A Survey on Compressive Spectrum Sensing for Cognitive Radio Networks

Benazzouza, Salma; Ridouani, Mohammed; Salahdine, Fatima; Hayar, Aawatif

doi:10.1109/isc246665.2019.9071710

Cited by 23 publications

(7 citation statements)

References 41 publications

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“…In this section, we will present the most common spectrum detection techniques where each user individually decides if some frequency band in the spectrum is available for use. In particular, we will focus on the most popular in wireless communications, such as Energy Detector (ED), Cyclo-stationary Feature Detection (CFD), Compressive Detection (CD), and Matched Filter and Waveform-based technique [13,14]. Furthermore, we will present recent works on spectrum sensing exploiting the capabilities of machine/deep learning.…”

Section: Spectrum Sensing Methodsmentioning

confidence: 99%

An Efficient Distributed Approach for Cooperative Spectrum Sensing in Varying Interests Cognitive Radio Networks

Trigka

Δρίτσας

2022

Sensors

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The rapid growth in wireless communications, coupled with insufficient utilization of the spectrum, led to the development of new wireless services and the promising technology of cognitive radio (CR) networks, which facilitate periodic access to the unoccupied spectrum bands and thus increases spectral efficiency. A fundamental task in CR networks is spectrum sensing, through which unauthorized secondary users (SUs) detect unoccupied bands in the spectrum. To achieve this, an accurate estimate of the power spectrum is necessary. From this perspective, and given that many other factors can affect individual detection, such as pathloss and receiver uncertainty, we aim to improve its estimate by exploiting the spatial diversity in the SUs’ observations. Spectrum sensing is treated as a parameters estimation problem, assuming that the parameters’ vector of each SU consists of some global and partially common parameters. To exploit this modeling, distributed and cooperative spectrum sensing is the subject of interest in this study. Diffusion techniques, and especially the Adapt-Then-Combine (ATC) method will be exploited, where each SU cooperates with a group of nodes in its neighborhood that share the same parameters of interest. We consider a network of three static PUs with overlapping power spectrums, and thus, frequency bands. The performance of the employed method will be evaluated under two scenarios: (i) when the PUs spectrum varies, since some frequency bands are not yet utilized, and (ii) when the frequency bands of the PUs are fixed, but there is a mobile SU in the network, changing regions and parameters of interest. Experimental results and performance analysis reveal the ATC algorithm robustness and efficiency.

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Section: Spectrum Sensing Methodsmentioning

confidence: 99%

An Efficient Distributed Approach for Cooperative Spectrum Sensing in Varying Interests Cognitive Radio Networks

Trigka

Δρίτσας

2022

Sensors

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“…We considered a centralized cooperative CRN with I nodes classified into Z normal nodes (SUs) and L abnormal nodes (MUs), which cooperate with an FC to perform the spectrum sensing process. Before reaching the final decision about the spectrum occupation, all CRN users, including SUs and MUs, initially performed a double authentication process based on compressive sensing technique [23,24], combined with a machine learning approach to confirm their reliability. Then, once the corresponding FC has successfully completed its classification, each user identified as a trusted user is automatically allowed to start the spectrum sensing process.…”

Section: A Stacking Model-based Malicious Users Detectionmentioning

confidence: 99%

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Benazzouza

Ridouani²,

Salahdine³

et al. 2022

Sensors

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Cooperative network is a promising concept for achieving a high-accuracy decision of spectrum sensing in cognitive radio networks. It enables a collaborative exchange of the sensing measurements among the network users to monitor the primary spectrum occupancy. However, the presence of malicious users leads to harmful interferences in the system by transmitting incorrect local sensing observations.To overcome this security related problem and to improve the accuracy decision of spectrum sensing in cooperative cognitive radio networks, we proposed a new approach based on two machine learning solutions. For the first solution, a new stacking model-based malicious users detection is proposed, using two innovative techniques, including chaotic compressive sensing technique-based authentication for feature extraction with a minimum of measurements and an ensemble machine learning technique for users classification. For the second solution, a novel deep learning technique is proposed, using scalogram images as inputs for the primary user spectrum’s classification. The simulation results show the high efficiency of both proposed solutions, where the accuracy of the new stacking model reaches 97% in the presence of 50% of malicious users, while the new scalogram technique-based spectrum sensing is fast and achieves a high probability of detection with a lower number of epochs and a low probability of false alarm.

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“…Estimating the wideband signal's sparseness degree is crucial since it serves as background information for choosing the right number of observations [64]. However, in a rapidly evolving distributed environment, it is challenging to continue learning this prior knowledge [65].…”

Section: Research Challenges In Spectrummentioning

confidence: 99%

Cognitive Wireless Networks Based Spectrum Sensing Strategies: A Comparative Analysis

Haldorai¹,

Sivaraj²,

Nagabushanam

et al. 2022

Applied Computational Intelligence and Soft Computing

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Because of numerous dormant application fields, wireless sensor networks (WSNs) have emerged as an important and novel area in radio and mobile computing research. These applications range from enclosed system configurations in the home and office to alfresco enlistment in an opponent’s landmass in a strategic flashpoint. Cognitive radio networks (CRNs) can be created by integrating radio link capabilities with network layer operations utilizing cognitive radios. The goal of CRN design is to optimize the general system operations to meet customer requirements at any location worldwide by much more efficiently addressing CRNs instead of simply connecting spectrum utilization. When compared to conventional radio networks, CRNs are more versatile and susceptible to wireless connections. Recent advancements in wireless communication have resulted in increasing spectrum scarcity. As a modern innovation, cognitive radio aims to tackle this challenge by proactively utilizing the spectrum. Because cognitive radio (CR) technology gives assailants additional possibilities than a normal wireless network, privacy in a CRN becomes a difficult challenge. We concentrate on examining the surveillance system at a societal level, in which both defense and monitoring are critical components in assuring the channel’s privacy. The current state of investigation into spectrum sensing and potential risks in cognitive radios is reviewed in this study.

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A Survey on Compressive Spectrum Sensing for Cognitive Radio Networks

Cited by 23 publications

References 41 publications

An Efficient Distributed Approach for Cooperative Spectrum Sensing in Varying Interests Cognitive Radio Networks

An Efficient Distributed Approach for Cooperative Spectrum Sensing in Varying Interests Cognitive Radio Networks

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Cognitive Wireless Networks Based Spectrum Sensing Strategies: A Comparative Analysis

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