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The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented. [ABSTRACT FROM AUTHOR]

Current cutting-edge solutions to the spectrum shortage problem are unable to meet the growing demand for a limited spectrum. A key dimension beyond state-of-the-art solutions is to exploit the free spectrum more effectively. Although various schemes have been proposed for trading spectrum, few studies have focused on optimal admission of spectrum requests for maximizing service providers' (SP's) profit. Thus, this timely study presents a novel intelligent admission scheme for spectrum requests from the perspective of a non-cooperative game, in which the information of all participants (customers and providers) is incomplete to others, and each player wishes to maximize its benefit. The proposed control admission policy may evict clients in-service to release spectrum for serving certain, e.g., wealthy clients. Evicted clients are compensated using a dynamic strategy that adopts greedy game theory to capture the conflict of interest between SP and evicted users. Simulation experiment results validate and demonstrate the feasibility and efficiency of the proposed scheme, compared to a benchmark reinforcement learning approach and another widely used scheme for admission and eviction control of cognitive radio users. [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 Radio Networks (CRNs) block new arriving services and remove existing services to increase the spectrum efficiency. Particularly, Secondary Users (SUs) experience a decline in service deprivation in the arrival of Primary Users (PUs). To overcome this, a Spectrum and energy Efficiency in Cognitive Radio Networks using Dynamic Channel Reservation and Komodo Mlipir Optimization (SE-KMOA-CRN) is proposed. The proposed method splits reserved and non-reserved bands from the accessible spectrum. Numerous channels from the non-reserved band are dynamically assigned to the reserved band to support services that experience disruptions when functioning on non-reserved band. From the non-reserved band, a count of channels dynamically assigned to the reserved band to support services that experience disruptions when functioning on the non-reserved band. It enables priority-based channel assignment and termination while providing dynamic access to the obtainable spectrum. Here, Allocation of channels to Reserved Band CRN using Dynamic Channel Reservation Algorithm (DCRA). Komodo Mlipir Optimization Algorithm (KMOA) is employed for Service selection and Channel restoration. The proposed SE-KMOA-CRN method is modelled utilizing Continuous Time Markov Chain (CTMC) with mathematical equations for some quality of service (QoS) factors is obtained. The SE-KMOA-CRN approach attains higher Throughput, lower End-to-End Delay, higher Energy Efficiency, greater spectral efficiency and higher Packet Delivery Ratio compared with existing methods, like Multiple objective optimization for spectrum along energy efficacy trade-off in IRS-assisted CRNs using NOMA (SE-MOO-CRN), Energy efficacy in CRN utilizing cooperative spectrum sensing under hybrid spectrum handoff (SE-TOA-CRN) and Energy-effectual cross-layer spectrum sharing on CR green IoT (SE-CRG-CRN) respectively. [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]

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]

In wireless networks, there are two prominent challenges. The first challenge is ensuring that users have opportunities to access channels and request new services. The second challenge is maintaining connections for data flows. These challenges are compounded by the occurrence of channel failures, which often occur due to characteristics of radio transmission such as signal attenuation, signal blockage or device and power outages. Channel failures can significantly impact the effectiveness of both the primary and secondary networks. Therefore, it becomes crucial to prioritize retainability which denotes the need to maintain uninterrupted user connections even during network disruptions. This paper proposes an analytical model that evaluates performance of cognitive radio networks in the context of random channel failure rates. Additionally, the dynamic channel reservation (DCR) scheme is introduced. It can be integrated into dynamic spectrum access (DSA) strategies. This integration aims to give priority to existing services over requests from users to provide cognitive networks with more opportunities to allocate idle channels or maintain their current services. Moreover, the cost functions for both the primary user (PU) and the secondary user (SU) are calculated. This calculation considers the failure rate specifically in either reserved channels (RCN) or non-reserved channels (N-RCN) to meet different performance requirements. The results show a decrease in the SUs cost function, which guarantees that the quality of service (QoS) requirements for the PU are fulfilled. Importantly, this reduction in SU cost leads to an enhancement in SU channel availability or throughput when compared to previous models.

