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Spectrum is considered one of the most highly regulated and limited natural resources. Cognitive radio (CR) relies on cutting-edge technology which helps to rectify the issues related to spectrum shortage in wireless communication systems. The CR technology allows the secondary user to accomplish the process related to spectrum sensing for identifying the usage of spectrum in the cognitive radio network (CRN). Though various spectrum sensing approaches are introduced, they exhibit complexity during spectrum sensing. To overcome the issues related to spectrum sensing and utilization, this research introduces improved quantum inspired evolution (IQISE) algorithm with ResNet 50 architecture. The IQISE-ResNet 50 which helps to enhance the spectrum efficiency is used in spectrum sensing. The detection of occupied and unoccupied users in CRN is performed using ResNet 50 architecture, while the IQISE is utilized in the process of training the model and optimizing the weights to enhance spectrum sensing efficiency. The experimental results show that the results achieved by the proposed approach are more effective than S-QRNN and honey badger remora optimizationbased AlexNet (HBRO-based AlexNet). For example, the probability of correct classification of the proposed approach at -10 dB for binary phase shift keying (BPSK) modulation is 0.55, whereas the S-QRNN achieves an accuracy of 0.49. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) technology enables dynamic spectrum access to meet the growing demand for wireless communication. This study investigates spectrum sensing methods, specifically energy detection (ED) and matched filter detection (MFD), within hybrid strategies. A novel hybrid MFD method was developed and evaluated via MATLAB simulations, analyzing factors like sample size, signal-to-noise ratio (SNR), and false alarm probability. Results reveal that ED has a higher miss-detection rate compared to MFD and the proposed hybrid method, which performs particularly well under low sample counts and SNR conditions. This research enhances spectrum sensing techniques in cognitive radio systems, paving the way for more reliable wireless communication networks. [ABSTRACT FROM AUTHOR]

Quadratic forms with random kernel matrices are ubiquitous in applications of multivariate statistics, ranging from signal processing to time series analysis, biomedical systems design, wireless communications performance analysis, and other fields. Their statistical characterization is crucial to both design guideline formulation and efficient computation of performance indices. To this end, random matrix theory can be successfully exploited. In particular, recent advancements in spectral characterization of finite-dimensional random matrices from the so-called polynomial ensembles allow for the analysis of several scenarios of interest in wireless communications and signal processing. In this work, we focus on the characterization of quadratic forms in unit-norm vectors, with unitarily invariant random kernel matrices, and we also provide some approximate but numerically accurate results concerning a non-unitarily invariant kernel matrix. Simulations are run with reference to a peculiar application scenario, the so-called spectrum sensing for wireless communications. Closed-form expressions for the moment generating function of the quadratic forms of interest are provided; this will pave the way to an analytical performance analysis of some spectrum sensing schemes, and will potentially assist in the rate analysis of some multi-antenna systems. [ABSTRACT FROM AUTHOR]

Cognitive radios (CRs) are envisioned as a potential solution, to provide spectrum‐efficient communication for futuristic wireless communication systems like the Internet of Things (IoT) and others. This article examines the efficiency of CR's detection performance over the α−κ−F fading channel. The α−κ−F fading model is a generalized fading model intended to collectively quantify channel nonlinearity and shadowing effects. Therefore, the effect of channel nonlinearity and shadowing together on CR detection performance has been studied. New analytical formulas for the average probability of detection (APOD) for a single user and cooperative detection are obtained for energy detection (ED)–based spectrum sensing (SS). Additionally, to provide a deeper understanding of detection characteristics, expression for the area under the receiver operating characteristics is also obtained. The effectiveness of CR detection under various nonlinearity and shadowing effects is investigated using the developed analytical expressions, and the results indicate that performance degrades as channel impairments increase. At last, Monte Carlo simulations are employed to demonstrate that the obtained analytical expressions are valid. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of wireless communications, optimizing spectrum utilization has become paramount. Cognitive radio (CR) technology offers a promising solution by enabling unlicensed secondary users (SUs) to intelligently access and exploit underutilized spectrum bands. The citizens broadband radio service (CBRS) framework provides a structured approach to shared spectrum access, making it ideal for CR systems implementation. However, efficient spectrum sensing, especially within CBRS, is a major challenge due to environmental variations, interference, and the need for timely detection of primary users (PUs). This paper addresses the issue of suboptimal spectrum sensing efficiency in CBRS-based CR systems and proposes innovative approaches to improve cooperative spectrum sensing. We explore a spectrum sensing paradigm that encourages collaboration among secondary users and utilizes their collective intelligence to achieve better spectrum sensing performance. Our goal is to improve spectrum utilization within the CBRS ecosystem and enable more efficient and harmonious sharing of this valuable resource. [ABSTRACT FROM AUTHOR]

