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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]

Spectrum sensing is recognized as a viable strategy to alleviate the scarcity of spectrum resources and to optimize their usage. In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) and quadrature (Q) component signals, we propose a multi-scale time-correlated perceptual attention model named MSTC-PANet. The model consists of multiple parallel temporal correlation perceptual attention (TCPA) modules, enabling us to extract features at different timescales and identify dependencies among features across various timescales. Our simulations show that MSTC-PANet significantly improves the detection of channel occupancy at low signal-to-noise ratios (SNR), particularly in untrained scenarios with lower SNR conditions and modulation uncertainties. The analysis of the ROC curve indicates that at an SNR of -20 dB, the proposed MSTC-PANet achieves a detection rate of 98% with a false alarm rate of 10%. Furthermore, MSTC-PANet, which has been trained using digital modulation techniques, also demonstrates applicability to analog modulation. [ABSTRACT FROM AUTHOR]

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for applying the p-ary m-sequence as a channel-searching pattern for rendezvous in the asymmetric channel model of cognitive radio. We mathematically analyzed and calculated the ETTR when the m-sequence is applied to the conventional scheme, and our simulation results demonstrated that the ETTR performance is significantly better than that of the JS algorithm. Furthermore, we introduced a new channel-searching scheme that maximizes the benefits of the m-sequence and proposed a method to adapt the generation of the m-sequence for use in the newly proposed scheme. We also derived the ETTR mathematically for the new scheme with the m-sequence and showed through simulations that the performance of the new scheme with the m-sequence is superior to that of the conventional scheme with the m-sequence. Notably, when there is only one common channel, the new scheme with the m-sequence achieved approximately four times the improvement in the ETTR compared to the conventional scheme. [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]

The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new technologies require faster data rates and reduced latency. This study explores hybrid spectrum sensing techniques, combining matched filter (MF) energy detection (ED) and an equal-gain combining-based energy detection Neyman-Pearson threshold estimation technique (EGC-ED-PTh) to enhance waveform detection accuracy in complex environments. The proposed method offers an enhanced signal-to-noise ratio (SNR) by optimizing the detection performance, particularly in low-SNR environments, thereby improving the signal reliability. The proposed algorithms are evaluated in comparison with traditional SS methods, including ED, MF, and cyclostationary feature detection (CFD). Additionally, characteristics including bit error rate (BER), power spectral density (PSD), probability of detection (pd), and probability of false alarm (pfa) were researched and evaluated for 500 and 1000 samples. The simulation findings show that the projected algorithms perform better than the traditional algorithms with minimum sidelobes of – 3024 and pfa effects and achieve a throughput gain of 5 and 4.7 dB compared with the conventional algorithms.

Abstract This study introduces a novel hybrid deep learning model featuring a dendritic layer for enhancing the performance of automatic modulation recognition (AMR). By replacing the fully connected layer, the proposed model demonstrates superior classification accuracy in AMR tasks. Comparative experiments with nine state-of-the-art deep learning models on the RadioML2016.10a dataset reveal its consistent superiority. Statistical analyses, including the Friedman test and Wilcoxon signed-rank test, confirm the significant advantage of the HDM-D model.

Abstract Spectrum sensing is a key technology and prerequisite for Transform Domain Communication Systems (TDCS). The traditional approach typically involves selecting a working sub-band and maintaining it without further changes, with spectrum sensing being conducted periodically. However, this approach presents two main issues: on the one hand, if the selected working band has few idle channels, TDCS devices are unable to flexibly switch sub-bands, leading to reduced performance; on the other hand, periodic sensing consumes time and energy, limiting TDCS’s transmission efficiency. In contrast to previous studies that unrealistically modeled the problem as a Markov Decision Process (MDP), this study accounts for the fact that TDCS devices cannot fully observe the entire spectrum state and must rely on historical observations, along with the current state of sub-bands, to make informed decisions. We innovatively model this as a Partially Observable Markov Decision Process (POMDP). Moreover, we consider both the number of skipped time slots and the selection of idle sub-bands, establishing distinct termination conditions for each action. By assigning different weights to balance sensing overhead and spectrum utilization while reducing conflicts, the algorithm’s adaptability and performance are improved. To address the Q-value overestimation problem inherent in traditional Deep Recurrent Q-Network (DRQN) due to the use of a single network, we propose a DDRQN-BandShift strategy that combines Double Deep Q-Network (DDQN) and DRQN. Simulation results show that the proposed scheme significantly improves TDCS transmission efficiency while effectively reducing sensing costs.