Featured Application: The article provides practical recommendations on the use of force density spectra in vibration prediction along railway lines. The planning application and approval process of railway tracks is generally accompanied by a vibration immission assessment. Starting with the source spectrum, which is ideally obtained through measurements, the German guideline VDI 3837 recommends a series of multiplications using transfer spectra which account for the various subdomains of the wave propagation path, such as the effect of the superstructure, the free field propagation, the soil-structure coupling and the transmission inside buildings. Typically, these one-third octave spectra are an average over empirical reference values. While simplified empirical relations are prone to a large variance, the use of artificial vibration sources allows the actual vibration transmission behavior from the tracks to the immission points to be quantified. Using so-called transfer admittances, also known as transfer mobilities, which account for all dynamic interactions along the transmission path (track, tunnel structures, foundations, structural properties), together with force density spectra for relevant rail vehicles, the authors investigate the practical application of the method presented in Report No. 0123 of the Federal Transit Administration (2018) for the frequency range 5–200 Hz. The article demonstrates how such force density spectra were obtained for the most common train types in the Austrian rail network at two different track sections using artificial vibration sources. Furthermore, practical aspects are discussed and a recently developed approximation method for estimating line transfer admittances from point transfer admittances using simplified models is introduced. [ABSTRACT FROM AUTHOR]

In this study, we explore the task of dynamic spectrum access based on game theory to mitigate spectrum shortage and improve network utilization in multichannel wireless networks. Usually, the available network bandwidth is limited and divided into several channels, and there exists a need for efficient reuse and adaptive allocation of such channels. During the communication process, U users compete with each other for C shared channels even without knowing accurate, complete channel state information. In order to avoid collision, traditional methods usually depend on centralized scheduling or message exchange, which are cumbersome and computationally expensive. To deal with this issue, we propose a deep Q-network, based on LSTM and fair channel allocation policy, to learn the dynamic spectrum access rules for network utility maximization. Extensive validation of the proposed approach shows that our scheme yields quite promising results. [ABSTRACT FROM AUTHOR]

Summary: The Third Generation Partnership Project launched the Narrowband Internet of Things (NB‐IoT) as part of 5G technology under Release‐13. These networks are authorized Low Power Wide Area Networks. It uses less power and sends message over long distances. The NB‐IoT is can be used widely in industries, environment, and home for automation. While comparing NB‐IoT to regular Long Term Evaluation Machine Type Communication, the repetition in uplink is 128. The extreme coverage by 164 dB in terms of Maximum Coupling Loss. The NB‐IoT is employed in deep coverage for users. If we use many repetitions in NB‐IoT technology, the results are shorter battery life and higher maintenance cost. Hence, we suggest abandoning the random spectrum access strategy in favor of a novel technique termed Dynamic Spectrum Access, which uses reinforcement learning algorithm to eliminate the shortcomings. This method is used to decrease the number of re‐transmissions so that NB‐IoT system power goes to less and augments coverage. [ABSTRACT FROM AUTHOR]

Cognitive radio technology is introduced to identify the spectrum holes dynamically. Such dynamic spectrum access operations cause the nodes in the cognitive radio sensor networks (CRSNs) into energy depletion problem. To handle this problem or to utilize the entire network's energy efficiently, clustering is found as one of the best solution. Even though uniform clustering mechanism reduce the energy consumption but it is not suitable when the network node density increases. Hence, this paper proposes a new clustering mechanism called as energy and spectrum aware clustering routing protocol (ESCRP) for CRSNs. Initially, the entire network is clustered nonuniformly into several clusters. Next, energy, channel availability rate, geographical and temporal correlation metrics are used to select the cluster head (CH) for each cluster. Finally, the collected information from each CM is transferred to the sink node through multi-hop communication between CHs. Extensive simulation experiments are carried out over the proposed ESCRP and the performance is measured with several performance metrics including Network lifetime and average energy consumed. From the experimental results, we observed that the weight combination α = 1/8, β = 1/8, γ = 3/4 consumed less energy than the remaining combination weight values. Finally, the average energy consumed for the combination α = 1/8, β = 1/8, γ = 3/4 for 2-layered network and 3-layered network is approximately 0.4J and 1.2J respectively. [ABSTRACT FROM AUTHOR]

Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user's protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms. [ABSTRACT FROM AUTHOR]

Modulation classification is a crucial technique to utilize the unconsumed spectrum in Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) systems to meet the required traffic demands for future-generation cellular networks. This paper presents an end-to-end experimental setup as a generic methodology to implement various Transfer Learning (TL) models in an indoor environment. This allows us to learn the features from multiple modulation signals to train and test the model. The performance evaluation of proposed TL models such as Convolutional Neural Network - Random Forest (CNN-RF), and Convolutional Long Short Term Deep Neural Network (CLDNN) - Random Forest (CLDNN-RF) have been thoroughly discussed. The result shows that the proposed TL models yield more than 90% classification accuracy for various modulation types. A proposed framework for location-specific TL model selection based on the maximum classification accuracy has been investigated. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods' performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods. [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]

The advent of the Internet of Things and 5G has further accelerated the growth in devices attempting to gain access to the wireless spectrum. A consequence of this has been the commensurate growth in spectrum conflict and congestion across the wireless spectrum, which has begun to impose a significant impost upon innovation in both the public and private sectors. One potential avenue for resolving these issues, and improving the efficiency of spectrum utilisation can be found in devices making intelligent decisions about their access to spectrum through Dynamic Spectrum Allocation. Changing to a system of Dynamic Spectrum Allocation would require the development of complex and sophisticated inference frameworks, that would be able to be deployed at a scale able to support significant numbers of devices. The development and deployment of these systems cannot exist in isolation, but rather would require the development of tools that can simulate, measure, and predict Spectral Occupancy. To support the development such tools, this work reviews not just the available prediction frameworks for networked systems with sparse sensing over large scale geospatial environments, but also holistically considers the myriad of technological approaches required to support Dynamic Spectrum Allocation. [ABSTRACT FROM AUTHOR]

Cognitive radio technology was introduced as a possible solution for spectrum scarcity by exploiting dynamic spectrum access. In the last two decades, most researchers focused on enabling cognitive radios for managing the spectrum. However, due to their intelligent nature, cognitive radios can scan the radio frequency environment and change their transmission parameters accordingly on-the-fly. Such capabilities make it suitable for the design of both advanced jamming and anti-jamming systems. In this context, our work presents a novel, robust algorithm for spectrum characterisation in wideband radios. The proposed algorithm considers that a wideband spectrum is sensed by a cognitive radio terminal. The wideband is constituted of different narrowband signals that could either be licit signals or signals jammed by stealthy jammers. Cyclostationary feature detection is adopted to measure the spectral correlation density function of each narrowband signal. Then, cyclic and angular frequency profiles are obtained from the spectral correlation density function, concatenated, and used as the feature sets for the artificial neural network, which characterise each narrowband signal as a licit signal with a particular modulation scheme or a signal jammed by a specific stealthy jammer. The algorithm is tested under both multi-tone and modulated stealthy jamming attacks. Results show that the classification accuracy of our novel algorithm is superior when compared with recently proposed signal classifications and jamming detection algorithms. The applications of the algorithm can be found in both commercial and military communication systems. [ABSTRACT FROM AUTHOR]

Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (target) data lead to performance degradation or even failure of existing DL-based AMC. The primary reason for this is the difficulty in obtaining sufficient labeled training data in the target domain. Therefore, we propose a novel cross-domain AMC algorithm based on multimodal information and transfer learning, utilizing abundant unlabeled target domain data. We achieve complementary gains by fusing multimodal information such as amplitude, phase, and spectrum, which are used to train a network. Additionally, we apply domain adversarial neural network technology from transfer learning to learn from a large number of unlabeled data samples in the target domain to address the issue of decreased accuracy in cross-domain AMC caused by differences in sampling rate, signal-to-noise ratio, and channel variations. Furthermore, we introduce class weight weighting and entropy weighting to solve the partial domain adaptation problem, considering that the target domain has fewer modulation signal classes than the source domain. Experimental results on two designed modulation datasets demonstrate improved performance gains, thus validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we address the spectrum efficiency enhancement for Internet of Things (IoT) by introducing the graph-theory-based spectrum optimization reuse. During the spectrum reuse, how to ascertain the interference range and user's transmission status is critical. In this article, we model the interference range between IoT terminals by considering their interaction status which includes interaction frequency, duration and stability. The notion of interaction status is very similar to that of social distance among people in the context of pandemic control, which is a highly effective model for supporting contact tracing by analyzing the interaction frequency, duration and stability among people. Compared with other traditional dynamic spectrum optimization methods, introducing the concept of social distance can better evaluate the IoT user's interference and transmission status from a novel perspective, then enhancing the optimal spectrum allocation. By modeling the social distances among IoT terminals, we estimate the desirable interference range for IoT devices which serves as the basis for graph-theory-oriented spectrum optimization. Together with the actual physical distances between IoT devices, they form the basis for optimizing spectrum reuse patterns. Graph theory is further utilized to complete the final spectrum optimization. Furthermore, comparison simulation tests are conducted to evaluate the performances of our proposed solution in network benefits and system capacity.Kindly check and confirm the inserted city "Singapore" is correctly identified.CorrectPlease provide author biographys and photos.The biographys and photos are correct.Kindly check and confirm the corresponding affiliation processed is correctly identified.Corresponding author is both at Aff1 and Aff2. [ABSTRACT FROM AUTHOR]

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

Dynamic spectrum access has been promoted as a key technology in cognitive radio to achieve better spectrum utilization. It allows unauthorized secondary users to utilize the authorized primary user's spectrum opportunistically, when primary user is absent. Therefore, it is an important task for secondary users to observe primary user activity in the channel. Implementation of such spectrum access scheme, cognitive radio requires a fast and reliable spectrum sensing technique to monitor primary user activity. Among all the available spectrum sensing schemes, Energy detection is most widely used because of its low complexity. However, the conventional energy detection method produces poor performance in a lower signal regime, resulting in longer sensing duration and low detection probability. To overcome these challenges, we have proposed an adaptive decision threshold approach instead of a fixed decision threshold in a modified Three Event Energy Detection framework. Additionally, a new objective function is formulated prioritizing the PU over SU using a weight factor along with spectrum utilization factor which achieves a better trade-off between the miss detection and false alarm probability. Simulation results illustrate that the proposed approach has improved efficacy in decision error probability and detection performance compared to the existing methods. [ABSTRACT FROM AUTHOR]

Dynamic access to the spectrum is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is most often effective spectrum occupancy detection. In many cases, machine learning algorithms improve this detection's effectiveness. Given the recent trend of using federated learning, we present a federated learning algorithm for distributed spectrum occupancy detection. This idea improves overall spectrum-detection effectiveness, simultaneously keeping a low amount of data that needs to be exchanged between sensors. The proposed solution achieves a higher accuracy score than separate and autonomous models used without federated learning. Additionally, the proposed solution shows some sort of resistance to faulty sensors encountered in the system. The results of the work presented in the article are based on actual signal samples collected in the laboratory. The proposed algorithm is effective (in terms of spectrum occupancy detection and amount of exchanged data), especially in the context of a set of sensors in which there are faulty sensors. [ABSTRACT FROM AUTHOR]

This paper provides an overview of cognitive radio technology and its applications in the field of civil aviation. Cognitive radio technology is a relatively new and emerging field that allows for dynamic spectrum access and efficient use of spectrum resources. In the context of civil aviation, cognitive radio technology has the potential to enable more efficient use of the limited radio spectrum available for communication and navigation purposes. This paper examines the current state of cognitive radio technology, including ongoing research and development efforts, regulatory issues, and potential challenges to widespread adoption. The potential applications of cognitive radio technology in civil aviation are also explored, including improved spectrum utilization, increased safety and security, and enhanced situational awareness. Finally, the paper concludes with a discussion of future research directions and the potential impact of cognitive radio technology on the future of civil aviation. It is hoped that this paper will serve as a useful resource for researchers, engineers, and policy makers interested in the emerging field of cognitive radio technology and its potential applications in the field of civil aviation. [ABSTRACT FROM AUTHOR]