Summary: The discovery of malevolent users in cognitive radio networks (CRNs) is done by using blockchain‐based technique. For diagnosing the availability of frequency resources, the spectrum sensing (SS) plays an essential role in CRN. It is critical to enable cognitive radio (CR) technology in wireless communication systems for the next generation. Due to the increasing demand of the users, some bands are overloaded and others become underutilized. The CR applied models for vigorously assigning unlicensed users. The missed detection problem occurs in the conventional spectrum sensing schemes, which hampers the proper utilization of the spectrum. These motivated the development of a newly devised passive reputation method with an optimized deep model for spectrum sensing in CRN‐based blockchain. The sensing spectrum in CRN is designed using the cooperative spectrum sensing algorithm. The spectrum sensing is done with the decision made using AlexNet where the weights of AlexNet are trained with a hybrid optimization algorithm, named Honey Badger Remora Optimization (HBRO) algorithm. Thus, the output attained from the Alexnet_HBRO algorithm is considered. Meanwhile, the data are recorded in the blockchain, and then, the passive reputation is performed, and the determined output is considered. At last, the spectrum sensing is carried out by fusing AlexNet and passive reputation output where weights are optimally generated using the proposed Snake Honey Badger Remora Optimization (SHBRO) algorithm. The proposed SHBRO offered a high detection probability of 1, false alarm probability of 0.003, sensing time of 551.567s, and computation time of 210.102s. [ABSTRACT FROM AUTHOR]

With the help of cognitive radio, users will no longer have to deal with spectrum utilization and allocation conflicts. This technology allows them to utilize the idle resources of their system without affecting those who are licensed to do so. The advancements that have been made in wireless communication have not solved the challenges that arise when it comes to developing machine learning systems for dynamic and distributed environments. The paper aims to provide new researchers with an introduction to the various technologies that will be utilized in the future networks. These include machine learning, spectrum sensing, and compressive sensing. The findings will help researchers develop new techniques for enhancing the spectrum sensing's efficacy. [ABSTRACT FROM AUTHOR]

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device's signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use. [ABSTRACT FROM AUTHOR]

The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange. [ABSTRACT FROM AUTHOR]

The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU's parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods. [ABSTRACT FROM AUTHOR]

The exponential development of wireless applications has increased problems in the spectrum. The unlicensed frequency spectrum is becoming highly saturated, in order to support the conditions of new radio devices with increasing data rates. The spectrum that has previously been allocated is also unused. As a consequence of these developments, scientists have been putting a lot of effort into developing an approach to the issue of the limited spectrum that could make it possible to create a more effective utilization of it. As cognitive radio permits opportunistic use of the licensed spectrum in less crowded areas, it has been proposed as a solution to this challenge. This paper provides an overview of the cognitive radio environment, including a dynamic spectrum access strategy, as well as additional information on the cognitive capabilities operating in combination IoT communication technologies. We investigate the utilization of cognitive radio in the Internet of Things along with the significant role that cognitive radio plays in making the Internet of Things possible. Cognitive radio will provide a comprehensive examination of spectrum sensing, which will cover the many types of sensing, sensing that is based on machine learning, as well as open topics that still need to be addressed further in this sector. This research paper is written in a way that provides detailed instructions for the purpose of assisting new researchers in the area of Cognitive Radio Networks. [ABSTRACT FROM AUTHOR]

In order to address the issue of spectrum scarcity, cognitive radio (CR) offers an effective usage of radio spectrum resources by offering dynamic spectrum access. One kind of wireless sensor networks having CR capabilities are wireless cognitive radio sensor networks (WCRSNs). The present work investigates throughput and energy-efficiency of WCRSN. More precisely, scenarios involving both noise and α - μ fading that affect the sensing (S) channels are taken into consideration. Each cognitive radio sensor (CRS) receives an unknown licensed signal information from the primary user (PU). The CRS uses an energy detection sensing technique and makes a local decision in one-bit binary form. The decisions from all the CRSs are forwarded to control center via reporting (R) channels and are combined based on the hard-decision fusion technique for making the final decision about active and inactive status of the PU. Throughput and energy-efficiency performances of WCRSN are assessed while taking into account the effects of pertinent network factors. To that end, first a brand-new mathematical probability of detection that is subject to noise plus generalized α - μ fading is established. Additionally, a computer based simulation and experimental setup are performed to verify the resulting expression. The analytical frameworks for assessing throughput and energy-efficiency performances under a variety of network and channel situations are then developed. Further, the effect of inaccurate S and R channels with channel error probability (q) on the general effectiveness of WCRSN is also examined. Finally, the effects of the α - μ fading parameters, the signal-to-noise ratio, the number of CRSs, and the detection threshold on the WCRSN are examined. For a number of network characteristics, throughput is maximized and energy-efficiency is up to 75% has been achieved. [ABSTRACT FROM AUTHOR]

The emergence of the fifth generation (5G) mobile communication network highly promoted the enhancement of broadband wireless communication. One of the famous physical transmission technologies in regard to wireless communication is orthogonal frequency division multiplexing (OFDM) and the requirements of cognitive radio (CR) met by this OFDM. Spectrum sensing (SS) is a key enabling function in CR to improve utilization spectrum and eases the spectrum resources. Among more other modulation methods, OFDM is broadly utilized in various next generation and current wireless communications systems. This paper enables SS in OFDM based CR using proposed gannet optimization algorithm (GOA). Firstly, simulation is undergone and signal is received from OFDM based CR network. Here, generation of test statistics, such as signal energy, Eigen statistics, matched filter and wavelets done to ensure efficient CR communication without interference. Furthermore, fusion center undergoes fusion process, at which weights determined by proposed GOA and decision is finally processed. GOA diving patterns used for exploring the optimal region with in the search space and then enable exploitation phase to ensure better solution to compute weight vector. This research is evaluated by various performance metrics, such as mean square error and also bit error rate with values of 0.007 and 0.023, correspondingly for SNR of 20 dB with Nakagami fading channel. [ABSTRACT FROM AUTHOR]

