Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has been progressing at a rapid pace and significant advances have been made. To help researchers stay abreast of these advances, surveys and tutorial papers are strongly needed. Therefore, in this paper, we aimed to provide an in-depth survey on the most recent advances in spectrum sensing, covering its development from its inception to its current state and beyond. In addition, we highlight the efficiency and limitations of both narrowband and wideband spectrum sensing techniques as well as the challenges involved in their implementation. TV white spaces are also discussed in this paper as the first real application of cognitive radio. Last but by no means least, we discuss future research directions. 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.

https://www.mdpi.com/1424-8220/19/1/126/pdf/1

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

Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network

An improved blind spectrum sensing scheme is established by the covariance matrix Cholesky decomposition and radial basis function (RBF)-support vector machine (SVM) decision classification at low signal-to-noise ratios (SNRs). Under strong background noises, the proposed scheme improves the recognition rate of primary users (PUs) than that of the current blind spectrum sensing. First, the ratio of the maximum-to-minimum eigenvalue of a covariance matrix obtained by the Cholesky decomposition is used to construct the statistics. Second, the statistics are labeled with “+1” or “−1,” namely, the energy characteristics of the training samples are extracted and marked with “+1” for PUs and “−1” for noises. Finally, an RBF-SVM classification model, with an intelligent RBF as the SVM kernel function, is obtained by training the above-mentioned statistics and the labels. Thus, the received signals are classified as PUs or not be trained in the SVM model. The threshold possesses self-learning ability, and it distinguishes PU signals from noises effectively. The classification among PU signals and noises is implemented by the optimal SVM decision boundary, derived from maximizing the margin of the decision boundary of trained samples for efficient detection. In addition, the complexity of the statistic construction is lower than that of the conventional maximum minimum eigenvalue (MME). The simulation results show that the RBF in our scheme has 77.5% accuracy at −10 dB, and it outperforms linear kernel function significantly by about 27.5% in accuracy at −10 dB. In addition, the average error probability of the proposed scheme is reduced by about 26% when compared with those of original SVM schemes at −20 dB. The proposed scheme also outperforms the current MME detection in detection probability over 10% at −20 dB. Therefore, the proposed blind spectrum sensing scheme can be efficiently used to detect the PUs by the covariance matrix Cholesky decomposition and the RBF-SVM decision classification in the fifth-generation (5G) communications, especially at low SNRs.

/pdf/improved-blind-spectrum-sensing-by-covariance-matrix-41z7lrvzc3.pdf

Improved Blind Spectrum Sensing by Covariance Matrix Cholesky Decomposition and RBF-SVM Decision Classification at Low SNRs

Machine learning for cooperative spectrum sensing and sharing: A survey

Spectrum sensing in cognitive radio networks and metacognition for dynamic spectrum sharing between radar and communication system: A review

There's a huge demand for spectrum, but in conventional system radio spectrum is inefficiently used. To overcome this problem and increase array use of spectrum, cognitive radio (CR) technology concept was proposed. CR is the greatest mechanism for use spectrum efficiently. The main objective of CR is to use scarce and limited natural resources efficiently without any interference to the primary users (PUs). Among most challenging problems in cognitive radio systems is spectrum sensing concepts. Energy detection, Matched filter detection and Cyclostationary detections are most common strategies for spectrum sensing. This study has been performed based on Energy detection spectrum method. The reason for choosing Energy detection technique, it doesn't need any earlier information from the PU transmission. Also, the particular result of energy detection method degrades with lower sign to noises ratio (SNR) level signal location. General detection performance of Energy detection is highly dependent upon noise, especially while the SNR is very low for PU. Considering this issue, this paper presents an adaptive threshold model for efficient Energy detection technique to enhance the detection performance at low SNR level. Simulation outcomes show, the detection performance using offered system model is more preferable than fixed threshold at low SNR signal locations.

Enhanced spectrum sensing based on Energy detection in cognitive radio network using adaptive threshold

Random sampling for effective spectrum sensing in cognitive radio time slotted environment

Cognitive radio (CR) is a form of wireless communication in which a transceiver can wisely detect communication channels that are in use and those which are not, and immediately move into vacant channels while avoiding occupied ones. In such systems, spectrum sensing (SS) is a crucial operation. It consists to detect the available frequency bands. Many spectrum sensing techniques are presented in the literature. In this paper, we present a comparative study between two Spectrum Sensing techniques: the Maximum Eigenvalue Detection (MED) and the Energy Detection (ED). The performance of these two methods is evaluated in terms of their Receiver Operating Characteristics (ROC curves) and their detection probability for different values of Signal to Noise Ratio (SNR) and of smoothing factor L. The results of this comparative study are given and discussed.

Energy detection versus maximum eigenvalue based detection: A comparative study

Cognitive radio (CR) technology has been proposed as a solution for the issue of spectrum under-utilization in wireless communication. It is designed to facilitate effective utilization of the spectrum and to provide highly reliable communication for all the network users. As it is presented in the literature, spectrum sensing (SS) is the important function to establish cognitive radio networks. In this paper, we investigate the effect of random sampling on the maximum eigenvalue based detection. The performance of this approach is evaluated in terms of its receiver operating characteristic curve (ROC curve) based on Monte-Carlo simulation and its probability of detection for different values of signal to noise ratio (SNR) and of smoothing factor L. Obtained results give a probability of detection PD = 1 which shows that it is possible to perform a spectrum sensing method by applying random sampling.

Maximum Eigenvalue Based Detection Using Jittered Random Sampling

Cognitive radio is a mechanism allowing dynamic access to spectrum channels. Since its beginnings, researchers have been working on using this inventive technology to control and manage the spectrum resources. Consequently, this research field has been progressing rapidly and important advances have been made. Spectrum sensing is a key function of cognitive radios that helps prevent the harmful interference with licensed users, as well as identifies the available spectrum to improve its utilization. Several spectrum sensing techniques are found in scientific literature. In this paper, we investigate the effect of the random sampling in spectrum sensing. We propose a spectrum sensing approach based on the energy detection and on the maximum eigenvalue detection (MED) combined with random sampling. The performance of the proposed approach is evaluated in terms of the receiver operating characteristics curves and in terms of the detection probability for different values of signal to noise ratio. The obtained results are compared to the uniform sampling case to show the added value of random sampling.

Effect of random sampling on spectrum sensing for cognitive radio networks

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