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 The wireless Internet of Things (IoT) is widely used for data transmission in power systems. Wireless communication is an important part of the IoT. The existing modulation classification algorithms have low classification accuracy when facing strong electromagnetic interference, which causes decoding error link interruption and wastes wireless channel resources. Therefore, it is necessary to study signal modulation classification methods in a low signal‐to‐noise ratio (SNR) environment. In this paper, a lightweight Deep Neural Networks (DNNs) modulation classification method based on the Informer architecture classifier and two‐dimensional (2‐D) curves input of the spectral correlation function (SCF) is proposed, which uses in‐phase and quadrature (I/Q) signals to generate 2‐D cross‐section SCF curve first and then feeds the feature curve into the Informer network to classify the modulation method. This model can better learn the robustness characteristics in a long sequence. Through testing, the classification accuracy of the modulation signal is not much lower than that of the current good classification method when the SNR is 10 dB, and this method can still show higher accuracy when hardware resources are limited. It is a compact design of a modulation classification model and easy to deploy on low‐cost embedded platforms.

Spectrum sensing is a key technology and prerequisite for Transform Domain Communication Systems (TDCS). The traditional approach typically involves selecting a working sub-band and maintaining it without further changes, with spectrum sensing being conducted periodically. However, this approach presents two main issues: on the one hand, if the selected working band has few idle channels, TDCS devices are unable to flexibly switch sub-bands, leading to reduced performance; on the other hand, periodic sensing consumes time and energy, limiting TDCS's transmission efficiency. In contrast to previous studies that unrealistically modeled the problem as a Markov Decision Process (MDP), this study accounts for the fact that TDCS devices cannot fully observe the entire spectrum state and must rely on historical observations, along with the current state of sub-bands, to make informed decisions. We innovatively model this as a Partially Observable Markov Decision Process (POMDP). Moreover, we consider both the number of skipped time slots and the selection of idle sub-bands, establishing distinct termination conditions for each action. By assigning different weights to balance sensing overhead and spectrum utilization while reducing conflicts, the algorithm's adaptability and performance are improved. To address the Q-value overestimation problem inherent in traditional Deep Recurrent Q-Network (DRQN) due to the use of a single network, we propose a DDRQN-BandShift strategy that combines Double Deep Q-Network (DDQN) and DRQN. Simulation results show that the proposed scheme significantly improves TDCS transmission efficiency while effectively reducing sensing costs. [ABSTRACT FROM AUTHOR]