Summary: The analysis of spectrum sensing performance of energy detection (ED)‐based wireless cognitive radio sensor network (ED‐WCRSN) with hard‐decision combining (HDC) is presented in this paper. Particularly, several network parameters are derived to estimate the performance of ED‐WCRSN, considering channel errors, noise, and generalized α−μ fading. In the considered network, first, cognitive radio sensor (CRS) senses a primary user (PU), gets sensing data, and then uses an ED to make a local binary decisions about PU's active or inactive status. In both sensing and reporting channels, the channel error probability is also taken into account. Next, HDC technique is used at control center (CC) to combine the locally obtained decisions, and a final decision about the status of the PU is made. To do so, first, the expression for the novel and analytical, which incorporates noise and α−μ fading, detection probability in a CRS is derived and validated using Monte Carlo simulations in MATLAB and using an experimental setup. Then, utilizing derived mathematical expressions, closed‐form expressions of an average error rate (AER), optimal numbers of CRSs, and detection thresholds under noisy and α−μ fading conditions are developed. The substantial influence of channel and network factors is assessed using receiver operating characteristics (ROC), complementary ROC, and AER. Finally, the impacts of channel and network parameters on ED‐WCRSN performance are explored. For numerous parameters of the considered network, the optimal values detection threshold and number of CRSs are also found. [ABSTRACT FROM AUTHOR]

There is an enormous development of wireless communications in the past few years. Due to this frequency spectrum is becoming a very precious resource and insufficiency of the spectrum is a serious problem. So, there is a need to improve the utilization of spectrum effectively. Cognitive radio has demonstrated to be a successful innovation for catching the range, yet additionally for utilizing it. Spectrum sensing can be achieved utilizing a traditional energy identification method that utilizes a single threshold to identify the nearness or nonappearance of the signal, yet whose performance degrades when the SNR is low. To overcome this issue, researchers provide twofold threshold-based spectrum sensing, which looks at the history of sensing in the confusion zone. The test results are compared to these two levels, and a judgement is made on whether main users should be present. In simulations, the suggested double threshold approach outperforms both the double and single threshold detection methods. We investigated spectrum allocation and efficiency using the proposed double threshold method. [ABSTRACT FROM AUTHOR]

This article highlights the potential advantages of the proposed hybrid algorithm for 6G. The algorithm's adaptability addresses the dynamic spectral characteristics of 6G environments, enabling a seamless transition between different spectral conditions. The hybrid approach holds promise for enhancing spectrum utilization, reducing interference, and optimizing overall communication system performance. As 6G technology evolves, the integration of energy detection and cyclostationary spectrum sensing through the hybrid algorithm offers a glimpse into innovative techniques that can shape the future of wireless communication, unlocking the potential for enhanced connectivity, efficiency, and user experience. Several parameters such as the probability of detection (Pd), probability of false alarm (Pfa), bit error rate (BER), and power spectral density (PSD) are compared and analyzed with the conventional spectrum sensing algorithms. We see that the proposed hybrid algorithms obtain a substantial detection performance at the low signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

The exponential growth in wireless communication has resulted in a high demand for novel wireless services within the frequency bands that can deal with the challenge pertaining to the current spectrum shortage. Thus, a 5G-MIMO based CR communication system has been developed to enhance the spectrum by employing a spectrum sensing (SS) algorithm. This recommended SS algorithm, based on the hybrid filter detection (HFD) method, employs the Cosine law for filtering the traffic signal, and then enables segmentation by applying the Welch algorithm. Then, the Hann algorithm is employed to window the traffic signal, and facilitates damping the MIMO impact for different waveforms. This also includes universal filtered multi-carrier (UFMC), filtered-orthogonal frequency division multiplexing (F-OFDM), filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA), and Generalized Frequency Division Multiplexing (GFDM) waveforms. Also, evaluation of the operating parameters such as SNR, the antenna count, the signal span, and power as well as computational complexity was done. Based on the simulation results, a notable achievement was seen for the parameters that were below 0 dB of SNR, <0.09 % global system error probability, >96 % global detection probability, <0.06 % global false alarm probability, and possessing simple complexity. The recommended system's parameters were assessed, which demonstrated its superiority versus other previously developed systems. This algorithm was found to identify all types of 5G signals and not just one type of 5G. [ABSTRACT FROM AUTHOR]

