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Compressive Spectrum Sensing for Cognitive Radio Networks [article]

Fatima Salahdine
2018 arXiv   pre-print
In the spectrum sensing process, the channel occupancy is measured with spectrum sensing techniques in order to detect unused channels.  ...  A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently.  ...  For the sensing matrix, one-bit compressive sensing can perform well with random matrices as well as the structured matrices including Circulant and Toeplitz matrices [207] .  ... 
arXiv:1802.03674v1 fatcat:6ddexxzymjfqzpcw6cfoia42xy

Metrics for Evaluating the Efficiency of Compressing Sensing Techniques

Fatima Salahdine, Elias Ghribi, Naima Kaabouch
2020 2020 International Conference on Information Networking (ICOIN)  
There are two main points to consider when it comes to using compressive sensing.  ...  Performing compressive sensing requires analyzing and investigating the efficiency of the measurement matrix and the recovery algorithm.  ...  Sensors networks Wireless sensors network is another area for which compressive sensing has been proposed.  ... 
doi:10.1109/icoin48656.2020.9016490 dblp:conf/icoin/SalahdineGK20 fatcat:n4d5cf4jb5awzjoyh2deot7q2y

State of the art and prospects of structured sensing matrices in compressed sensing

Kezhi Li, Shuang Cong
2015 Frontiers of Computer Science  
However, the pure random sensing matrices usually require huge memory for storage and high computational cost for signal reconstruction.  ...  Compressed sensing (CS) enables people to acquire the compressed measurements directly and recover sparse or compressible signals faithfully even when the sampling rate is much lower than the Nyquist rate  ...  Fig. 1 1 (a) Compressive sensing measurement process with a random Gaussian measurement matrix Φ and DCT matrix Ψ as sparsifying matrix. (b) Measurement process with Θ = ΦΨ.  ... 
doi:10.1007/s11704-015-3326-8 fatcat:ftl6hdkjdvaqflxcjo2imcn26a

Frugal Sensing: Wideband Power Spectrum Sensing From Few Bits

Omar Mehanna, Nicholas D. Sidiropoulos
2013 IEEE Transactions on Signal Processing  
It now appears increasingly likely that spectrum sensing will be performed using networks of sensors, or crowd-sourced to handheld mobile devices.  ...  Wideband spectrum sensing is a key requirement for cognitive radio access.  ...  Define the circulant matrix . For example, for and , Circulant matrices are diagonalized by a DFT: , where holds the eigen- values of [26, p. 107]. Note that . Since we enforce  ... 
doi:10.1109/tsp.2013.2252171 fatcat:duyobnba5jatln3ktduxr63ffm

Secure Wireless Communications Based on Compressive Sensing: A Survey

Yushu Zhang, Yong Xiang, Leo Yu Zhang, Yue Rong, Song Guo
2018 IEEE Communications Surveys and Tutorials  
The first phase reviews the security aspects of CS according to different types of random measurement matrices such as Gaussian matrix, circulant matrix, and other special random matrices, which establishes  ...  , remote sensing imaging, and anology to information conversion, since it can realize simultaneous sampling and compression.  ...  A CSC scheme must leak the information more or less, for example, for the cases of Gaussian measurement matrix, circulant measurement matrix, and structurally random matrices.  ... 
doi:10.1109/comst.2018.2878943 fatcat:7tjm5dm7jvg75iieqdn33ubnum

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Youness Arjoune, Naima Kaabouch
2019 Sensors  
This survey paper was designed in a way to help new researchers in the field to become familiar with the concepts of spectrum sensing, compressive sensing, and machine learning, all of which are the enabling  ...  technologies of the future networks, yet to help researchers further improve the efficiently of spectrum sensing.  ...  It consists of pseudo-random number generators that produce the partial measurements. An autocorrelation of the compressed measurements is then used to reconstruct an estimate of the wideband signal.  ... 
doi:10.3390/s19010126 fatcat:gurmhbaj25f7rixhcwmyzg47fu

Uniform Recovery Bounds for Structured Random Matrices in Corrupted Compressed Sensing

Peng Zhang, Lu Gan, Cong Ling, Sumei Sun
2018 IEEE Transactions on Signal Processing  
In this paper, we prove the uniform recovery guarantee of this problem for two classes of structured sensing matrices.  ...  The first class can be expressed as the product of a unit-norm tight frame (UTF), a random diagonal matrix and a bounded columnwise orthonormal matrix (e.g., partial random circulant matrix).  ...  Popular sensing matrices under this framework include partial random circulant matrices [14] , random demodulation [33] , random probing [34] and compressive multiplexing [35] . B.  ... 
doi:10.1109/tsp.2018.2806345 fatcat:cuz5mo453vakxarrrrh4h6bsca

Sparse Signal Processing Concepts for Efficient 5G System Design

Gerhard Wunder, Holger Boche, Thomas Strohmer, Peter Jung
2015 IEEE Access  
We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security.  ...  Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years.  ...  Strohmer acknowledges partial support from the NSF via grant dtra-dms 1042939, and from DARPA via grant N66001-11-1-4090. P. Jung was supported by DFG-grant JU 2795/2-1.  ... 
doi:10.1109/access.2015.2407194 fatcat:g5bkjwwdorasfmsmnfngfkptdm

