Search

The following paper on "Antenna Pattern Synthesis Using the Quasi Newton Method, Firefly and Particle Swarm Optimization Techniques", presents a study of antenna array synthesis using the optimization methods on various parameters such as pattern shape, side lobe level reduction, placement of nulls, and beam width. The optimization performance is studied in simulation and inferences are made from the study. The last paper "Performance Analysis of Meningioma Brain Tumor Detection System Using Feature Learning Optimization and ANFIS Classification Method", is about the classification and segmentation of meningioma tumors. The following paper, titled "Miniaturised Triangular Microstrip Antenna with Metamaterial for Wireless Sensor Node Applications", presents the design of a compact triangular microstrip antenna with metamaterial for wireless sensor node applications. [Extracted from the article]

The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing the critical task of seamlessly integrating Cognitive Radio Sensor Networks (CRSNs) and VLC technologies. The central challenge addressed is the intricate landscape of this integration, characterized by notable trade-offs between performance and complexity, which escalate with the addition of more devices and increased data rates. This scenario necessitates the development of advanced cognitive radio strategies, potentially facilitated by Machine Learning (ML) and Deep Learning (DL) approaches, albeit introducing new complexities such as the necessity for pre-training with extensive datasets. The review scrutinizes the fundamental aspects of CRSNs and VLC, spotlighting key areas like Energy Efficient Resource Allocation, Industrial Scenarios, and Energy Harvesting, and explores the synergistic amalgamation of these technologies as a promising pathway for enhanced spectrum utilization and network performance. By delving into the integration of cognitive radio technology with visible light, this study furnishes valuable insights into the potential for innovative applications in wireless communication, presenting a balanced overview of the current advancements and prospective avenues in the field of cognitive hybrid RF/VLC systems. [ABSTRACT FROM AUTHOR]

Spectrum sensing technology can effectively improves the utilization of spectrum resources by empowering unauthorized users to access the idle spectrum opportunely. This paper proposes a new spectrum sensing algorithm to determine whether a signal exists under impulsive noise by only using the sampled data. First, this method uses the Similarity Measure of samples to construct the test statistic. Theoretical analysis shows that it can suppress the influence of impulsive noise, realize accurate signal detection and improve the reliability of the system with fewer samples. Then, the probability distribution of test statistic is investigated through statistical theory analysis, under the both case of the absence and the presence of primary user signals, respectively, and the probabilities of detection and the false alarm are derived. After that, we give the detection threshold for a given false-alarm probability. Finally, experimental simulation confirms the effectiveness of the proposed algorithm and the correctness of theoretical analysis. [ABSTRACT FROM AUTHOR]

In cognitive radio network (CRN), effective spectrum management provides better quality of service. The spectrum is limited but the significance of the spectrum is increasing at each network generation. Due to the ineffective spectrum allocation policies, several researches have indicated that a vast segment of the licensed radio is not viably used. A CRN is an intelligent spectrum utilization innovation that provides better spectrum interface. Spectrum sensing detects unused spectrum in the manner that protects interferences to the authorized users. In principle, the secondary user (SU) receives the primary user (PU) signal and reports it to the fusion center for decision or spectrum allocation. The SU cooperates in the detection of the presence or absence of the PU. This type of spectrum sensing is called cooperative spectrum sensing. However, the significance of this type of spectrum sensing is blurred by the security problems. Malicious users can deliberately report misleading information regarding the presence of the PU. Hence, in this paper, a support vector machine learning algorithm is proposed to statistically learn the behavior of the malicious users and it classifies the legitimate SU and malicious users. A particle swarm optimization algorithm is also integrated to learn the smallest possible distinguishable malicious users' energy report deviation from the legitimate SUs. The probability of detection and energy of detection have been applied to evaluate the contribution of the proposed method. Finally, the simulation results have confirmed that better spectrum management can be derived from the proposed statistical approach. [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]

This paper presents the design of a frequency-tunable substrate-integrated waveguide (SIW) band-reject filter, specifically for spectrum underlay cognitive radio operation. The proposed filter has a simple tuning circuit but provides a wide frequency tuning range from 2.9 to 4.4 GHz (41%). The second resonant mode has been suppressed using a simple quadrature coupling; hence, the fundamental mode bandwidth of the filter has been increased from 2.08 GHz to 3.36 GHz. Due to its wide tuning range, simple tuning circuit, and increased fundamental mode bandwidth, the proposed filter is greatly important in underlay cognitive radio construction. The second-order filter has also been developed, and its performance is analyzed with both simulation and measurement. It gives a tunable bandwidth of 41%, an insertion loss of more than 10 dB, and a fundamental mode bandwidth of 3.36 GHz. The filter performance is also analyzed after connecting it to the standard horn antenna. Finally, from the total efficiency plots, it has been concluded that the proposed filter achieves all the above advantages with a low-lossy nature. [ABSTRACT FROM AUTHOR]