This study introduces a novel hybrid deep learning model featuring a dendritic layer for enhancing the performance of automatic modulation recognition (AMR). By replacing the fully connected layer, the proposed model demonstrates superior classification accuracy in AMR tasks. Comparative experiments with nine state-of-the-art deep learning models on the RadioML2016.10a dataset reveal its consistent superiority. Statistical analyses, including the Friedman test and Wilcoxon signed-rank test, confirm the significant advantage of the HDM-D model. [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 order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have recently demonstrated promise in improving the precision and efficacy of spectrum sensing. Our research introduces a groundbreaking approach to spectrum sensing by leveraging convolutional neural networks (CNNs) to significantly advance the precision and effectiveness of identifying unused frequency bands. We treat spectrum sensing as a classification task and train our model with diverse signal types and noise data, enabling unparalleled adaptability to novel signals. Our method surpasses traditional techniques such as the maximum–minimum eigenvalue ratio-based and frequency domain entropy-based methods, showcasing superior performance and adaptability. In particular, our CNN-based approach demonstrates exceptional accuracy, even outperforming established methods when faced with additive white Gaussian noise (AWGN). [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has experienced rapid growth in various applications, resulting in significant advancements that exhibit considerable variations in characteristics and requirements. Cognitive radio networks (CRNs) present a promising solution for ultra-reliable communication and dynamic spectrum sharing among IoT devices in 6G environment. The most critical task in CRNs is to identify unused spectrum opportunities, known as holes, across different times and locations. Addressing this challenge requires an effective spectrum sensing strategy at the medium access control layer to optimize spectrum use while minimizing interference with licensed user signals. In this paper, we have proposed a novel dynamic spectrum access scheme, which aims to address both spectrum availability and network reliability for various secondary user flows in IoT-centric CRNs. Our study examines the effect of random channel failure and their recovery on the performance of CRN. Moreover, we develop a continuous-time Markov chain model to examine the network performance across various key performance indicators (KPIs) in the presence of multiple channel failures and sensing errors. This analysis helps identify valuable trade-offs among the KPIs. [ABSTRACT FROM AUTHOR]

The escalating proliferation of connected devices underscores the imperative for sophisticated spectrum management. Cognitive radio networks (CRNs) dynamically allocate unused spectrum portions to optimize network efficiency and meet specific application demands, enhancing overall quality of service (QoS). This study explores the complex domain of minimizing costs based on Quality of Service (QoS) in a Cognitive Radio Network (CRN). It addresses the difficulties arising from a dynamic real-world network environment characterized by fluctuating channel conditions, imperfect spectrum sensing, collision constraints with primary users, power allocation to secondary users, the preservation of QoS, and the maximization of data rates. The proposed real-time scheduling algorithm, integrating an orthogonal frequency division multiplexing based CRN operator with a water-filling power allocation mechanism, represents a significant advancement to achieve the objectives. Simulation results illustrate the algorithm's efficacy in minimizing total costs for CRN operators while ensuring superior QoS compared to prevailing sensing-only and leasing-only policies, thereby demonstrating a noteworthy enhancement in the existing cost minimization paradigm, maintaining QoS, imperfect spectrum sensing, collision constraint, power allocation, spectral efficiency, scalability and rate maximization. Additionally, the performance of the proposed approach is compared with the existing scenario of [48]. [ABSTRACT FROM AUTHOR]

Graph‐based spectrum sensing in noisy environments has major implications for civilian and military signal processing applications. However, existing algorithms suffer from high computational complexity and performance deterioration at low signal‐to‐noise ratios (SNRs). Therefore, a spectrum‐sensing algorithm based on graph feature fusion using a quadratic form derived from self‐loop weights and the graph Laplacian matrix is proposed in this study. The sum of the first and second block maxima of the power spectrum of the observed signal is selected as the input to the graph converter. Self‐loop weights are combined with the Laplacian matrix to construct the graph quadratic form, which serves as the test statistic for decision‐making. By applying majorisation and the extreme value theory, it is demonstrated that the proposed algorithm outperforms existing methods. The simulation results confirm the robust spectrum‐sensing performance across various signal modulation types and pulse shapes. Thus, compared to existing algorithms, except block range‐ and energy‐detection‐based methods, the proposed algorithm demonstrates the best spectrum‐sensing performance under low SNRs and channel‐fading conditions, while achieving the lowest computational complexity. The proposed approach enables more efficient and accurate spectrum sensing, fostering advancements in communication technologies and defence applications. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is an effective method to optimize the use of spectrum resources. Spectrum sensing plays significant role in finding free channels that can be used with CR. the most important One of issues in CR is interference to the primary user (PU). Consequently, there is a significant research gap for an algorithm that distributes channels dynamically and takes into consideration all kinds of interference.This study looks at a universal optimal voting rule to an algorithm to determine the optimum parameters λ opt , n opt that minimize the Bayes risk cost function which has different weights for the probability of false alarm Q f and the missing probability Q m in general. As discussed formerly, Q m is most important from Q f . On another side, an algorithm that determines other technique which gives a large priority for Q m over the Q f . In this technique, we determine the optimum parameters λ opt , n opt , Supposing that secondary users (K) are fixed, i.e. what is the optimum fusion rule n opt and optimum threshold λ opt that minimizes Q f with constraint on Q m .In this study, we introduced a technique that ensures Qm ​ remains below a specified threshold while minimizing Qf as quickly as possible. We developed an algorithm to determine the optimal fusion rule and threshold that minimize Qf while keeping Qm​below the given threshold. The performance of cooperative spectrum sensing was analyzed using hard combination fusion rules. Among these, the OR rule showed superior performance in cooperative spectrum sensing for Cognitive Radio, outperforming the AND rule. [ABSTRACT FROM AUTHOR]