With the rapid development of technologies such as wireless communications and the Internet of Things (IoT), the proliferation of IoT devices will intensify the competition for spectrum resources. The introduction of cognitive radio technology in IoT can minimize the shortage of spectrum resources. However, the open environment of cognitive IoT may involve free-riding problems. Due to the selfishness of the participants, there are usually a large number of free-riders in the system who opportunistically gain more rewards by stealing the spectrum sensing results from other participants and accessing the spectrum without spectrum sensing. However, this behavior seriously affects the fault tolerance of the system and the motivation of the participants, resulting in degrading the system's performance. Based on the energy-harvesting cognitive IoT model, this paper considers the free-riding problem of Secondary Users (SUs). Since free-riders can harvest more energy in spectrum sensing time slots, the application of energy harvesting technology will exacerbate the free-riding behavior of selfish SUs in Cooperative Spectrum Sensing (CSS). In order to prevent the low detection performance of the system due to the free-riding behavior of too many SUs, a penalty mechanism is established to stimulate SUs to sense the spectrum normally during the sensing process. In the system model with multiple primary users (PUs) and multiple SUs, each SU considers whether to free-ride and which PU's spectrum to sense and access in order to maximize its own interests. To address this issue, a two-layer game-based cooperative spectrum sensing and access method is proposed to improve spectrum utilization. Simulation results show that compared with traditional methods, the average throughput of the proposed TL-CSAG algorithm increased by 26.3 % and the proposed method makes the SUs allocation more fair. [ABSTRACT FROM AUTHOR]

The primary objective of anti-jamming techniques is to ensure that the transmitted data arrives at the intended receiver without being disturbed or jammed with by any jamming signal or other hostile activities to ensuring the security of the communication system. Deep reinforcement learning (DRL) has been extensively utilized in solving the dynamic spectrum anti-jamming problem. However, most of existing DRL-based algorithms require lots of training time, which fails to adapt the fast-channging jamming environment. Our objective is to find a practical and fast-convergence anti-jamming learning solution. To achieve this, we redesign the DRL algorithm in the following two ways. First, we split the cycle of reinforcement learning into two parts: applying process and training process. Second, we use soft labels instead of rewards which bring more information. We further theoretically show that the information gain can help our proposed algorithm converge faster. Moreover, we also show that our labeled DRL algorithm is better than the idealized DRL-based scheme which can obtain the same information as the soft labels. Simulation results demonstrate that compared with existing DRL-based algorithms, our proposed algorithm reduces the number of iterations by up to 90%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cognitive Radio (CR) is an emerging technology that solves the spectrum inefficient problem in licensed spectrum pools by using dynamic spectral access (DSA). Spectrum Handoff plays an important role in DSA to ensure seamless and robust cognitive user (CU) services to maintain CR network (CRN) quality. In this article, we present the analytical model of pool-based spectral handoff process of two different licensing spectral pools under the Heterogeneous spectral environment (HetSE) of both Ad-HOC (opportunistic) and centralized (negotiated) CRNs. The concept of Intra-Pool and Inter-Pool spectrum handoff are considered to investigate the performance of CU in every possible dimension for developing an optimized and effective CRN. The Spectrum Handoff (SHO) performance metrics: probability mass function (PMF), link maintenance probability (LMP) and link failure probability (LFP) of the CU are derived using intra-pool and inter-pool spectrum handoff concepts under HetSE to investigate the characteristics of CRN for both opportunistic and negotiated spectrum access strategies. The proposed model offers the maximum value of LMPs as 0.944 and 0.270 in opportunistic situation and negotiated situation, respectively with varying PU arrival rate. The results show that both the strategies produce significantly different performance for pool based spectrum handoff under HetSE of CRN. The Monte-Carlo simulation results are also performed Python platform and compare with the theoretical results to validate the proposed model considering both PUs and CUs activity model. [ABSTRACT FROM AUTHOR]

