Relevant bibliographies by topics / Compressive spectrum sensing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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  5. Conference papers

Academic literature on the topic 'Compressive spectrum sensing'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Compressive spectrum sensing"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Compressive spectrum sensing"

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Zhang, Xingjian. "Compressive spectrum sensing in cognitive IoT." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/44700.

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With the rising of new paradigms in wireless communications such as Internet of things (IoT), current static frequency allocation policy faces a primary challenge of spectrum scarcity, and thus encourages the IoT devices to have cognitive capabilities to access the underutilised spectrum in the temporal and spatial dimensions. Wideband spectrum sensing is one of the key functions to enable dynamic spectrum access, but entails a major implementation challenge in terms of sampling rate and computation cost since the sampling rate of analog-to-digital converters (ADCs) should be higher than twice
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Liu, Feng. "Compressive Measurement of Spread Spectrum Signals." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/347310.

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Spread Spectrum (SS) techniques are methods used in communication systems where the spectra of the signal is spread over a much wider bandwidth. The large bandwidth of the resulting signals make SS signals difficult to intercept using conventional methods based on Nyquist sampling. Recently, a novel concept called compressive sensing has emerged. Compressive sensing theory suggests that a signal can be reconstructed from much fewer measurements than suggested by the Shannon Nyquist theorem, provided that the signal can be sparsely represented in a dictionary. In this work, motivated by this co
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Lui, Feng. "Spread Spectrum Signal Detection from Compressive Measurements." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579660.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV<br>Spread Spectrum (SS) techniques are methods used to deliberately spread the spectrum of transmitted signals in communication systems. The increased bandwidth makes detection of these signals challenging for non-cooperative receivers. In this paper, we investigate detection of Frequency Hopping Spread Spectrum (FHSS) signals from compressive measurements. The theoretical and simulated performances
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Qin, Zhijin. "Compressive sensing over TV white space in wideband cognitive radio." Thesis, Queen Mary, University of London, 2016. http://qmro.qmul.ac.uk/xmlui/handle/123456789/24244.

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Spectrum scarcity is an important challenge faced by high-speed wireless communications. Meanwhile, caused by current spectrum assignment policy, a large portion of spectrum is underutilized. Motivated by this, cognitive radio (CR) has emerged as one of the most promising candidate solutions to improve spectrum utilization, by allowing secondary users (SUs) to opportunistically access the temporarily unused spectrum, without introducing harmful interference to primary users. Moreover, opening of TV white space (TVWS) gives us the con dence to enable CR for TVWS spectrum. A crucial requirement
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Lagunas, Targarona Eva. "Compressive sensing based candidate detector and its applications to spectrum sensing and through-the-wall radar imaging." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144629.

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Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is
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Amrani, Naoufal. "Spectral decorrelation for coding remote sensing data." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/402237.

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Hoy en día, los datos de teledetección son esenciales para muchas aplicaciones dirigidas a la observación de la tierra. El potencial de los datos de teledetección en ofrecer información valiosa permite entender mejor las características de la tierra y las actividades humanas. Los desarrollos recientes en los sensores de satélites permiten cubrir amplias áreas geográficas, produciendo imágenes con resoluciones espaciales, espectrales y temporales sin precedentes. Esta cantidad de datos producidos implica una necesidad requiere técnicas de compresión eficientes para mejorar la transmisión y la
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Merlet, Sylvain. "Acquisition compressée en IRM de diffusion." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00916582.

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Cette thèse est consacrée à l'élaboration de nouvelles méthodes d'acquisition et de traitement de données en IRM de diffusion (IRMd) afin de caractériser la diffusion des molécules d'eau dans les fibres de matière blanche à l'échelle d'un voxel. Plus particulièrement, nous travaillons sur un moyen de reconstruction précis de l'Ensemble Average Propagator (EAP), qui représente la fonction de probabilité de diffusion des molécules d'eau. Plusieurs modèles de diffusion tels que le tenseur de diffusion ou la fonction de distribution d'orientation sont très utilisés dans la communauté de l'IRMd afi
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Dunlop, Matthew, and Phillip Poon. "Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C)." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579667.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV<br>The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measur
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Yang, Yang. "2D signal processing: efficient models for spectral compressive sensing & single image reflection suppression." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6667.

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Two efficient models in two-dimensional signal processing are proposed in the thesis. The first model deals with large scale spectral compressive sensing in continuous domain, which aims to recover a 2D spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500*500, whereas traditional approaches only handle signals of size around 20*20. The second model deals with the problem of single image
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Álvarez, Cortés Sara. "Pyramidal regression-based coding for remote sensing data." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667742.

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Los datos hiperespectrales capturados por teledetección cuentan con cientos o miles de componentes espectrales de similares longitudes de onda. Almacenarlos y transmitirlos conlleva una demanda excesiva en ancho de banda y memoria, ya de por sí bastante limitados, que pueden dar lugar a descartar información ya capturada o a dejar de capturarla. Para paliar estas limitaciones, se aplican algoritmos de compresión. Además, la tecnología de los sensores evoluciona continuamente, pudiéndose adquirir datos con mayores dimensiones. De ahí que, para no penalizar el funcionamiento y rendimiento de fut
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Books on the topic "Compressive spectrum sensing"

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Yazicigil, Rabia Tugce. Compressive Sampling as an Enabling Solution for Energy-Efficient and Rapid Wideband RF Spectrum Sensing in Emerging Cognitive Radio Systems. [publisher not identified], 2016.