This paper presents a study on the impact of circuit parameters on the transmission of electrical energy in wireless power transfer systems designed for intelligent sensing devices within the urban electric power Internet of Things (IoT). Relying on the essential principles of resonant mutual inductance models, the paper conducts an analytical investigation into the phenomena of power-frequency splitting characteristics, efficiency-frequency splitting characteristics, and efficacy synchronization characteristics within wireless energy transmission technologies. The investigation includes a detailed analysis of a wireless power transfer system model operating at 100 kHz, delineating how varying circuit parameters influence the system's efficiency. Via the utilization of graphical software and computational programming for simulation modeling, this research delved into the dynamics between key parameters such as equivalent load and coupling coefficient and their influence on distinct splitting phenomena. This rigorous approach substantiated the validity of the proposed power-frequency and efficiency-frequency splitting characteristics outlined in the study. Based on the analytical results, it is shown that selecting an appropriate equivalent load or utilizing impedance matching networks to adjust the equivalent load to a suitable size is crucial in consideration of the system's output power, voltage withstand level, and transmission efficiency. The research findings provide a theoretical basis for the design of wireless power supply systems for non-directly buried cable front-end sensing devices. [ABSTRACT FROM AUTHOR]

Mobile Crowdsensing (MCS) has shown the greatest potential that allows smart devices to collect and share different sensing data. Mobile users (or participants) send the desired sensing data to the service providers and collect rewards. However, the reward needs to be given such as, it does not increase platform costs. On the other hand, the unsatisfactory reward may reduce the interest of the participant which may degrade the quality data. Therefore, increasing sensing data quality with a constrained budget is a crucial challenge. There has been extensive research on the reward mechanism for MCS, but, most of the work is on the basic assumption that participant will complete their assigned task positively. In this paper, we propose an efficient user selection mechanism for Mobile Crowdsensing System (MCS) by considering the Probability of Success (PoS) of users (i.e. participant may fail to complete the assigned task with some probability). For the selection of an efficient user, the proposed mechanism also accounts the parameters like data quality and platform cost. We also propose a reward calculation model for the selected users. Minimizing the platform cost with a constrained budget is an NP-hard problem. To provide a sub-optimal solution to this problem Chaotic Krill-Herd optimization algorithm is used. The extensive simulation results reveal that the proposed method outperforms the existing work by considerable margins. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) is developed to provide effective spectrum usage. CR is much significant in improving the efficiency of the global internet in applications. The evolutionary measurement technology is utilised to improve the evaluation of channel-state information. The outcome attained very few spectrums sensing in CR for complex mobility. A good optimisation method is needed to improve the accurate channel state prediction in successful channel access. Thus, this paper aims to implement a novel power and channel allocation mechanism with the help of a new Modified Levy Flight-based Artificial Ecosystem Optimisation (MLF-AEO) Optimisation Strategy. This paper achieves the optimal power control and channel allocation mechanism intending to solve the multiple objective functions based on the constraints like Interference among users, Outage Probability, and throughput. The superiority of the proposed algorithm is thoroughly verified by various simulation results. [ABSTRACT FROM AUTHOR]

An emerging railway technology called smart railway promises to deliver higher transportation efficiency, enhanced comfort in services, and greater eco-friendliness. The smart railway is expected to integrate fifth-generation mobile communication (5G), Artificial Intelligence (AI), and other technologies, which poses new problems in the construction, operation and maintenance of railway wireless networks. Wireless Digital Twins (DTs), which have recently emerged as a new paradigm for the design of wireless networks, can address these problems and enable the whole lifecycle management of railway wireless networks. However, there are still many scientific issues and challenges for railway-oriented wireless DT. Relevant key technologies to solve these problems are introduced and described, including characterization of materials' physical-EM properties, autonomous reconstruction of Three-dimensional (3D) environment model, AI-empowered environmental cognition, Ray-Tracing (RT), model-based and AIbased RT acceleration, and generation of multi-spectra sensing data. Moreover, this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on highperformance RT developed by our laboratory. This paper outlines the framework for realizing the wireless DT of smart railways, providing the direction for future research. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition (AMR) of radiation source signals is a research focus in the field of cognitive radio. However, the AMR of radiation source signals at low SNRs still faces a great challenge. Therefore, the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper. First, the time series of the radiation source signals are reconstructed into two-dimensional data matrix, which greatly simplifies the signal preprocessing process. Second, the depthwise convolution and large-size convolutional kernels based residual neural network (DLRNet) is proposed to improve the feature extraction capability of the AMR model. Finally, the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type. Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method. The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions. [ABSTRACT FROM AUTHOR]

