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.

The advancements in digital communication technology have made communication between humans more accessible and instant. However, personal and sensitive information may be available online through social networks and online services that lack the security measures to protect this information. Communication systems are vulnerable and can easily be penetrated by malicious users through social engineering attacks. These attacks aim at tricking individuals or enterprises into accomplishing actions that benefit attackers or providing them with sensitive data such as social security number, health records, and passwords. Social engineering is one of the biggest challenges facing network security because it exploits the natural human tendency to trust. This paper provides an in-depth survey about the social engineering attacks, their classifications, detection strategies, and prevention procedures.

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Abstract-Spectrum sensing is an important process in cognitive radio. A number of sensing techniques that have been proposed suffer from high processing time, hardware cost and computational complexity. To address these problems, compressive sensing has been proposed to decrease the processing time and expedite the scanning process of the radio spectrum. Selection of a suitable sparse recovery algorithm is necessary to achieve this goal. A number of sparse recovery algorithms have been proposed. This paper surveys the sparse recovery algorithms, classify them into categories, and compares their performances. For the comparison, we used several metrics such as recovery error, recovery time, covariance, and phase transition diagram. The results show that techniques under Greedy category are faster, techniques of Convex and Relaxation category perform better in term of recovery error, and Bayesian based techniques are observed to have an advantageous balance of small recovery error and a short recovery time.

In cognitive radio, one of the main challenges is wideband spectrum sensing. Existing spectrum 4 sensing techniques are based on a set of observations sampled by an analog/digital converter (ADC) at the 5 Nyquist rate. However, those techniques can sense only one band at a time because of the hardware limitations 6 on sampling rate. In addition, in order to sense a wideband spectrum, the band is divided into narrow bands or 7 multiple frequency bands. Secondary users (SU) have to sense each band using multiple RF frontends 8 simultaneously, which results in a very high processing time, hardware cost, and computational complexity. In 9 order to overcome this problem, the signal sampling should be as fast as possible, even with high dimensional 10 signals. Compressive sensing has been proposed as one of the solutions to reduce the processing time and 11 accelerate the scanning process. It allows reducing the number of samples required for high dimensional signal 12 acquisition while keeping the important information. Over the last decade, a number of papers related to 13 compressive sensing techniques have been published. However, most of these papers describe techniques 14 corresponding to one process either sparse representation, sensing matrix, or recovery. This paper provides an in 15 depth survey on compressive sensing techniques and classifies these techniques according to which process they 16 target, namely, sparse representation, sensing matrix, or recovery algorithms.It also discusses examples of 17 potential applicationsof these techniques including in spectrum sensing, channel estimation, and multiple-input 18 multiple-output (MIMO) based cognitive radio.Metrics to evaluate the efficiencies of existing compressive 19 sensing techniques are providedas well as the benefits and challenges in the context of cognitive radio networks.20 KeywordsCognitive radio network, spectrum sensing, compressive sensing, sparsity representation, sensing 21 matrix, processing time, recovery algorithms, restrict isometry property, analog to digital converter, Shannon-22 Nyquist theorem, channel estimation, MIMO. 23 1 Introduction 24 Over the last decade, a number of spectrum sensing techniques have been proposed to detect and locate 25 dynamically unused spectrum channels in a band of interest [1-6]. Examples of these techniques include energy 26 detection [7], matched filter [8], autocorrelation [9-11],and wavelet based detection [12].Energy detection 27operates by comparing the SU signal average energy with an estimated threshold. This technique does not 2 require any knowledge of the PU signal and itis simple and easy to implement. However,it has high false 29 detection ratesand it is not able to distinguish between signals and noise [7]. Matched filter requires the 30 knowledge of the PU signal characteristics including frequency, modulation type, and bandwidth. It operates by 31 comparing the matched filter output with a threshold [8].Autocorrelation based detection requires the knowledge 32 of the statistical distribut...

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Summary Compressive sensing involves 3 main processes: signal sparse representation, linear encoding or measurement collection, and nonlinear decoding or sparse recovery. In the measurement process, a measurement matrix is used to sample only the components that best represent the signal. The choice of the measurement matrix has an important impact on the accuracy and the processing time of the sparse recovery process. Hence, the design of accurate measurement matrices is of vital importance in compressive sensing. Over the last decade, a number of measurement matrices have been proposed. Therefore, a detailed review of these measurement matrices and a comparison of their performances are strongly needed. This paper explains the foundation of compressive sensing and highlights the process of measurement by reviewing the existing measurement matrices. It provides a 3‐level classification and compares the performance of 8 measurement matrices belonging to 4 different types using 5 evaluation metrics: the recovery error, processing time, recovery time, covariance, and phase transition diagram. The theoretical performance comparison is validated with experimental results, and the results show that the Circulant, Toeplitz, and Hadamard matrices outperform the other measurement matrices.

International audience—In cognitive radio networks, spectrum sensing aims to detect the unused spectrum channels in order to use the radio spectrum more efficiently. Various methods have been proposed in the past, such as energy, feature detection, and matched filter. These methods are characterized by a sensing threshold, which plays an important role in the sensing performance. Most of the existing techniques used a static threshold. However, the noise is random, and, thus the threshold should be dynamic. In this paper, we suggest an approach with an estimated and dynamic sensing threshold to increase the efficiency of the sensing detection. The matched filter method with dynamic threshold is simulated and its results are compared to those of other existing techniques. Keywords—cognitive radio networks; spectrum sensing; energy detection; matched filter detection; autocorrelation based sensing; estimated dynamic threshol