In the recent past, efficient cooperative spectrum sensing and usage are playing a vital role in wireless communication because of the significant progress of mobile devices. There is a recent surge and interest on non-orthogonal multiple access (NOMA) focused on communication powered by wireless mode. In modern research, more attention has been focused on efficient and accurate NOMA. NOMA wireless communication is highly adapted with cognitive radio network (CRN) for improving performance. In the existing CRN, the secondary users could be able to access the idle available spectrum while primary users are engaged. In the traditional CRN, the primary user's frequency bands are sensed as free, the secondary users could be utilized those bands of frequency resources. In this research, the novel methodology is proposed for cooperative spectrum sensing in CRN for 5G wireless communication using NOMA. The higher cooperative spectrum efficiency can be detected in the presence of channel noise. Cooperative spectrum sensing is used to improve the efficient utilization of spectrum. The spectrum bands with license authority primary user are shared by secondary users by simultaneously transmitting information with primary users. The cooperative spectrum sensing provides well under the circumstances that the different channel interference to the primary user can be guaranteed to be negligible than an assured thresholding value. The Noisy channel state information like AWGN and Rayleigh fading channels are considered as wireless transmission mediums for transmitting a signal using multiple-input-multiple-output NOMA to increase the number of users. The proposed NOMA is fascinated with significant benefits in CRN is an essential wireless communication method for upcoming 5G technology. From experimental results it has been proved that the novel methodology performance is efficient and accurate than existing methodologies by showing graphical representations and tabulated parameters. [ABSTRACT FROM AUTHOR]

Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method's purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA- CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA- CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs. [ABSTRACT FROM AUTHOR]

In recent years, the use of wireless communication systems and wireless network services has increased exponentially, causing spectrum scarcity. In addition, fixed spectrum assignment policies further contribute to spectrum scarcity. The application of dynamic spectrum access, which is the basis of Cognitive Radio systems rather than fixed spectrum assignment, plays an important role in solving the spectrum scarcity problem. Dynamic spectrum access means identifying and re-evaluating idle spectrum regions. Empty spectrum regions are determined with the help of suitable detectors, a process known as spectrum detection in the literature. In this study, we propose two new detectors under the assumption of normality with the help of Standard Condition Number and Demmel Condition Number statistics. Theoretical analyses were made, and thresholds obtained for the proposed detectors. To evaluate the performance of the proposed detectors, they were compared with several detectors in the literature (MME, DCN, SNE) in terms of detection performance and detection times. It is clear from the simulation studies that the detection performance of the proposed detectors was better. [ABSTRACT FROM AUTHOR]

Inefficient utilization of the authorized spectrum emerges cognitive radio (CR) as a hopeful technology for both present and future telecommunications. It is owing to the potency to leverage the obtainable bandwidth of other wireless communication networks and thereby increase its occupancy. The key feature for the cognitive radio system for distinguishing the blank spectrum is spectrum sensing. This paper is intended to establish a hybrid sensing model for spectrum detection in CR to enhance the sensing efficiency of traditional techniques of spectrum sensing, which consists of two parallel paths of hybrid detectors. The first path is formed from two sequential detector stages; in the first phase, energy detector is used to recognize the PU signal existence where the signal has not been identified. Maximum–Minimum Eigenvalue (MME) is used as a second stage to detect the PU signal presence. The second path consists of two parallel stage detectors employing separate ED and MME to detect the PU signal individually, the two results are gathered to make a decision, and then the final detection decision is determined based on the two paths' detection combined results. The proposed hybrid sensing approach adopted for enhancing the sensing performance is validated with conventional methods. Simulation results show that the proposed approach outperforms various traditional and hybrid approaches in terms of maximizing the detection probability on the specified limitations on the false alarm probability, as it can increase the detection probability to 94% instead of 79% for the parallel detector at SNR = − 10 dB and Pfa = 0.1. [ABSTRACT FROM AUTHOR]

Cognitive radio has been recommended for enhancing wireless spectrum usage, and an important part of cognitive radio is spectrum sensing. Conventional spectrum sensing techniques rely on energy detection and feature extraction from a received signal at a specific position. The advancement of artificial intelligence and deep learning has provided a chance to increase spectrum sensing efficiency. The proposed investigation presented a hybrid model comprising CNN, BILSTM, and Transformer Networks (TN) for spectrum sensing. With the aid of Transformer networks, the proposed model improves sensing accuracy even for lower SNR signals, particularly at −20 dB. The results of the proposed technique show that CNN-BILSTM-TN enhances spectrum sensing in comparison to earlier models studied in this domain. The proposed approach outperforms prior methods in terms of F1 Score and has an improved probability of detection at −20 dB and a lower likelihood of missed detection. Analysis of ten modulation techniques' performance criteria, such as the Jaccard index, Cohen Kappa coefficient, and Matthew's correlation coefficient, demonstrates that the suggested method's spectrum sensing is better even at lower SNRs. [ABSTRACT FROM AUTHOR]

Cognitive radio is emerging technology in wireless communication, the main approach of cognitive radio is to proper utilization of spectrum, it means some time all the allotted bands are not fully utilized, the unused band can be intelligently utilized by secondary user. In this paper some of the CR techniques are discussed. [ABSTRACT FROM AUTHOR]