The wireless Internet of Things (IoT) is widely used for data transmission in power systems. Wireless communication is an important part of the IoT. The existing modulation classification algorithms have low classification accuracy when facing strong electromagnetic interference, which causes decoding error link interruption and wastes wireless channel resources. Therefore, it is necessary to study signal modulation classification methods in a low signal‐to‐noise ratio (SNR) environment. In this paper, a lightweight Deep Neural Networks (DNNs) modulation classification method based on the Informer architecture classifier and two‐dimensional (2‐D) curves input of the spectral correlation function (SCF) is proposed, which uses in‐phase and quadrature (I/Q) signals to generate 2‐D cross‐section SCF curve first and then feeds the feature curve into the Informer network to classify the modulation method. This model can better learn the robustness characteristics in a long sequence. Through testing, the classification accuracy of the modulation signal is not much lower than that of the current good classification method when the SNR is 10 dB, and this method can still show higher accuracy when hardware resources are limited. It is a compact design of a modulation classification model and easy to deploy on low‐cost embedded platforms. [ABSTRACT FROM AUTHOR]

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Abstract Compressive sensing (CS) has been widely used to sense the wideband spectrum with fewer measurements by taking advantage of radio spectrum underutilization. As new smart devices, such as IoT devices, smart home devices, and wearables, use batteries and have limited memory, more research is needed to reduce the overuse of cognitive radio (CR) resources through spectrum sensing. To reduce the number of compressive measurements required for spectrum recovery, researchers proposed approaches like weighted and sequential compressive sensing. In this paper, we estimate the primary user’s (PU) behavior statistics and use the estimated information in a novel weighted sequential compressive spectrum sensing approach. Our proposed approach can reduce and adapt the number of measurements and the sensing time to the changing number of active channels in a dynamically changing wideband spectrum.

This paper addresses the problem of noncooperative spectrum sensing in very low signal-to-noise ratio (SNR) conditions. In our approach, detecting an unoccupied bandwidth consists of detecting the presence or absence of a communication signal on this bandwidth. Digital communication signals may contain hidden periodicities, so we use Recurrence Quantification Analysis (RQA) to reveal the hidden periodicities. RQA is very sensitive and offers reliable estimation of the phase space dimension m or the time delay τ. In view of the limitations of the algorithms proposed in the literature, we have proposed a new algorithm to simultaneously estimate the optimal values of m and τ. The new proposed optimal values allow the state reconstruction of the observed signal and then the estimation of the distance matrix. This distance matrix has particular properties that we have exploited to propose a Recurrence-Analysis-based Detector (RAD). The RAD can detect a communication signal in a very low SNR condition. Using Receiver Operating Characteristic curves, our experimental results corroborate the robustness of our proposed algorithm compared with classic widely used algorithms.