In this paper, the fundamental principles of UWB and CRS technologies have been presented, and the approach of these systems in recent research focuses on the efficient use of RF resources. UWB systems are an alternative with great scientific support for identifying spectral gaps and allocating RF dynamics in the CRS. Microstrip planar antennas for UWB and CRS systems have been mentioned as practical alternatives for implementing the radio interfaces of these complex systems, as will be proposed in this work. The need for the most efficient use of available spectral resources is the starting point for understanding the growing interest in emerging mobile communications systems. The coexistence of several wireless systems in a limited frequency band and the increasing demand for applications that require high transmission rates have needed new strategies for managing and allocating RF resources. In this sense, UWB and CRS technologies have been presented as promising improvements in the use of radiofrequency resources. This paper presents the fundamental principles of UWB and CRS technologies, emphasizing microstrip planar antennas, whose application has been successfully proven in recent research over the last few decades, as will be exposed in this work. [ABSTRACT FROM AUTHOR]

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system, an interference decision method based Q-learning algorithm is proposed. First, dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces. Second, a multidimensional motion space is utilized, and the search range of which changes with the distance of the projectile, to select parameters and minimize the amount of ineffective interference parameters. The interference effect is determined by detecting whether the fuze signal disappears. Finally, a weighted reward function is used to determine the reward value based on the range state, output power, and parameter quantity information of the interference form. The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous. The optimal interference form for each distance state has been obtained. Compared with the single-interference decision method, the proposed decision method can effectively improve the success rate of interference. [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]

With the rapid development of wireless communication networks and Internet of Things technology (IoT), higher requirements have been put forward for spectrum resource utilization and system performance. In order to further improve the utilization of spectrum resources and system performance, this paper proposes an intelligent reflecting surface (IRS)-assisted fair energy allocation algorithm for cognitive multiple-input single-output (MISO) wireless-powered networks. The goal of this paper is to maximize the minimum energy receiving power in the energy receiver, which is constrained by the signal-to-interference-plus-noise ratio (SINR) threshold of the information receiver in the secondary network, the maximum transmission power at the cognitive base station (CBS), and the interference power threshold of the secondary network on the main network. Due to the coupling between variables, this paper uses iterative optimization algorithms to optimize and solve different variables. That is, when solving the active beamforming variables, the passive beamforming variables are fixed; then, the obtained active beamforming variables are fixed, and the passive beamforming variables are solved. Through continuous iterative optimization, the system converges. The simulation results have verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Fifth Generation (5G) communications are regarded as the cornerstone to household consumer experience improvements and smart manufacturing revolution from the standpoint of industries' objectives. It is anticipated that Envisaged 5G (E5G) mobile technology would be operational in certain developed countries by 2023. The Internet of Things (IoTs) will transform how humans live when combined with smart and integrated sensing devices, such as in-home sensing devices. Recent research is being carried out all over the world to produce a new technique that can be crucial in the success of the anticipated 5G mobile technology. High output, reduced latency, highly reliable, greater scalability, high performance, capacity, bandwidth efficiency, virtual open-air transmission, and efficient energy mobile wireless communications are all being investigated currently. In this work, a comprehensive path for addressing the difficulties and developments associated with 5G mobile technology is provided. The debate and description of a complete analysis of current situations, certain characteristics and prospective scenarios, important technologies, problems and advances, and spectrum allocation of envisioned 5G mobile technologies are provided. Furthermore, this paper analyzes the most notable elements of 5G mobile technology, such as Cognitive Radio (CR), flexibility, accessibility, and cloud-based service offers, which will assure 5G mobile technology's dominance as the main protocol for international communication. Eventually, this paper provides a method for integrating CR with current wireless communication systems, the necessity for further evolution of the E5Gnetwork, and the need for comprehensive consideration of architecture evolution and function enhancement to enhance the E5G mobile technologies. [ABSTRACT FROM AUTHOR]

Currently, due to the proliferation of smart appliances and the rapid development of wireless communication technologies, there is a huge shortage of spectrum resources. Cognitive radio utilizes technologies that dramatically increase the utilization of free frequency bands, detecting the environment in real time and maximizing the reallocation of spectrum resources by sensing and reallocating the licensed free frequency bands. The frequency and accuracy required to determine the free bands is an important basis for determining the spectrum awareness in all aspects of a sensing radio system. Therefore, for the more complex network environment in reality and the intrusion of malicious users, this paper uses Fast K-medoids and Mean-shift methods for information fusion and an iterative approach to overcome malicious use to fuse data and finally obtain realistic feature statistics. The results show that the collaborative identification strategy proposed in this paper has good results. [ABSTRACT FROM AUTHOR]