Decentralized vehicle-to-everything (V2X) networks (i.e., C-V2X Mode-4 and NR-V2X Mode-2) utilize sensing-based semi-persistent scheduling (SPS) where vehicles sense and reserve suitable radio resources for Basic Safety Message (BSM) transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). Vehicles rely on these received periodic BSMs to localize nearby (transmitting) vehicles and infrastructure, referred to as cooperative awareness. Cooperative awareness enables line of sight and non-line of sight localization, extending a vehicle’s sensing and perception range. In this work, we first show that under high vehicle density scenarios, existing SPS (with prespecified RRIs) suffer from poor cooperative awareness, quantified as tracking error. Tracking error is defined as the difference between a vehicle’s true and estimated location as measured by its neighbors. To address the issues of static RRI SPS and improve cooperative awareness, we propose two novel RRI selection algorithms– namely, Channel-aware RRI (Ch-RRI) selection and Age of Information (AoI)-aware RRI (AoI-RRI) selection. Ch-RRI dynamically selects an RRI based on channel resource availability depending upon the (sparse or dense) vehicle densities, whereas AoI-RRI utilizes a novel information freshness metric, called Age of Information (AoI) to select a suitable RRI. Both adaptive RRI algorithms use SPS for selecting transmission opportunities for timely BSM transmissions at the chosen RRI. System-level simulations demonstrate that both proposed schemes outperform the SPS with fixed RRI in terms of improved cooperative awareness. Furthermore, AoI-RRI SPS outperforms Ch-RRI SPS in high densities, whereas Ch-RRI SPS is slightly better than AoI-RRI SPS in low densities.

As the demand for wireless services continues to grow, the need for efficient and effective management of the radio frequency (RF) spectrum becomes increasingly important. A spectrum management approach based on data analysis and interpretation i.e. data-driven, offers a solution as it provides valuable insights into spectrum usage patterns, demand, and the potential for harmful interference in shared spectrum scenarios. This paper focuses on the opportunities and challenges of incorporating diverse data sources into the management of spectrum, with a specific emphasis on spectrum sharing, particularly within database-assisted spectrum sharing systems. The benefits of adopting a data-driven approach to these systems are demonstrated through simulation of specific case studies. These studies indicate the potential for achieving up to a 60% greater density of spectrum use compared to conventional approaches, while also effectively managing harmful interference.

Deep reinforcement learning(DRL) has attracted much attention in multi-user intelligent dynamic spectrum access due to its advantages in sensing and decision making.However,the weak interpretability of deep neural networks(DNNs) makes DRL models vulnerable to backdoor attacks.In this paper,a non-invasive backdoor attack method with low-cost is proposed against DSA-oriented DRL models in cognitive wireless networks.The attacker monitors the wireless channels to select backdoor triggers,and generates backdoor samples into the experience pool of a secondary user's DRL model.Then,the trigger can be implanted into the DRL model during the training phase.The attacker actively sends signals to activate the triggers in the DRL model during the inference phase,inducing secondary users to take the actions set by the attacker,thereby reducing their success rate of channel access.A series of simulation show that the proposed backdoor attack method can reduce the attack cost by 20%~30% while achieving an attack success rate over 90%,and is suitable for three different DRL models.