Compressive demodulation of mutually interfering signals

Yao Xie, Yuejie Chi, Lorne Applebaum, Robert Calderbank
2012 2012 IEEE Statistical Signal Processing Workshop (SSP)  
The promise of compressed sensing is the demodulation of sparse superpositions of signature waveforms from very few measurements.  ...  It describes a MUD architecture that uses subsampling to convert analog input to a digital signal, and then uses iterative matching pursuit to recover the active users.  ...  MODEL Consider a multiuser system with N users.  ... 
doi:10.1109/ssp.2012.6319768 dblp:conf/ssp/XieCAC12 fatcat:zglhx4jw4vbqpcpe7pkjhs5nqq

Compressive Sensing Based High-Resolution Channel Estimation for OFDM System

Jia Meng, Wotao Yin, Yingying Li, Nam Tuan Nguyen, Zhu Han
2012 IEEE Journal on Selected Topics in Signal Processing  
In this paper, we propose a system with an asymmetric DAC/ADC pair and formulate OFDM channel estimation as a compressive sensing problem.  ...  While there is no similar approaches (for asymmetric DAC/ADC pairs) to compare with, we derive the Cramér-Rao lower bound.  ...  The work of Wotao Yin was partially supported in part by NSF ECCS-1028790, NSF CAREER Award DMS-07-48839, and ONR Grant N00014-08-1-1101.  ... 
doi:10.1109/jstsp.2011.2169649 fatcat:wy6l5uyufbcghllt2yww5b6qnu

Structured Compressed Sensing: From Theory to Applications

Marco F. Duarte, Yonina C. Eldar
2011 IEEE Transactions on Signal Processing  
Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity.  ...  In our overview, the theme is exploiting signal and measurement structure in compressive sensing.  ...  ACKNOWLEDGMENT The authors would like to thank their colleagues for many useful comments and for their collaboration on many topics related to this review. In particular, they are grateful to M.  ... 
doi:10.1109/tsp.2011.2161982 fatcat:mlbtksjmqvbmfmi6gpxwtunbti

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Youness Arjoune, Naima Kaabouch
2018 Sensors  
The authors of [22] proposed a Bayesian compressive sensing with a circulant matrix for narrow-band spectrum sensing to handle uncertainty.  ...  This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the  ...  We plan also to investigate the application of one-bit compressive sensing for wideband spectrum sensing because of its advantages such as low complexity, fast sampling, and low storage cost as well as  ... 
doi:10.3390/s18061839 pmid:29874876 pmcid:PMC6022006 fatcat:l4xxtezqpzeknaahjbrppn6d74

Impact of the Sensing Spectrum on Signal Recovery in Generalized Linear Models [article]

Junjie Ma, Ji Xu, Arian Maleki
2021 arXiv   pre-print
Based on our framework, we are able to show that for instance, in phase-retrieval problems, matrices with spikier spectrums are better for EP, while in 1-bit compressed sensing problems, less spiky (flatter  ...  We define a notion for the spikiness of the spectrum of 𝐀 and show the importance of this measure in the performance of the EP.  ...  One-bit compressed sensing with partial gaussian circulant matrices. arXiv preprint arXiv:1710.03287, 2017. [17] David L. Donoho, Arian Maleki, and Andrea Montanari.  ... 
arXiv:2111.03237v2 fatcat:obamdwrt2benfmtwijiifnngwq

Large-Scale Image Retrieval Based on Compressed Camera Identification

Diego Valsesia, Giulio Coluccia, Tiziano Bianchi, Enrico Magli
2015 IEEE transactions on multimedia  
Recent works showed that random projections can be used to significantly compress the PRNU, enabling operation on very large scales, previously impossible due to the size of the PRNU and to the complexity  ...  Index Terms-Image forensics, image search and retrieval, photo response non-uniformity (PRNU), random projections.  ...  Both can be mitigated by using a partial circulant sensing matrix, which allows to generate a lower number of random coefficients and to efficiently perform the product using the FFT, maintaining the distance-preserving  ... 
doi:10.1109/tmm.2015.2455417 fatcat:rhpyqshxuvhnda3kci76klkl5i

2010 Index IEEE Transactions on Signal Processing Vol. 58

2010 IEEE Transactions on Signal Processing  
K., , N., LS-CS-Residual (LS-CS): Compressive Sensing on Least Squares Residual; TSP Aug. 2010 4108-4120 Vaswani, N., and Lu, W., Modified-CS: Modifying Compressive Sensing for Problems With Partially  ...  ., +, TSP May 2010 2521-2533 Adaptive Kalman Filtering in Networked Systems With Random Sensor De- lays, Multiple Packet Dropouts and Missing Measurements.  ...  Global Positioning System A Fixed-Lag Particle Filter for the Joint Detection/Compensation of Interference Effects in GPS Navigation.  ... 
doi:10.1109/tsp.2010.2092533 fatcat:4y66ezuo7zf6doe6nwjqwtc42i
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