Based on Compressive Sensing." International Journal of Engineering and Technology 8, no. 5 (2016): 2221–33. http://dx.doi.org/10.21817/ijet/2016/v8i5/160805074.

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19

Abed, Hadeel S., and Hikmat N. Abdullah. "CHAOTIC COMPRESSIVE SENSING OF TV –UHF BAND IN IRAQ USING CHEBYSHEV GRAM SCHMIDT SENSING MATRIX." Iraqi Journal of Information and Communications Technology 1, no. 1 (2021): 134–45. http://dx.doi.org/10.31987/ijict.1.1.152.

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Cognitive radio (CR) is a promising technology for solving spectrum sacristy problem. Spectrum sensing is the main step of CR. Sensing the wideband spectrum produces more challenges. Compressive sensing (CS) is a technology used as spectrum sening in CR to solve these challenges. CS consists of three stages: sparse representation, encoding and decoding. In encoding stage sensing matrix are required, and it plays an important role for performance of CS. The design of efficient sensing matrix requires achieving low mutual coherence . In decoding stage the recovery algorithm is applied to reconst

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20

Qi, Haoran, Xingjian Zhang, and Yue Gao. "Channel Energy Statistics Learning in Compressive Spectrum Sensing." IEEE Transactions on Wireless Communications 17, no. 12 (2018): 7910–21. http://dx.doi.org/10.1109/twc.2018.2872712.

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21

Sun, Hongjian, Wei-Yu Chiu, and A. Nallanathan. "Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios." IEEE Communications Letters 16, no. 11 (2012): 1812–15. http://dx.doi.org/10.1109/lcomm.2012.092812.121648.

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22

Eltabie, Omar M., Mohamed F. Abdelkader, and Atef M. Ghuniem. "Incorporating Primary Occupancy Patterns in Compressive Spectrum Sensing." IEEE Access 7 (2019): 29096–106. http://dx.doi.org/10.1109/access.2019.2899953.

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23

Sun, Weichao, Zhitao Huang, Fenghua Wang, and Xiang Wang. "Compressive wideband spectrum sensing based on single channel." Electronics Letters 51, no. 9 (2015): 693–95. http://dx.doi.org/10.1049/el.2014.4223.

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24

Bai, Linda, and Sumit Roy. "Compressive Spectrum Sensing Using a Bandpass Sampling Architecture." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2, no. 3 (2012): 433–42. http://dx.doi.org/10.1109/jetcas.2012.2214874.

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25

Jiang, Jing, Hongjian Sun, David Baglee, and H. Vincent Poor. "Achieving Autonomous Compressive Spectrum Sensing for Cognitive Radios." IEEE Transactions on Vehicular Technology 65, no. 3 (2016): 1281–91. http://dx.doi.org/10.1109/tvt.2015.2408258.

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26

Qin, Lizhi, Yuming Chen, Leli Zhong, and Hongzhi Zhao. "Compressive Wideband Spectrum Sensing Aided Intelligence Transmitter Design." Sensors 25, no. 8 (2025): 2400. https://doi.org/10.3390/s25082400.

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In order to realize robust communication in complicated interference electromagnetic environments, an intelligent transmitter design is proposed in this paper, where an auxiliary wideband receiver senses the electromagnetic distribution information in a wide bandwidth range to decide the optimal working frequency. One of the key issues is suppressing the self-interference of high-power transmitter signals to the co-platform wideband sensing receiver. Due to the multipath effect of the self-interference channel, perfect time synchronization of self-interference signals is not achievable, which

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27

Wang, Yulin, and Gengxin Zhang. "Compressed Wideband Spectrum Sensing Based on Discrete Cosine Transform." Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/464895.

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Discrete cosine transform (DCT) is a special type of transform which is widely used for compression of speech and image. However, its use for spectrum sensing has not yet received widespread attention. This paper aims to alleviate the sampling requirements of wideband spectrum sensing by utilizing the compressive sampling (CS) principle and exploiting the unique sparsity structure in the DCT domain. Compared with discrete Fourier transform (DFT), wideband communication signal has much sparser representation and easier implementation in DCT domain. Simulation result shows that the proposed DCT-

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28

Yu, Zhuizhuan, Xi Chen, Sebastian Hoyos, Brian M. Sadler, Jingxuan Gong, and Chengliang Qian. "Mixed-Signal Parallel Compressive Spectrum Sensing for Cognitive Radios." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/730509.