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Cognitive Radio (CR) is generally a wireless communication system that has the ability to improve the network’s system-capacity. Since, the white space or temporally unused spectrum are used to enhance the systemcapacity and the important operation involved in the cognition cycle is spectrum sensing. This spectrum sensing supports the Cognitive Radio users to adjust with the environment by identifying the white/vacant spaces without creating any interference to the primary user communication. The traditional filters such as Finite Impulse Response (FIR) filters and median filters used in the spectrum sensing obtains high area utilization in Cognitive Radio. In order to overcome this, an Adaptive Absolute SCORE (AAS) technique is developed based on the FIR for improving the sensing function and radio sensitivity. The area and frequency of the AAS are enhanced by using the Wallace tree multiplier (WTM) and Ladner-Fischer Adder (LFA) in the design of the FIR. The proposed architecture used for the spectrum sensing is named as AAS-WTM-LFA. This AAS-WTM-LFA architecture is developed in the Xilinx tool for different Virtex devices. The performance of AAS-WTM-LFA is analyzed in terms of LUT, slices, flip flops, bonded Input and Output Block (IOB), frequency and power. Additionally, the quality of signal processed through the AAS-WTM-LFA architecture is analyzed as Bit Error Rate (BER) and False Acceptance Rate (FAR). Additionally, the AAS-WTM-LFA architecture is compared with ACS, AAS, AAS-CSLA, AAS-R8-CSA and AASR8-CSLA. The number of LUT for AAS-WTM-LFA architecture is 247 for Spartan 6 device, that is less when compared to the remaining architectures. [ABSTRACT FROM AUTHOR]

Summary: Wireless communication is an emerging technology in recent days which involves the transmission of data or information over a wide range of distance. The wireless network is capable of using the unlicensed spectrum for the transmission of data for various applications like medical, science, and industries. There are cases where the licensed spectrum is not utilized up to the level. In such cases, the cognitive radio network (CRN) technology allows cognitive devices to sense it and further enables the dynamic access of the scarce resource for proper utilization. However, the excessive number of bandwidth access may lead to the occurrence of interferences among the system. This is the major issue faced in all CRNs. To resolve this, effective band scheduling mechanism in CRN has been proposed. In this research, efficient spectrum sensing is performed using stochastic optimization algorithm called Salp Swarm Optimization Algorithm (SSOA). This SSOA algorithm utilizes the best fitness function through three phases such as leaving, contention and joining of bands and provides a list of nonacquired list of bands. From the list of bands, the best band with majority bandwidth has been selected using Round Robin (RR) Algorithm. Here, the load system is allotted dynamically. Based on the load factor, the spectrum is scheduled. As a matter of fact, the whole spectrum scheduling methodology depends primarily on the base contributions made by the operation of spectrum sensing. Finally, the performance analysis is estimated for the throughput, settling time, number of bands occupied by base station (BS), and the bands occupied by each BS. From the performance analysis, the proposed system attains better results than other conventional approaches. [ABSTRACT FROM AUTHOR]

Cognitive Radio based Wireless Sensor Network is a novel concept that integrates the dynamic spectrum access capability of cognitive radio into wireless sensor networks for the futuristic sensor networks and wireless communication technology. Spectrum sensing plays a quintessential role in a cognitive radio network but is a major constraint for a battery powered sensor with stringent energy limitations. The spectrum sensing algorithms are expected to yield acceptable detection probability at low SNR under noise uncertainty with minimum power consumption in a WSN. In this paper, a new spectrum sensing method has been proposed to overcome sensing failure under low SNR environment. The proposed technique is based on adaptive double threshold theory which improves the detection performance by 39.63 and 27.22% at SNR = −10dB as compared to the conventional energy detection and available double threshold-based method respectively. Furthermore, the proposed method of spectrum sensing is evaluated for its deployment into a CR-WSN using the evaluation metrics: Time and Sample Complexity. The comparative evaluation of the spectrum sensing method in a WSN through simulations shows that the proposed technique offers substantial reduction in sample and time complexity of the wireless sensor nodes. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of wireless technologies has increased the need for frequency spectrum usage. To overcome this problem, Cognitive Radio (CR) has emerged as a new technique which enables the use of free ranges in the frequency spectrum. Cognitive radio networks (CRNs) are based on the principle of benefiting from the empty spectrum of secondary users (Sus) by using sensing techniques and analyzing frequency ranges. Although the frequency spectrum is used by various technologies, some frequency ranges are used inefficiently and inadequately. Fixed spectrum allocations cause unused frequency channels in the radio frequency spectrum. Spectrum detection, which is one of the features that cognitive radio applies to reduce the inefficiency of spectrum usage, scans all the primary users (PUs) in the spectrum area and the empty frequency bands. In this study, a method has been proposed to detect unused stationary frequency bands and to use these frequency bands effectively. This method, which is named as matched filter detection, was examined in AWGN channel and its results were evaluated in MATLAB. [ABSTRACT FROM AUTHOR]

In the last years, most researches proved that spectrum holes are not efficiently utilized in wireless communications. Cognitive radio (CR) is an efficient solution to face inefficient utilization of spectrum resources. The key technique, which enables CR to provide efficient utilization of spectrum resources is called spectrum sensing. Spectrum sensing enables a secondary user (SU) to track the activity of the primary user (PU) and the availability of spectrum holes that can be used without any disturbance to the PU. Fixed sensing time schemes give inefficient throughput performance with varying received signal-to-noise ratios (SNRs). So, in this paper, an adaptive sensing time optimization scheme in cooperative CR based on energy detection is investigated with different fusion rules. The proposed scheme adapts the sensing time based on the value of received SNR to maximize the achieved throughput with an acceptable probability of false alarm. The performance of the proposed scheme is investigated with AND, OR, and Marjory fusion rules and compared to those of fixed sensing time schemes. Simulation results show that the proposed scheme significantly enhances the achieved throughput, and reduces the probability of false alarm compared to those of the fixed sensing time schemes. In addition, the proposed scheme provides better performance as the number of SUs increases with the marjory fusion rule. [ABSTRACT FROM AUTHOR]