Traditional heterogeneous networks (HetNets) are constrained by their hardware design and configuration. These HetNets have a limited ability to adapt to variations in network dynamics. Software-defined radio technology has the potential to address this adaptability issue. In this paper, we introduce a software-defined radio (SDR)-based long-term evolution licensed assisted access (LTE-LAA) architecture for next-generation communication networks. We show that with proper design and tuning of the proposed architecture, high-level adaptability in HetNets becomes feasible with a higher throughput and lower power consumption. Firstly, maximizing the throughput and minimizing power consumption are formulated as a constrained optimization problem. Then, the obtained solution, alongside a heuristic solution, is compared against the solutions to existing approaches, showing our proposed strategy is drastically superior in terms of both power efficiency and system throughput. This study is then concluded by employing artificial intelligence techniques in multi-objective optimization, namely random forest regression, particle swarm, and genetic algorithms, to balance out the trade-offs between maximizing the throughput and power efficiency and minimizing energy consumption. These investigations demonstrate the potential of employing the proposed LTE-LAA architecture in addressing the requirements of next-generation HetNets in terms of power, throughput, and green scalability. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel system integrating reconfigurable intelligent surfaces (RISs) with cognitive radio (CR) technology, presenting a forward-looking solution aligned with the evolving standards of 6G and beyond networks. The proposed RIS-assisted CR networks operate with a base station (BS) transmitting signals to two users, the primary user (PU) and secondary user (SU), through direct and reflected signal paths, respectively. Our mathematical analysis focuses on deriving expressions for SU in the RIS-assisted CR system, validated through Monte Carlo simulations. The investigation covers diverse aspects, including the impact of the signal-to-noise ratio (SNR), power allocations, the number of reflected surfaces, and blocklength variations. The results provide nuanced insights into RIS-assisted CR system performance, highlighting its sensitivity to factors like the number of reflectors, fading severity, and correlation coefficient. Careful parameter selection, such as optimizing the configuration of reflectors, is shown to prevent a complete outage, showcasing the system's robustness. Additionally, the work suggests that the optimization of reflectors configuration can significantly enhance overall system performance, and RIS-assisted CR systems outperform reference schemes. This work contributes a thorough analysis of the proposed system, offering valuable insights for efficient performance evaluation and parameter optimization in RIS-assisted CR networks. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the expensive and scarce resources are energy and frequency spectrum. To overcome this scarcity of spectrum, cognitive radio has been introduced in WSNs. The licensed band is utilized by the primary users in cognitive radio whereas the secondary users can use the licensed channels. For improving the network lifetime, overall network scalability, and energy consumption, the clustering technique is used to group the sensor nodes into clusters. Clustering algorithms must be energy efficient because of the difficulties in replacing or recharging the batteries of nodes. While designing the algorithms, more constraints result in the clustering for CRSN as the dynamic spectrum access has been addressed. We propose an energy-efficient fuzzy clustering and congestion control algorithm (EFCCA) in this paper to improve energy efficiency. With the consideration of spectrum availability, queue length, and residual energy, the election of cluster head contributes to the energy efficiency of the algorithm. The active Queue Management algorithm is used to monitor and control the congestion rate. The proposed EFCCA's performance evaluates with the help of comparison with other clustering methods and it shows enhanced performance in energy efficiency and lifetime based on the outcomes of experimental investigation. [ABSTRACT FROM AUTHOR]

Cognitive satellite networks can enhance spectrum efficiency by enabling satellite networks to dynamically share idle spectrum with terrestrial networks. However, when taking into consideration the inter-cell or internet interferences in various application circumstances, dynamic spectrum optimization in cognitive satellite networks remains challenging due to the diversified QoS demands of secondary terrestrial users. To address this issue, this paper proposes an efficient spectrum allocation algorithm which is based on graph coloring technique and combined with analytic hierarchy process so as to meet the requirements of dynamic spectrum allocation to secondary terrestrial users with diversified QoS demands and yet without causing severe inference to primary satellite users. In this paper, we combine graph coloring algorithm and analytic hierarchy process so as to assign the heterogeneous spectrum bands to secondary users in an orderly manner. The method of graph coloring is applied to control obvious interference caused by the secondary users. We also provide numerical results to evaluate the impacts of different parameter settings on the performances of the proposed solution. [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]

The reconfigurable intelligent surfaces (RIS) represent a transformative technology in wireless communication, offering a novel approach to managing and enhancing radio signal propagation. By dynamically adjusting their electromagnetic properties, RIS can significantly improve the performance and efficiency of 5G and beyond communication systems. In this paper, we study a cognitive RIS-aided non-orthogonal multiple access (NOMA) network that serves multiple users and improves spectrum efficiency. Our analysis assumes a secondary network operates under multi-primary user constraints and interference from the primary source. We derive approximation closed-form formulas for outage probability (OP), and system throughput. To obtain further insights, an asymptotic expression for OP is computed by taking into account two power configurations at the source. Additionally, numerical results show the effects of important factors on performance, confirming the accuracy of the theoretical derivation. According to the simulation results, performance by the system under consideration might be improved considerably by combining a RIS and NOMA, particularly when compared to an orthogonal multiple access scheme. [ABSTRACT FROM AUTHOR]