Compressive sensing (CS) has been widely used to sense the wideband spectrum with fewer measurements by taking advantage of radio spectrum underutilization. As new smart devices, such as IoT devices, smart home devices, and wearables, use batteries and have limited memory, more research is needed to reduce the overuse of cognitive radio (CR) resources through spectrum sensing. To reduce the number of compressive measurements required for spectrum recovery, researchers proposed approaches like weighted and sequential compressive sensing. In this paper, we estimate the primary user's (PU) behavior statistics and use the estimated information in a novel weighted sequential compressive spectrum sensing approach. Our proposed approach can reduce and adapt the number of measurements and the sensing time to the changing number of active channels in a dynamically changing wideband spectrum. [ABSTRACT FROM AUTHOR]

To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time Fourier transform (STFT) and residual attention dense network (RADN). Specifically, the RADN model improves the basic residual block and introduces the convolutional block attention module (CBAM), combining residual connections and dense connections to form a powerful deep feature extraction structure known as residual in dense (RID). This significantly enhances the network's feature extraction capabilities. By performing STFT on the received signals and normalizing them, the signals are converted into time–frequency spectrograms as network inputs, better capturing signal features. The RADN is trained to extract abstract features from the time–frequency images, and the trained RADN serves as the final classifier for spectrum sensing. Experimental results demonstrate that the STFT-RADN spectrum sensing method significantly improves performance under low signal-to-noise ratio (SNR) conditions compared to traditional deep-learning-based methods. This method not only adapts to various modulation schemes but also exhibits high detection probability and strong robustness. [ABSTRACT FROM AUTHOR]

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by employing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Naïve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non-orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyse the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy. The performance of proposed system is compared with the literature and shown a significant improvement that proves the evidence of our findings. [ABSTRACT FROM AUTHOR]

To solve the problem that the available spectrum resources of L-band digital aeronautic communication system (LDACS) are limited and vulnerable to interference from high-power distance measuring equipment (DME) signals, a spectrum sensing method based on reduced dimension cyclic spectrum and residual neural network is proposed. Firstly, the cyclic spectrum characteristics of DME signal are analyzed theoretically. Then Fisher discriminant rate (FDR) is used to extract the vector with the highest cycle frequency energy, and the pre-processing features are enhanced by principal component analysis (PCA). Finally, the process of combining the cyclic spectral vector and convolutional neural network after data processing is given, and the effective detection of DME signal is achieved. Simulation results show that the method in not sensitive to noise, and the average detection probability is greater than 90% when the signal-to-noise ratio is no less than -15 dB. When the signal-to-noise ratio is not less than -14 dB, the detection probability is dose to 100%. [ABSTRACT FROM AUTHOR]

Cognitive radio networks enable the detection and opportunistic access to an idle spectrum through spectrum-sensing technologies, thus providing services to secondary users. However, at a low signal-to-noise ratio (SNR), existing spectrum-sensing methods, such as energy statistics and cyclostationary detection, tend to fail or become overly complex, limiting their sensing accuracy in complex application scenarios. In recent years, the integration of deep learning with wireless communications has shown significant potential. Utilizing neural networks to learn the statistical characteristics of signals can effectively adapt to the changing communication environment. To enhance spectrum-sensing performance under low-SNR conditions, this paper proposes a deep-learning-based spectrum-sensing method that combines multiple signal features, including energy statistics, power spectrum, cyclostationarity, and I/Q components. The proposed method used these combined features to form a specific matrix, which was then efficiently learned and detected through the designed 'SenseNet' network. Experimental results showed that at an SNR of −20 dB, the SenseNet model achieved a 58.8% spectrum-sensing accuracy, which is a 3.3% improvement over the existing convolutional neural network model. [ABSTRACT FROM AUTHOR]

Cognitive Radio Networks (CRNs) have the potential to improve energy efficiency and spectrum usage through the integration of Wireless Energy Harvesting (WEH) with spectrum sharing. According to the findings of this research, a unique Deep Reinforcement Learning (DRL) approach has been developed with the intention of increasing the availability of spectrum and making it possible for secondary users to effectively harvest energy. By dynamically adjusting spectrum sharing decisions in accordance with the energy availability of secondary users, the DRL model enhances network performance. This model places an emphasis on the essential aspect of "harvested energy." The findings of the simulation indicate that the DRL-based technique that was proposed greatly enhances the efficacy and throughput of CRNs, which highlights the potential of this approach for conducting wireless communication over extended periods of time. [ABSTRACT FROM AUTHOR]