Spectrum scarcity is an emerging issue in wireless communication systems due to the increasing demand of broadband services like mobile communications, wireless internet access, IoT applications, among others. The migration of analog TV to digital systems (a.k.a. digital TV switchover) has led to the release of a significant spectrum share that can be used to support said additional services. Likewise, TV white spaces emerge as spectral opportunities that can also be explored. Hence, cognitive radio (CR) presents itself as a feasible approach to efficiently use resources and exploit gaps within the spectrum. The goal of this paper is to unveil the state of the art revolving around the usage of TV white spaces, including some of the most important methods developed to exploit such spaces, upcoming opportunities, challenges for future research projects, and suggestions to improve current models. [ABSTRACT FROM AUTHOR]

In wireless communication systems, the correlation among the antennas of diversity systems can hardly be neglected. To the best of our knowledge, no previous studies have attended to address, thoroughly, this problem for the cross-correlation-based detectors. This will be the topic of this paper. Namely, in this paper, we consider the performance assessment of a second-order decision metric that is based on the cross correlation of antenna's output. The detector performance analysis has been made concerning the receiver operating characteristic curves, the robustness to estimation uncertainty, and the design parameters. This last introduces the minimum required sample number that guarantees a given detection performance. To this end, the probability distribution of the inner product of two complex non-zero-mean partially correlated random vectors is investigated. Next, we exhibited the relevance of our results through the analytical performance derivation of the cross-correlation-assisted sensing approach in terms of design parameters, spectral efficiency, and decision reliability. Monte Carlo simulations are achieved to prove the tightness of our derived results. [ABSTRACT FROM AUTHOR]

Green wireless communication has drawn ample interest in the 5G cellular networks. In this article, we investigate an energy‐efficient power allocation with spectrum sensing for cognitive radio (CR) networks. The optimization problem is formulated as a ratio of the sum rate (SR) to the total power consumption in the signal transmission and spectrum sensing subject to the total power constraint. Because the original optimization problem is nonconvex, we adopt Dinkelback's method and propose an optimal energy‐efficient power allocation scheme that iteratively improves the energy efficiency (EE) performance of the CR network and finally gives an optimal solution. In addition, an expression for the optimal solution is derived through the dual‐decomposition method, and its performance is validated through simulation results. The impact of the probability of detection (PD) and the probability of PU activeness (PA) on the achievable average EE and SR is demonstrated when the CR network is designed from the energy‐efficient perspective. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications. [ABSTRACT FROM AUTHOR]

Recently, cognitive radio (CR) is viewed as a novel approach for improving the utilization of a radio spectrum. The cognitive radio is defined as an intelligent wireless communication system that is aware of its surrounding and uses the technique of understanding-by-learning from the environment and adapt to statistical variations in the input stimuli. Spectrum sensing is a fundamental component in a cognitive radio. This paper analyses the performance of the artificial bee colony algorithm (ABC), optimization in different fading environments. [ABSTRACT FROM AUTHOR]

Accurate detection of spectrum holes is the most important and critical task in any cognitive radio (CR) communication system. When a single spectrum sensor is assigned to detect a specific primary channel, then the detection may be unreliable because of noise, random multipath fading and shadowing. Also, even when the primary channel is invisible at the CR transmitter, it may be visible at the CR receiver (the hidden primary channel problem). With a single sensor per channel, a high and consistently uniform level of sensitivity is required for reliable detection. These problems are solved by deploying multiple heterogeneous sensors at distributed locations. The proposed spectrum hole detection method uses cooperative sensing, where the challenge is to properly assign sensors to different primary channels in order to achieve the best reliability, a minimum error rate and high efficiency. Existing methods use particle swarm optimization, the ant colony system, the binary fire y algorithm, genetic algorithms and non-linear mixed integer programming. These methods are complex and require substantial pre-processing. The aim of this paper is to provide a simpler solution by using simpler binary integer programming for optimal assignment. Optimal assignment minimizes the probability of interference which is a non-linear function of decision variables. We present an approach used to linearize the objective function. Since multiple spectrum sensors are used, the optimal constrained assignment minimizes the maximum of interferences. While performing the optimization, the proposed method also takes care of the topological layout concerned with channel accessibility. The proposed algorithm is easily scalable and exible enough to adapt to different practical scenarios. [ABSTRACT FROM AUTHOR]

Cognitive radio systems necessitate the incorporation of cooperative spectrum sensing among cognitive users to increase the reliability of detection. We have found that cooperative spectrum sensing is not only advantageous, but is also essential to avoid interference with any primary users. Interference by licensed users becomes a chief concern and issue, which affects primary as well as secondary users leading to restrictions in spectrum sensing in cognitive radios. When the number of cognitive users increases, the overheads of the systems, which are meant to report the sensing results to the common receiver, which becomes massive. When the spectrum, which is in use becomes unavailable or when the licensed user takes the allocated band, these networks have the capability of changing their operating frequencies. In addition, cognitive radio networks are seen to have the unique capability of sensing the spectrum range and detecting any spectrum, which has been left underutilized. Further this capability of recognizing the spectrum range based on the dimensions detected, allows for determination of the band, which may be utilized. The main objective of this paper is to analyze the cognitive radio's spectrum sensing ability and evolving a self-configured system with dynamic intelligence networks without causing any interference to the primary user. The paper also brings focus to the quantitative analysis of the two spectrum sensing techniques namely; Energy Detection and Band Limited White Noise Detection. The estimation technique for detecting spectrum noise is based on the detection of probability and probability of false alarms at different Signal-to-Noise Ratio (SNR) levels using Additive White Gaussian Noise signal (AWGN). The efficiency of the proposed Cooperative CUSUM spectrum sensing algorithm performs better than existing optimal rules based on a single observation spectrum sensing techniques under cooperative networks. [ABSTRACT FROM AUTHOR]