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios. Therefore, this paper endeavored to address the challenge of AMR with limited data and proposed a novel meta-learning method, the Multi-Level Comparison Relation Network with Class Reconstruction (MCRN-CR). Firstly, the method designs a structure of a multi-level comparison relation network, which involves embedding functions to output their feature maps hierarchically, comprehensively calculating the relation scores between query samples and support samples to determine the modulation category. Secondly, the embedding function integrates a reconstruction module, leveraging an autoencoder for support sample reconstruction, wherein the encoder serves dual purposes as the embedding mechanism. The training regimen incorporates a meta-learning paradigm, harmoniously combining classification and reconstruction losses to refine the model's performance. The experimental results on the RadioML2018 dataset show that our designed method can greatly alleviate the small sample problem in AMR and is superior to existing methods. [ABSTRACT FROM AUTHOR]

In this paper, a unique low profile double stubbed ground plane frequency reconfigurable circular monopole antenna is introduced. The ground plane contains two RF-PIN diodes that enable the antenna to be reconfigured in ultra-wideband (3.2-10.8 GHz) and dual frequency (2.8-4.01 GHz and 7.56-8.2 GHz) modes. The proposed antenna is designed using an FR-4 substrate with the dimension about 33×28×1.6 mm3. The impedance matching of the antenna at ultra-wideband operation is improved by a defected ground structure. The measured and simulated results of the antenna are in close agreement. This antenna is useful for cognitive radio application. [ABSTRACT FROM AUTHOR]

Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally, it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops. These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios. Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes. The evaluation spans varying sensor densities, wireless channel conditions, and packet transmission rates, showcasing the protocol's superiority in ensuring reliable and efficient data transfer. Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs. By prioritizing fairness, reliability, and efficiency, the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services, thereby offering a valuable contribution to the field of medical event-drivenWSNs. [ABSTRACT FROM AUTHOR]

This paper investigates the incidence of coherent emission in solar radio bursts, using a revised catalog of 3800 solar radio bursts observed by the Nobeyama Radio Polarimeters from 1988 to 2023. We focus on the 1.0 and 2.0 GHz data, where radio fluxes of order 1010 Jy have been observed. Previous work has suggested that these bursts are due to electron cyclotron maser (ECM) emission. In at least one well-studied case, the bright emission at 1 GHz consists of narrowband spikes of millisecond duration. Coherent emission at 1 GHz can be distinguished from traditional incoherent gyrosynchrotron flare emission based on the radio spectrum: Gyrosynchrotron emission at 1 GHz usually has a spectrum rising with frequency, so bursts in which 1 GHz is stronger than higher-frequency measurements are unlikely to be incoherent gyrosynchrotron. Based on this criterion, it is found that for bursts exceeding 100 sfu, three-quarters of all bursts at 1 GHz and half of all 2 GHz bursts have a dominant coherent emission component, assumed to be ECM. The majority of the very bright bursts at 1 GHz are highly circularly polarized, consistent with a coherent emission mechanism, but not always 100% polarized. The frequency range from 1 to 2 GHz is heavily utilized for terrestrial applications, and these results are relevant for understanding the extreme flux levels that may impact such applications. Further, they provide a reference for comparison with the study of ECM emission from other stars and potentially exoplanets. [ABSTRACT FROM AUTHOR]

The accurate perception of external environment information through the robot foot is crucial for the mobile robot to evaluate its ability to traverse terrain. Adequate foot-end contact signals can provide robust support for robot motion control and decision-making processes. The shape and uncertain rotation of the wheel-legged robot foot end represent a significant challenge to sensing the robot foot-end contact state, which current foot-end sensing schemes cannot solve. This paper presents a sensing method for the tire stress field of wheel-legged robots. A finite element analysis was conducted to study the deformation characteristics of the foot-end tire under force. Based on this analysis, a heuristic contact position estimator was designed that utilizes symmetrical deformation characteristics. Strain sensors, arranged in an array, extract the deformation information on the inner surface of the tire at a frequency of 200 Hz. The contact position estimator reduces the dimensionality of the data and fits the eigenvalues to the estimated contact position. Using support vector regression, the force estimator utilizes the estimated contact position and sensor signal to estimate the normal reaction force, designated as FZ. The sensing system is capable of detecting the contact position on the wheel circumference (with a root mean square error of 1.150°), as well as the normal force of 160 N on the Z axis (with a root mean square error of 6.04%). To validate the efficacy of the sensor detection method, a series of randomized and repeated experiments were conducted on a self-constructed test platform. This novel approach offers a promising avenue for perceiving contact states in wheel-legged robots. [ABSTRACT FROM AUTHOR]