The identification of the presence of primary user enhances the spectrum efficiency in cognitive radio (CR). The studies suggested that the existence of malicious user adversely affects the system performances; especially the primary user emulation attack (PUEA) has a greater influence in spectrum sensing on the CR network. Moreover, the detection of PUEA is a challenging and complex task and involves constructive design with sensing algorithm. In this study, a support vector machine (SVM) along with energy vectors is designed to improve the spectrum sensing mechanism. The presented approach integrates the SVM with the Bayesian optimization algorithm (BOA) in which SVM aims to detect the malicious user by randomly selecting the primary and secondary users. The BOA aims to optimize the hyperparameters of the SVM, thereby improving the detection performances and maximizes the algorithms convergence speed. The experimental analysis illustrate that the presented approach predicts the PUEA with 98% accuracy and reduces the average node power is 9.7. Moreover, the results demonstrated that the system performance does not vary on implementing it with the large-scale CR network. Finally, the system performances are compared and evaluated with existing techniques in terms of accuracy, and average noise power. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) offer a promising solution for improving spectrum utilization. However, ensuring quality of service (QoS) for heterogeneous secondary users (SUs) during spectrum handoff, particularly under high primary network traffic, poses challenges. This study develops a Markov-based analytical model to evaluate the gain of a non-switching spectrum handoff technique using a hybrid interweave-underlay spectrum access strategy, considering sensing errors. The proposed model assesses the effects of the hybrid spectrum access method for prioritized traffic across multiple SU classes, aiming to meet QoS requirements for delay-sensitive traffic. The study examines the CRN's short-term behavior and realistic queueing scenarios by analyzing the system's transient dynamics. Different spectrum access methods are compared for evaluation purposes. The analysis focuses on evaluating the effectiveness of the enhanced hybrid spectrum access scheme compared to individual interweave and hybrid interweave-underlay spectrum access strategies in terms of QoS provisioning for heterogeneous SUs. The results demonstrate increased throughput and improved spectrum utilization with the suggested scheme, affirming its suitability for satisfying QoS requirements for both delay-sensitive and delay-tolerant users. [ABSTRACT FROM AUTHOR]

In order to deal with the difficulty of spectrum sensing in cognitive satellite wireless networks, a large-scale cognitive network spectrum sensing algorithm based on big data analysis theory is studied, and a new algorithm using mean exponential eigenvalue is proposed. This new approach fully uses all the eigenvalues in sample covariance matrix of the sensing results to make the decision, which can effectively improve the detection performance without obtaining the prior information from licensed users. Through simulation, the performance of various large scale cognitive radio spectrum sensing algorithms based on big data analysis theory is compared, and the influence of satellite to ground channel conditions and the number of sensing nodes on the performance of the algorithm is discussed. [ABSTRACT FROM AUTHOR]

The spectrum sensing function plays a significant role in the performance of cognitive radio (CR). Spectrum sensing specifies if free channels exist and identifies free channels for secondary users, actively helping in the improvement of spectrum usage and recognizing available channels in CR systems. Cyclostationary feature detection (CFD) is a spectrum sensing method that detects signals depending on different characteristics such as carrier frequency, modulation types, cyclic frequency, and symbol rates with an extremely low signal-to-noise ratio. At low SNR, CFD achieves a detection process with a high computation complexity. This paper designs Enhanced Cyclostationary Detector complexity with improved detection speed performances. For the sake of minimizing system complexity, utilizing the advantages of the Haar wavelet transform and signed correlator method for estimating the cyclic spectra of a detected signal. The proposed method performance was evaluated over Rayleigh flat fading and AWGN channel that had low SNR values. The acquired simulation results indicated the efficiency of the proposed method in terms of reduction 70% in complexity, 60% in time, and 7% in memory storage, with improved detection performance that is about 8% compared to conventional method at low SNR values reach to -30dB. [ABSTRACT FROM AUTHOR]