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Wideband spectrum sensing for cognitive radios requires very demanding analog-to-digital conversion (ADC) speed and dynamic range. In this paper, a mixed-signal parallel compressive sensing architecture is developed to realize wideband spectrum sensing for cognitive radios at sub-Nqyuist rates by exploiting the sparsity in current frequency usage. Overlapping windowed integrators are used for analog basis expansion, that provides flexible filter nulls for clock leakage spur rejection. A low-speed experimental system, built with off-the-shelf components, is presented. The impact of circuit noni

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29

Kim, Cheolsun, Dongju Park, and Heung-No Lee. "Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network." Sensors 20, no. 3 (2020): 594. http://dx.doi.org/10.3390/s20030594.

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Compressive sensing (CS) spectroscopy is well known for developing a compact spectrometer which consists of two parts: compressively measuring an input spectrum and recovering the spectrum using reconstruction techniques. Our goal here is to propose a novel residual convolutional neural network (ResCNN) for reconstructing the spectrum from the compressed measurements. The proposed ResCNN comprises learnable layers and a residual connection between the input and the output of these learnable layers. The ResCNN is trained using both synthetic and measured spectral datasets. The results demonstra

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30

Taher, Montadar Abas, Mohammad Z. Ahmed, and Emad Hmood Salman. "Compressive spectrum sensing using two-stage scheme for cognitive radio networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5899. http://dx.doi.org/10.11591/ijece.v10i6.pp5899-5908.

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The modern applications of communications that use wideband signals suffer the lacking since the resources of this kind of signals are limited especially for fifth generation (5G). The Compressive Spectrum Sensing (COMPSS) techniques address such issues to reuse the detected signals in the networks and applications of 5G. However, the raw techniques of COMPSS have low compression ratio and high computational complexity rather than high level of noise variance. In this paper, a hybrid COMPSS scheme has been developed for both non-cooperative and cooperative cognitive radio networks. The propose

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31

Montadar, Abas Taher, Z. Ahmed Mohammad, and Hmood Salman Emad. "Compressive spectrum sensing using two-stage scheme for cognitive radio networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5899–908. https://doi.org/10.11591/ijece.v10i6.pp5899-5908.

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The modern applications of communications that use wideband signals suffer the lacking since the resources of this kind of signals are limited especially for fifth generation (5G). The compressive spectrum sensing (COMPSS) techniques address such issues to reuse the detected signals in the networks and applications of 5G. However, the raw techniques of COMPSS have low compression ratio and high computational complexity rather than high level of noise variance. In this paper, a hybrid COMPSS scheme has been developed for both non-cooperative and cooperative cognitive radio networks. The propose

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32

Xu, Bin Bin. "Gradual Compressive Spectrum Sensing for Wideband Cognitive Radio Network." Applied Mechanics and Materials 441 (December 2013): 915–19. http://dx.doi.org/10.4028/www.scientific.net/amm.441.915.

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In this paper, a Gradual Compressive spectrum sensing method is presented for collaborated users in wideband cognitive radio (CR) network. By taking the advantage of compressive sensing (CS), we can reconstructs the wideband spectrum using sub-Nyquist samples. Furthermore, we employ gradual signal acquisition and recovery that can terminate the process once the result of spectral recovery converge. This proposed method is flexible for different wideband signal sparsity and signal to noise ratio, leading to enhanced network throughput. Simulations testify the effectiveness of the proposed metho

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33

Astaiza, Evelio, Hector Fabio Bermudez, and Wilmar Yesid Campo. "Efficient Wideband Spectrum sensing Based on Compressive Sensing and Multiband Signal Covariance." IEEE Latin America Transactions 15, no. 3 (2017): 393–99. http://dx.doi.org/10.1109/tla.2017.7867167.

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34

Wisudawan, Hasbi Nur Prasetyo, Dyonisius Dony Ariananda, and Risanuri Hidayat. "Compressive Joint Angular and Frequency Spectrum Sensing Based on MUSIC Spectrum Reconstruction." Wireless Personal Communications 111, no. 1 (2019): 513–40. http://dx.doi.org/10.1007/s11277-019-06871-4.

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35

Meng, Fan, Yun-Zuo Zhang, Wei-Wei Feng, Peng-Fei Wu, and Ge-Yin Zou. "Spectrum detection based on compressive sensing inside multimode fibers." Acta Physica Sinica 69, no. 13 (2020): 134204. http://dx.doi.org/10.7498/aps.69.20200014.

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36

Farrag, Mohammed. "Secure differential compressive spectrum sensing with 1-bit quantisation." IET Communications 13, no. 6 (2019): 637–41. http://dx.doi.org/10.1049/iet-com.2018.5279.

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37

Zeng, Fanzi, Chen Li, and Zhi Tian. "Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks." IEEE Journal of Selected Topics in Signal Processing 5, no. 1 (2011): 37–48. http://dx.doi.org/10.1109/jstsp.2010.2055037.