This paper proposes a cognitive radio network (CRN)-based hybrid wideband precoding for maximizing spectral efficiency in millimeter-wave relay-assisted multi-user (MU) multiple-input multiple-output (MIMO) systems. The underlying problem is NP-hard and non-convex due to the joint optimization of hybrid processing components and the constant amplitude constraint imposed by the analog beamformer in the radio frequency (RF) domain. Furthermore, the analog beamforming solution common to all sub-carriers adds another layer of design complexity. Two hybrid beamforming architectures, i.e., mixed and fully connected ones, are taken into account to tackle this problem, considering the decode-and-forward (DF) relay node. To reduce the complexity of the original optimization problem, an attempt is made to decompose it into sub-problems. Leveraging this, each sub-problem is addressed by following a decoupled design methodology. The phase-only beamforming solution is derived to maximize the sum of spectral efficiency, while digital baseband processing components are designed to keep interference within a predefined limit. Computer simulations are conducted by changing system parameters under different accuracy levels of channel-state information (CSI), and the obtained results demonstrate the effectiveness of the proposed technique. Additionally, the mixed structure shows better energy efficiency performance compared to its counterparts and outperforms benchmarks. [ABSTRACT FROM AUTHOR]

This paper aims to promote technology usage among older Saudi people by addressing the common problems they encounter around adhering to medication schedules and using mobile health applications. An interactive mobile user interface has been designed based on requirements determined using three inputs: (1) relevant existing research work, (2) interviews with medicine specialists, and (3) a survey of 602 older Saudis. The output from the first input is a set of updated guidelines adapted to the older Saudi population and reconstructed based on cognitive process attributes. The interview output identifies medication adherence issues and proposed solutions. The survey output produced a mobile technology model for older Saudis. The guidelines have been applied to a prototype and then tested in two phases: (1) pilot testing and (2) usability testing. In the testing phase, 50 older Saudi users provided insights into the effectiveness, error safety, and productivity factors of the solution developed, with the results confirming that designing mobile applications based on the model of older users and cognitive attributes improved effectiveness and reduced errors when performing mobile tasks, specifically around adherence to medications. For the productivity factor, the results align with the physical characteristics of the targeted older individuals, who typically require more time than younger people to perform certain tasks. [ABSTRACT FROM AUTHOR]

The internet of everything (IoEs) has become a well-known tool for transforming the concept of connected environments into reality in near future. The cognitive radio network is a potential candidate that addresses the issues of efficient spectrum utilization for next generation connected environments. Further, the spectrum mobility plays a significant role in cognitive connected environments during communication, for switching the channel on the appearance of primary user (PU) throughout the cognitive users' (CUs') data transmission. The performance of spectrum mobility relies on the ability of the system: 1) to detect the appearance of PU as soon as possible and 2) to stop the data transmission as-well-as switch to another channel. The potential approaches to detect the appearance of PU during CUs' data transmission are the "spectrum prediction (SP)" and "spectrum monitoring" (SM). The SP relies on the pre-available information about the channel and PUs' activities which is well explored technique, however, the SM is a real-time approach and is in its infancy. In this paper, several SM techniques with their effects on the spectrum mobility are illustrated. Moreover, the concept of imperfect SM is introduced and its effects on various performance metrics are investigated. Further, a potential approach of cooperative SM is proposed to diminish the effects of imperfections. In addition to this, the potential issues as well as research challenges regarding these techniques are presented. [ABSTRACT FROM AUTHOR]

The main objective of the research presented in the following work was the adaptation of reflection-radar technology in a detection and navigation system using radio-tomographic imaging techniques. As key aspects of this work, the energy optimization of high-frequency transmitters can be considered for use inside buildings while maintaining user safety. The resulting building monitoring and control system using a network of intelligent sensors supported by artificial intelligence algorithms, such as logistic regression or neural networks, should be considered an outcome. This paper discusses the methodology for extracting information from signal echoes and how they were transported and aggregated. The data extracted in this way were used to support user navigation through a building, optimize energy based on presence information, and increase the facility's overall security level. A band from 5 GHz to 6 GHz was chosen as the carrier frequency of the signals, representing a compromise between energy expenditure, range, and the properties of wave behavior in contact with different types of matter. The system includes proprietary hardware solutions that allow parameters to be adjusted over the entire range and guarantee adaptation for RTI (radio tomography imaging) technology. [ABSTRACT FROM AUTHOR]

Cognitive Vehicular Networks (CVNs) can improve spectrum utilization by intelligently using idle spectrum, so as to fulfill the needs of communication. The previous researches only considered vehicle-to-vehicle(V2V) links or vehicle-to-infrastructure (V2I) links and ignored the influence of spectrum sensing errors. Therefore, in this paper, V2V links and V2I links are simultaneously discussed in the presence of spectrum sensing errors in the CVNs communication environment that we establish, and a dynamic spectrum access problem aiming at spectrum utilization is framed. In order to solve the above problems, the reinforcement learning method is introduced in this paper. But the impact of two kinds of collisions on the spectrum access rate of cognitive vehicles is neglected in the reinforcement learning method, and the above collisions which exist between cognitive vehicles, between cognitive vehicles and primary vehicles. Hence, different reward functions are designed according to different collision situations, and an improved reinforcement learning method is utilized to improve the success probability of spectrum access. To verify the effectiveness of the improved method, the performance and convergence of the proposed method are significantly better than other methods by comparing with the Myopic method, DQN and traditional DDQN in Python. [ABSTRACT FROM AUTHOR]