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Book chapters on the topic "Compressive spectrum sensing"

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Gao, Yue, and Zhijin Qin. "Robust Compressive Spectrum Sensing." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00290-9_4.

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Gao, Yue, and Zhijin Qin. "Secure Compressive Spectrum Sensing." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00290-9_5.

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Gao, Yue, and Zhijin Qin. "Data-Driven Compressive Spectrum Sensing." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00290-9_3.

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Chen, Yanping, Yulong Gao, and Yongkui Ma. "Distributed Compressive Sensing Based Spectrum Sensing Method." In Machine Learning and Intelligent Communications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73564-1_24.

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Sankararajan, Radha, Hemalatha Rajendran, and Aasha Nandhini Sukumaran. "Compressive Spectrum Sensing for Cognitive Radio Networks." In Compressive Sensing for Wireless Communication. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337652-8.

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Mishra, Amit Kumar, and Ryno Strauss Verster. "CS Based Spectrum Sensing for ES." In Compressive Sensing Based Algorithms for Electronic Defence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46700-9_7.

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Dantu, Neeraj Kumar Reddy. "Dynamic Spectrum Sensing in Cognitive Radio Networks Using Compressive Sensing." In Lecture Notes in Electrical Engineering. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1823-4_9.

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Astaiza, Evelio, Héctor Bermudez, and Octavio J. Salcedo Parra. "Technique Stages for Efficient Wideband Spectrum Sensing Based on Compressive Sensing." In Mobile, Secure, and Programmable Networking. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67807-8_11.

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Morghare, Gaurav, and Sarita Singh Bhadauria. "Wideband Spectrum Compressive Sensing Technique in Cognitive Radio Networks." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_1.

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Benzater, Hadj Abdelkader, Djamal Teguig, and Nacerredine Lassami. "Compressive Spectrum Sensing in Cognitive Radio Networks: Recovery and Detection." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-60632-8_28.

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Conference papers on the topic "Compressive spectrum sensing"

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Polo, Yvan Lamelas, Ying Wang, Ashish Pandharipande, and Geert Leus. "Compressive wide-band spectrum sensing." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960089.

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Havary-Nassab, Veria, Shahrokh Valaee, and Shahram Shahbazpanahi. "Mobile distributed compressive sensing for spectrum sensing." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6855012.

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Romero, Daniel, Roberto Lopez-Valcarce, and Geert Leus. "Compressive wideband spectrum sensing with spectral prior information." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638505.

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Dai, Bin, Christopher Jones, Jimmy Price, Darren Gascooke, and Anthony Van Zuilekom. "COMPRESSIVE SENSING BASED OPTICAL SPECTROMETER FOR DOWNHOLE FLUID ANALYSIS." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0112.

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Downhole fluid analysis has the potential to resolve ambiguity in very complex reservoirs. Downhole fluid spectra contain a wealth of information to fingerprint a fluid and help to assess continuity. Commonly, a narrowband spectrometer with limited number of channels is used to acquire optical spectra of downhole fluid. The spectral resolution of this type of spectrometer is low due to limited number of narrowband channels. In this paper, we demonstrate a new type, compressive sensing (CS) based broadband spectrometer that provides accurate and high-resolution spectral measurement. Several spe
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Jung, Hoiyoon, Jung-Sun Um, Seung Keun Park, and Hyung Do Choi. "Dynamic compressive spectrum sensing method using adaptive compression sequences." In 2014 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2014. http://dx.doi.org/10.1109/ictc.2014.6983227.

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Wang, Ying, Ashish Pandharipande, and Geert Leus. "Compressive sampling based MVDR spectrum sensing." In 2010 2nd International Workshop on Cognitive Information Processing (CIP). IEEE, 2010. http://dx.doi.org/10.1109/cip.2010.5604239.

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Ying Wang, A. Pandharipande, Y. L. Polo, and G. Leus. "Distributed compressive wide-band spectrum sensing." In 2009 Information Theory and Applications Workshop (ITA). IEEE, 2009. http://dx.doi.org/10.1109/ita.2009.5044942.

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Ariananda, Dyonisius Dony, and Geert Leus. "Cooperative compressive wideband power spectrum sensing." In 2012 46th Asilomar Conference on Signals, Systems and Computers. IEEE, 2012. http://dx.doi.org/10.1109/acssc.2012.6489012.

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Sun, Hongjian, Arumugam Nallanathan, Jing Jiang, and H. Vincent Poor. "Compressive autonomous sensing (CASe) for wideband spectrum sensing." In ICC 2012 - 2012 IEEE International Conference on Communications. IEEE, 2012. http://dx.doi.org/10.1109/icc.2012.6363831.

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Koochakzadeh, Ali, Heng Qiao, and Piya Pal. "Compressive spectrum sensing with spectral priors for cognitive radar." In 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa). IEEE, 2016. http://dx.doi.org/10.1109/cosera.2016.7745708.

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