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38

Quan, Yinghui, Lei Zhang, Yachao Li, Hongxian Wang, and Mengdao Xing. "OTHR Spectrum Reconstruction of Maneuvering Target with Compressive Sensing." International Journal of Antennas and Propagation 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/870352.

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High-frequency (HF) over-the-horizon radar (OTHR) works in a very complicated electromagnetic environment. It usually suffers performance degradation caused by transient interference. In this paper, we study the transient interference excision and full spectrum reconstruction of maneuvering targets. The segmental subspace projection (SP) approach is applied to suppress the clutter and locate the transient interference. After interference excision, the spectrum is reconstructed from incomplete measurements via compressive sensing (CS) by using a redundant Fourier-chirp dictionary. An improved o

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39

Alwan, Nuha A. S. "Compressive Covariance Sensing-Based Power Spectrum Estimation of Real-Valued Signals Subject to Sub-Nyquist Sampling." Modelling and Simulation in Engineering 2021 (April 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/5511486.

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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

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40

YAN, Shengnan, Mingxin LIU, and Jingjing SI. "Distributed Collaborative Spectrum Sensing Using 1-Bit Compressive Sensing in Cognitive Radio Networks." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E103.A, no. 1 (2020): 382–88. http://dx.doi.org/10.1587/transfun.2019eal2076.

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41

Salahdine, Fatima, Elias Ghribi, and Naima Kaabouch. "A Cooperative Spectrum Sensing Scheme Based on Compressive Sensing for Cognitive Radio Networks." International Journal of Digital Information and Wireless Communications 9, no. 2 (2019): 124–36. http://dx.doi.org/10.17781/p002619.

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42

Zeng, Haoye, Yantao Yu, Guojin Liu, and Yucheng Wu. "A Robust Method Based on Deep Learning for Compressive Spectrum Sensing." Sensors 25, no. 7 (2025): 2187. https://doi.org/10.3390/s25072187.

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In cognitive radio, compressive spectrum sensing (CSS) is critical for efficient wideband spectrum sensing (WSS). However, traditional reconstruction algorithms exhibit suboptimal performance, and conventional WSS methods fail to fully capture the inherent structural information of wideband spectrum signals. Moreover, most existing deep learning-based approaches fail to effectively exploit the sparse structures of wideband spectrum signals, resulting in limited reconstruction performance. To overcome these limitations, we propose BEISTA-Net, a deep learning-based framework for reconstructing c

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43

REN, Shiyu, Zhimin ZENG, Caili GUO, Xuekang SUN, and Kun SU. "A Low Computational Complexity Algorithm for Compressive Wideband Spectrum Sensing." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E100.A, no. 1 (2017): 294–300. http://dx.doi.org/10.1587/transfun.e100.a.294.

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44

Koteeshwari, R. S., and B. Malarkodi. "Compressive spectrum sensing for 5G cognitive radio networks – LASSO approach." Heliyon 8, no. 6 (2022): e09621. http://dx.doi.org/10.1016/j.heliyon.2022.e09621.

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45

Guo, Qiang, Yunhua Liang, Minghua Chen, Hongwei Chen, and Shizhong Xie. "Compressive spectrum sensing of radar pulses based on photonic techniques." Optics Express 23, no. 4 (2015): 4517. http://dx.doi.org/10.1364/oe.23.004517.

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46

Qi, Haoran, Xingjian Zhang, and Yue Gao. "Low-Complexity Subspace-Aided Compressive Spectrum Sensing Over Wideband Whitespace." IEEE Transactions on Vehicular Technology 68, no. 12 (2019): 11762–77. http://dx.doi.org/10.1109/tvt.2019.2937649.

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47

Liu, Yipeng, and Qun Wan. "Anti-sampling-distortion compressive wideband spectrum sensing for Cognitive Radio." International Journal of Mobile Communications 9, no. 6 (2011): 604. http://dx.doi.org/10.1504/ijmc.2011.042779.

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48

Zhang, Xingjian, Yuan Ma, Haoran Qi, et al. "Distributed Compressive Sensing Augmented Wideband Spectrum Sharing for Cognitive IoT." IEEE Internet of Things Journal 5, no. 4 (2018): 3234–45. http://dx.doi.org/10.1109/jiot.2018.2837891.

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49

Liang, Jun-hua, Yang Liu, and Wen-jun Zhang. "Joint compressive spectrum sensing scheme in wideband cognitive radio networks." Journal of Shanghai University (English Edition) 15, no. 6 (2011): 568–73. http://dx.doi.org/10.1007/s11741-011-0788-2.

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50

Liu, Yipeng, and Qun Wan. "Compressive slow-varying wideband power spectrum sensing for cognitive radio." annals of telecommunications - annales des télécommunications 69, no. 9-10 (2013): 559–67. http://dx.doi.org/10.1007/s12243-013-0414-3.

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