The automatic modulation classification (AMC) plays an important and necessary role in the truncated wireless signal, which is used in modern communications. The proposed convolution neural network (CNN) for AMC is based on a method of feature expansion by integrating I/Q (time form) with r/O (polar form) in order to take advantage of two things: first, feature expansion helps to increase features; the second is that converting to polar form helps to increase classification accuracy for higher order modulation due to diversity in polar form. CNN consists of six blocks. Each block contains symmetric and asymmetric filters, as well as max and average pooling filters. This paper uses DeepSig: RadioML which is a dataset of 24 modulation classes. The proposed network has outperformed many recent papers in terms of classification accuracy for 24 modulation types, with a classification accuracy of up to 96.06 at an SNR=20 dB. [ABSTRACT FROM AUTHOR]

In general, judging the use/idle state of the wireless spectrum is the foundation for cognitive radio users (secondary users, SUs) to access limited spectrum resources efficiently. Rich information can be mined by the inherent correlation of electromagnetic spectrum data from SUs in time, frequency, space, and other dimensions. Therefore, how to efficiently use the spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency, and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results showed that our model achieves better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirms the feasibility of the model. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is an integral system in telecommunications technology that gives unlicensed users access to licensed spectrums via dynamic spectrum access (DSA), to promote spectral efficiency. A significant operation in cognitive radio system is spectrum sensing. This paper evaluates and compares two of the major non-cooperative sensing techniques (energy and cyclostationary feature detector (CFD)) in order, to determine which gives better performance. Matlab Simulink was used as modeling and simulating tool for the evaluations. From the results, energy detector was simpler and faster but unlike CFD exhibited poor performance in corrupt channels. [ABSTRACT FROM AUTHOR]

With the advent of the Industry 5.0 era, the Internet of Things (IoT) devices face unprecedented proliferation, requiring higher communications rates and lower transmission delays. Considering its high spectrum efficiency, the promising filter bank multicarrier (FBMC) technique using offset quadrature amplitude modulation (OQAM) has been applied to Beyond 5G (B5G) industry IoT networks. However, due to the broadcasting nature of wireless channels, the FBMC-OQAMindustry IoT network is inevitably vulnerable to adversary attacks frommalicious IoT nodes. The FBMC-OQAMindustry cognitive radio network (ICRNet) is proposed to ensure security at the physical layer to tackle the above challenge. As a pivotal step of ICRNet, blind modulation recognition (BMR) can detect and recognize the modulation type of malicious signals. The previous works need to accomplish the BMR task of FBMC-OQAM signals in ICRNet nodes. A novel FBMC BMR algorithm is proposed with the transform channel convolution network (TCCNet) rather than a complicated two-dimensional convolution. Firstly, this is achieved by designing a low-complexity binary constellation diagram (BCD) gridding matrix as the input of TCCNet. Then, a transform channel convolution strategy is developed to convert the image-like BCD matrix into a serieslike data format, accelerating the BMR process while keeping discriminative features. Monte Carlo experimental results demonstrate that the proposed TCCNet obtains a performance gain of 8% and 40% over the traditional inphase/quadrature (I/Q)-based and constellation diagram (CD)-based methods at a signal noise ratio (SNR) of 12 dB, respectively. Moreover, the proposed TCCNet can achieve around 29.682 and 2.356 times faster than existing CD-Alex Network (CD-AlexNet) and I/Q-Convolutional Long Deep Neural Network (I/Q-CLDNN) algorithms, respectively. [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]

Detecting hydrogen leaks remains a pivotal challenge demanding robust solutions. Among diverse detection techniques, the fiber-optic method distinguishes itself through unique benefits, such as its distributed measurement properties. The adoption of hydrogen-sensitive materials coated on fibers has gained significant traction in research circles, credited to its operational simplicity and exceptional adaptability across varied conditions. This manuscript offers an exhaustive investigation into hydrogen-sensitive materials and their incorporation into fiber-optic hydrogen sensors. The research profoundly analyzes the sensor architectures, performance indicators, and the spectrum of sensing materials. A detailed understanding of these sensors' potentials and constraints emerges through rigorous examination, juxtaposition, and holistic discourse. Furthermore, this analysis judiciously assesses the inherent challenges tied to these systems, simultaneously highlighting potential pathways for future innovation. By spotlighting the hurdles and opportunities, this paper furnishes a view on hydrogen sensing technology, particularly related to optical fiber-based applications. [ABSTRACT FROM AUTHOR]

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture. [ABSTRACT FROM AUTHOR]