It is well-known that cognitive radio (CR) is a fundamental solution to mitigate the spectrum scarcity of today's wireless communication systems. For the realization of CR, there are many obstacles to resolve, including technical and policy perspectives. Since CR is a very sensitive technology, it is particularly important to test the scheme with appropriate operation in a real environment. In this regard, we have developed a testbed of a regional spectrum sensing based CR system for digital TV (DTV) white space. The developed system has a centralized spectrum sensor, and according to the sensing results, mobile WiMAX system provides high quality multimedia service through the spectrum hole in DTV band. There is also a fast frequency hopping capability depending on the sensing results to minimize interference to the actual licensee, the primary DTV system. The performance analysis shows that the developed CR system has excellent performance satisfying the IEEE 802.22 requirements, and is suitable for use in the real environment. [ABSTRACT FROM AUTHOR]

This correspondence proposes a novel multiple-antenna linearly weighted (MALW) spectrum sensing method with random arrivals of primary users (PUs) for cognitive users. The method first constructs a local test statistic for each antenna based on the assumption that the PU is randomly arriving, and then calculates the estimated optimum coefficient for every local test statistic and finally gets the total test statistic by linearly combing the local test statistics and the estimated optimum coefficients. Given false alarm probability, the proposed method calculates the corresponding threshold according to Neyman–Pearson criteria and makes the final decision by comparing the total test statistic with the threshold. Simulation results prove the validity and the superiority of the proposed MALW method when the PU is arriving randomly during the spectrum sensing period. [ABSTRACT FROM AUTHOR]

With the recent emergence and its wide spread applicability Internet of Things (IoT) is placing pressure on network resources and most importantly on availability of spectrum. Spectrum scarcity is the issue to be addressed in networking within IoT. Cognitive radio is the technology which addresses the problem of spectrum scarcity in an efficient way. Equipping the IoT devices with cognitive radio capability will lead to a new dimension called cognitive radio enabled IoT devices. To achieve ON-demand IoT solutions and interference free communications cognitive radio enabled IoT devices will become an effective platform for many applications. As there is high dynamicity in availability of spectrum it is challenging for designing an efficient routing protocol for secondary users in cognitive device networks. In this work we are going to estimate spectrum quality and spectrum availability based on two parameters called global information about spectrum usage and instant spectrum status information. Enhanced energy detector is used at each and every node for better probability of detection. For estimating spectrum quality and availability we are introducing novel routing metrics. To have restriction on the number of rerouting and to increase the performance of routing in our proposed routing metric only one retransmission is allowed. Then, two algorithms for routing are designed for evaluating the performance of routing and we find that the bit error rates of proposed algorithms (nodes are dynamic) have decreased a lot when compared to conventional methods (nodes are static) and throughput of proposed algorithm also improved a lot. [ABSTRACT FROM AUTHOR]

This paper considers coded multichannel cooperative spectrum sensing (MC-CSS) employing local Neyman–Pearson testing at each sensor and channel decoding integrated with fusion-based decision. A joint multichannel decoding and decision fusion (JMCDDF) algorithm for performing a likelihood ratio test at the fusion center is derived based on the belief propagation technique. For benchmark comparison, we also derive the analytical performance of MC-CSS with error-free reporting channels that do not require channel coding, employing equal decision thresholds at each sensor. Using the JMCDDF algorithm, we compare the performance between uncoded and coded MC-CSS schemes when applying low-density parity-check (LDPC) codes in the presence of reporting channel errors. Monte–Carlo simulation results show considerable performance gains when using the proposed coded MC-CSS schemes. A 3-dB saving in link budget can be achieved by such coded MC-CSS schemes with a short (3, 6) regular LDPC code of codeword length $n_c=\text{200}$ , over uncoded MC-CSS. Finally, it is shown that in some cases, protecting the primary user (PU) channels by using variable nodes of higher degrees improves the performance. To equalize the performance for all sensed PU channels, we introduce a simple permutation technique, where, in each transmission, different variable nodes are used to protect local decisions. [ABSTRACT FROM AUTHOR]