Accurate threshold setting for energy detector is important for example in dynamic spectrum access. This requires accurate statistical distribution models of the observed energy. In this paper, we consider energy detection (ED) for $M$ -ary quadrature amplitude modulation (QAM) signals. The derivation of the exact solution of the distribution model (ES) requires all combinations of QAM signals in the observed signals based on the brute-force search and it leads to a significant computational cost. For this issue, this paper proposes three statistical distribution models which assume $M=\infty $ to avoid the brute-force search. Due to the assumption of $M$ , the proposed models are independent of $M$ and can handle adaptive modulation where $M$ can be changed dynamically. In the numerical evaluations, we compare the three proposed models with the other typical approximation models under additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. In addition, the proposed models are extended for more realistic scenario where imperfect synchronization is considered. The comprehensive numerical evaluations show that the first proposed model is most accurate among all considered models except ES but requires relatively high computational cost. The second proposed model where the observed energy is assumed to follow Gaussian distribution is the least complexity but can have reduced accuracy. The third proposed model based on skew-normal distribution can achieve comparable accuracy and less complexity compared to the first model.

Recently, the Gini index detector (GID) has been proposed as an alternative for data-fusion cooperative spectrum sensing, being mostly suitable for channels with line-of-sight or dominant multi-path components. The GID is quite robust against time-varying noise and signal powers, has the constant false-alarm rate property, can outperform many the state-of-the-art robust detectors, and is one of the simplest detectors developed so far. The modified GID (mGID) is devised in this article. It inherits the attractive attributes of the GID, yet with a computational cost far below the GID. Specifically, the time complexity of the mGID obeys approximately the same run-time growth rate of the GID, but has a constant factor approximately 23.4 times smaller. Equivalently, the mGID takes approximately 4 % of the computation time spent to calculate the GID test statistic, which brings a huge reduction in the latency of the spectrum sensing process. Moreover, this latency reduction comes with no performance loss with respect to the GID. [ABSTRACT FROM AUTHOR]

The exponential growth of Internet of Things (IoT) and 5G networks has resulted in maximum users, and the role of cognitive radio has become pivotal in handling the crowded users. In this scenario, cognitive radio techniques such as spectrum sensing, spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication. IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index, frequency bands, coding rate etc., to accommodate the above characteristics. Implementing the above learning methods on the embedded chip leads to high latency, high power consumption and more chip area utilisation. To overcome the problems mentioned above, we present DEEP HOLE Radio systems, the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency (RF) environment. DEEP HOLE Radio provides (i) an optimized deep learning framework with a good trade-off between latency, power and utilization. (ii) Complete Hardware-Software architecture where the SoC's coupled with radio transceivers for maximum performance. The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni directional antennas. The whole spectrum of knowledge has been extracted using GNU radio. These extracted features are used to train the proposed optimized deep learning models, which run parallel on Zynq-SoC 7000, consuming less area, power, latency and less utilization area. The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn, Long Term Short Memory (LSTM), Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN). The outcome shows that the proposed framework has outperformed the existing framework regarding the area, power and time. Moreover, the experimental results show that the proposed framework decreases the delay, power and area by 15%, 20% 25% concerning the existing RFlearn and other hardware constraint frameworks. [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]

Wireless communication demands rapidly increase due to the increase of Internet of Things applications, which leads researchers to build secondary networks that exploit the spectrum holes of primary networks. Cognitive radio (CR) technology is adopted in ad-hoc networks (AHNs) rather than infrastructure-based networks because AHNs have lower cost, higher coverage, and easier maintenance compared to infrastructure-based networks. Moreover, in-band-full-duplex (IBFD) in CR networks (CRNs) is gaining the interest of researchers. This mix between IBFD and CRNs brings a great enhancement in the network's performance due to efficient dynamic spectrum access. Therefore, we propose an adaptive FD-CRNs routing protocol that uses a common control channel. Adaptive FD communication is conducted in our protocol where the secondary users adapt their communication mode based on the primary users' activity on the spectrum. Communication modes used in our work are FD transmit and sense, FD transmit and receive, and sensing only. The performance of our protocol was evaluated using a java language simulator for IBFD-CRNs introduced previously in the literature. Also, we compare the performance of our protocol with three previous protocols, probabilistic and deterministic path selection in cognitive radio network, and multi-cast half-duplex routing protocol and broadcast full-duplex routing protocol. The performance metrics used are throughput and total execution time. [ABSTRACT FROM AUTHOR]