Continuous monitoring of vital signs based on advanced sensing technologies has attracted extensive attention due to the ravages of COVID-19. A maintenance-free and low-cost passive wireless sensing system based on surface acoustic wave (SAW) device can be used to continuously monitor temperature. However, the current SAW-based passive sensing system is mostly designed at a low frequency around 433 MHz, which leads to the relatively large size of SAW devices and antenna, hindering their application in wearable devices. In this paper, SAW devices with a resonant frequency distributed in the 870 MHz to 960 MHz range are rationally designed and fabricated. Based on the finite-element method (FEM) and coupling-of-modes (COM) model, the device parameters, including interdigital transducer (IDT) pairs, aperture size, and reflector pairs, are systematically optimized, and the theoretical and experimental results show high consistency. Finally, SAW temperature sensors with a quality factor greater than 2200 are obtained for real-time temperature monitoring ranging from 20 to 50 °C. Benefitting from the higher operating frequency, the size of the sensing system can be reduced for human body temperature monitoring, showing its potential to be used as a wearable monitoring device in the future. [ABSTRACT FROM AUTHOR]

5G communication technology supports the Internet of Things, remote health care centers, and cloud computing by tuning their communication services over a very wide range of frequency bands with lowcost, low-battery consumption, and low latency. However, the development of such wireless technology is highly dependent on radio frequency spectra. The Cognitive Radio Sensor Network (CRSN) is an excellent candidate to improve radio spectrum utilization and manage the heavy communication data traffic in 5G wireless networks. CRSN can sense the frequency channels, making it possible for secondary users (who are denied service) to use the free channels. Despite the outstanding features of CRSNs, some limitations overshadow their performance. The most critical limitation is energy and its optimal consumption to increase the network's lifetime. Recent research has shown that energy harvesting can be an effective way to increase the lifetime of CRSNs. However, the sensors should sense the frequency spectrum with a high success rate. In this paper, several optimal sensor nodes using energy harvesting with the approach of increasing the network's lifetime are proposed to solve the mentioned challenge. This way, the sensor nodes are divided into two independent groups for simultaneous spectrum sensing and energy harvesting in each time frame. We will solve this problem based on mathematical optimization and the use of proposed solutions for convex problems. Finally, simulations are developed to evaluate the ability of the proposed solution, assuming the systems use IEEE802.15.4/Zigbee and IEEE802.11af. [ABSTRACT FROM AUTHOR]

In view of the current problems of spectrum resource scarcity, return congestion, and insufficient energy utilization in the unmanned aerial vehicle (UAV)-assisted Internet of Vehicles (IoV), this paper investigates the cooperative caching and power control, and proposes a joint optimization method to improve the overall Energy Efficiency (EE). In this method, we first propose a communication establishment threshold to control the V2V communication distance and serve as a joint optimization factor. Then we derive the closed form expressions of offloading ratio and EE of the UAV-assisted IoV, and formulate the optimization problem of maximizing EE. Due to the coupling relationship between caching strategy and transmission power, it is difficult for us to directly solve the optimization problem. Furthermore, we propose an alternating optimization algorithm for solving the optimization problem. Finally, the experimental simulation compare the propose joint optimization method with other existing optimization methods, and the simulation results prove the effectiveness and superiority of the propose joint optimization method. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems. [ABSTRACT FROM AUTHOR]

This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms. Radio clustering is a method of searching for similar signals within the analyzed part of the radio spectrum. Typically, it is based on one or several specific parameters processed from the signal in a given channel. The authors propose a slightly different, innovative approach; thanks to the self-supervised learning of neural networks, there is no need to define specific parameters, and the feature vector, enabling comparison of Euclidean distances between signals, is generated by a deep neural network trained using a contrastive loss function on a dataset containing different radio modulations. The authors describe self-supervised solutions based on contrastive learning and the methods of signal segmentation and augmentation. The training process utilizes a custom database and the Resnet-50 network with a contrastive cost function. Radio clustering is used for autonomous spectrum analysis across wide frequency ranges and enables, among other things, the detection of tactical radio stations operating with widely dispersed frequency-hopping or a significant reduction in computational power required for real-time analysis of a large number of radio signals. [ABSTRACT FROM AUTHOR]

Automatic modulation classification (AMC) is a challenging topic in the development of cognitive radio, which can sense and learn surrounding electromagnetic environments and help to make corresponding decisions. In this paper, we propose to complete the real-time AMC through constructing a lightweight neural network MobileViT driven by the clustered constellation images. Firstly, the clustered constellation images are transformed from I/Q sequences to help extract robust and discriminative features. Then the lightweight neural network called MobileViT is developed for the real-time constellation image classification. Experimental results on the public dataset RadioML 2016.10a with edge computing platform demonstrate the superiority and efficiency of MobileViT. Furthermore, the extensive ablation tests prove the robustness of the proposed method to the learning rate and batch size. To the best of our knowledge, this is the first attempt to deploy the deep learning model to complete the real-time classification of modulation schemes of received signals at the edge. [ABSTRACT FROM AUTHOR]