Spectrum sensing is an important technological requirement in the quest to realize dynamic spectrum access (DSA) in today’s wireless world. Cognitive radio (CR) has been identified as an enabling technology that will considerably mitigate the effect of spectrum underutilization and cushion spectrum scarcity. But for this to happen, fast and accurate sensing technique must be developed. Quite a number of spectrum sensing techniques are available in literature, but these are not without inherent short comings. Recently, applications of wavelet techniques for spectrum sensing is receiving attention in the research community, this is attributed to its unique ability to operate both in the time and frequency domains and its suitability for wideband sensing. This paper takes a general look at the applications of wavelets in solving problems in science and engineering and then focused on its recent applications in spectrum sensing. Besides discussing the general spectrum sensing techniques in literature, the paper also discussed wavelet-based spectrum sensing, and its variants; pointing out the merits and limitations of each. It noted that, like any other sensing technique, wavelet-based technique has its strengths and weaknesses, hence, the advantages and disadvantages of this technique are also highlighted. Also, wavelet techniques in spectrum sensing was variously compared with existing wavelet sensing techniques; other spectrum sensing techniques; and existing wideband sensing techniques. Emerging research trends involving wavelets in wireless communications systems design are discussed while some challenges posed by wavelet techniques are mentioned. The paper is intended to provide necessary information and serve as a pointer to relevant literatures for researchers seeking information about wavelets and their applications in science and engineering and particularly in spectrum sensing for CR. [ABSTRACT FROM AUTHOR]

The rapid growth of wireless technology demands to employ the available spectrum efficiently. In this work, an Energy Detector is designed to be used in a Cognitive Radio scenario using a platform based on Software Defined Radio principles. The design of this detector is developed by using Simulink and Xilinx System Generator MATLAB software to be implemented on a Field Programmable Gate Array (FPGA) device. To sense the spectrum, the energy detector (ED) is the most used approach due to its low computational complexity. Furthermore, ED offers the ability to identify spectrum holes without prior knowledge regarding transmission characteristics of primary users. However, setting a threshold for energy detection requires to estimate noise power, which can be established by appropriate estimation methods. In this regard, a new method is proposed and implemented on FPGA to establish a threshold and detect properly the available spectrum. Results obtained reveal a proper performance of the proposed detector given by and Pd > 0.9 and Pfa = 0.1, respectively, in the SNR range [-8, 15] dB. On the other hand, Digital Television in Cuba requires a robust and efficient automatic signal detection method to identify Television white spaces. Cognitive radio users would use these spectral holes to increase bandwidth and improve connectivity for wireless communications applications. To this end, this work is aimed to detect white spaces of the Digital Television spectrum using energy detector method. Simulation results show that the detector performs correctly on this scenario. [ABSTRACT FROM AUTHOR]

Cognitive radio is an intelligent wireless solution that aims to enhance the access to the radio spectrum. In this technology, secondary users sense the radio spectrum, select the best channel among a pool of free channels, and determine the optimal transmission parameters to meet their quality-of-service requirements while maximizing the spectral efficiency. Over the past decade, several channel-ranking mechanisms have been proposed. However, these mechanisms consider only the remaining idle time of the channel and exclude some crucial parameters. This convincingly demonstrates a strong need for a new channel quality-ranking model that jointly considers several parameters to select the best communication channel for transmission. This paper proposes a utility model that integrates several important parameters for ranking channels. First, we underline the importance of the process of the channel quality ranking. Then, we describe a multi-attributes, utility-based, channel quality-ranking model. Finally, we describe a series of experiments and their results, which show that our model effectively ranks the best communication channels first. [ABSTRACT FROM AUTHOR]

Under the energy detection scheme based cognitive radio (CR) system, the process of spectrum sensing is of high importance. The sensing performance of CR primarily depends on two important parameters namely, the sensing time τ and the reference threshold λ. In order to achieve a goal where the CR system obtains a high value of throughput and simultaneously ensures a sufficient level of protection to the licensed users, the values of these parameters can neither be too high nor too low, so proper settings of their values is of prime concern. However, under these constraints on choosing a particular value of τ and λ, it is challenging for CR to fulfill this goal. In this paper we propose a CR system which operates under the scheme of double threshold to ensure a sufficient protection required by the licensed users and also makes an efficient utilization of the confusion region to improve its achievable throughput. It is observed that, under the proposed approach, the CR system achieves better throughput than the CR system based on the single threshold and also to the conventional double threshold based CR system where confusion region is used based on the results of sensing performed in the next sensing rounds. We further study the problem of optimizing the sensing duration to maximize the throughput of the proposed CR system. We formulate the sensing-throughput tradeoff problem mathematically and prove that, the formulated problem indeed has an optimal sensing duration where throughput of the CR system is maximized. [ABSTRACT FROM AUTHOR]

Primary User Emulation (PUE) attack is a type of Denial of Service (DoS) attack in Cognitive Wireless Sensor Network (CWSN), where malicious secondary users (SU) try to emulate primary users (PU) to maximize their own spectrum usage or obstruct other SU from accessing the spectrum. In this paper, we have designed an application to monitor the SU's behavior with respect to the CWSN normal behavior profile towards it's one hop neighbor. Abnormal behavior towards PUE attack of any SU helps us to identify PUE attackers in the network. Our application does not require extensive computational capabilities and memory and therefore suitable for resource constraint cognitive sensor nodes. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of spectrum sensing based on energy detection method in cognitive radio over wireless communication channels when users experiences fading and nonfading effects. The closed-form analytical expressions for the detection probability are derived over nonfading additive white Gaussian noise channel, Rayleigh, Rician and Nakagami-m fading channels. The detection probability involving Marcum-Q function is replaced by closed-form expression. The probability distribution function of fading channels is used to obtain the expressions for detection probability. The new derived numerical results are simulated under various parameters. [ABSTRACT FROM AUTHOR]