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Non-cooperative scenarios in cognitive wireless sensor network (CWSN) are often encountered with shadowing and hidden terminal issues. Cooperative spectrum sensing (CSS) can solve these issues but at the expense of large overhead. It is necessary to optimise the energy of battery-operated unlicensed users, also known as secondary users (SUs). The effect of fading and noise uncertainty is often overlooked when determining CSS performance. Energy efficiency (EE) is a comprehensive parameter that gives a complete picture of the overall performance of the CSS. There are several parameters that affect the EE of CSS. In this paper, the detector threshold is optimised to maximise the EE of the system. A system model is proposed to determine the EE of centralised CSS over different fading channels in noisy reporting conditions. An iterative algorithm is presented which determines the optimum detector threshold for which the EE is maximum. Results show that the optimum value of the detector threshold from the analytical model matches with simulation data. [ABSTRACT FROM AUTHOR]

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance. [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]

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]

Internet-of-Things (IoT) is being widely embraced with the number of connected devices growing rapidly. Moreover, IoT applications are emerging in diverse verticals such as connected cars, connected factories, and smart agriculture. For new business models, in order to meet key network performance indicators, connectivity must be flexible and agile. An integrated satellite-aerial-terrestrial network (I-SAT) has recently stimulated interest in providing wireless communication due to its high maneuverability, versatile deployment, and pervasive connectivity. The resource planning, task distribution, and action management of an I-SAT can be accomplished through effective acquisition, coordination, transmission, and aggregation of diverse information. This paper considers an I-SAT network, in which multiple unmanned aerial vehicles (UAVs) with aerial stations and a terrestrial base station (BS), in a cognitive setting, in the presence of satellite-receiver communication, are deployed to support smart vehicles on the ground. By taking into account different limitations and Quality of Service (QoS) constraints, the goal is to maximize the average throughput among users by jointly optimizing user association, BS/UAV transmission power, and UAV trajectory. The formulated problem is a non-convex optimization problem with a complicated expression that is hard to solve. To tackle this problem, an alternating iterative algorithm based on the block descent method is proposed. Precisely, the problem is subdivided into three subproblems, transmitter-vehicle association optimization, BS/UAV power allocation optimization, and UAV trajectory control. Then, in an iterative process, these subproblems are solved sequentially. The proposed solution uses a segment-by-segment technique, which breaks the complete UAV flight trajectory into smaller time segments to reduce computation time when the network service period is considerable. As a result, each time segment’s optimization can be solved more quickly. Furthermore, the paper presents the results of network simulations carried out to assess the efficiency of the proposed solution. The findings show that the presented scheme outperforms different benchmark schemes in terms of the average user throughput when observing multiple different scenarios. [ABSTRACT FROM AUTHOR]

With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%. [ABSTRACT FROM AUTHOR]

This paper presents a novel design of a compact, wideband tunable microstrip filtenna system for effective spectrum sensing in cognitive radio (CR) applications. The proposed filtenna structure has a total bandwidth of 1.63 GHz and flexible frequency scanning design throughout the frequency range from 1.93 to 3.56 GHz with high selectivity and narrow bandwidths ranging from 39.9 to 53 MHz . Frequency tuning is accomplished electrically via integrating a varactor diode into the filtenna construction. The filtenna is realized on a Rogers TMM4 substrate with h = 1.52 mm thickness and relative dielectric constant of ε r = 4.5 with dimensions of (25 × 35) mm 2 . The obtained gain and efficiency of the filtenna ranged from 0.7 to 2.26 dBi and 49% to 60%, respectively, within the tuning range. Simple biasing circuitry, wideband operation, and compact planar structure are distinctive and appealing aspects of the design. For the manufactured prototypes, a significant level of agreement is found between the simulated and measured results in terms of scattering parameter S 11 and radiation patterns at different operating frequencies. [ABSTRACT FROM AUTHOR]

This paper presents a promising solution to address the scarcity of spectrum resources and enhance spectrum efficiency in the context of cognitive radio (CR)-based soft-switching free-space optical (FSO)/terahertz (THz) radio frequency (RF) dual-hop amplify-and-forward (AF)–non-orthogonal multiple access (ROMANO) links. The impact of maximum tolerable interference power in the primary network, transmit power in the secondary transmitter, and maximum relay transmission power on the link are thoroughly studied. The numerical results ultimately validate the effectiveness of this link in improving performance, and a comparative analysis is conducted with the without-CR scheme, highlighting the distinctive characteristics of the proposed link. [ABSTRACT FROM AUTHOR]

Primary User Emulation Attack (PUEA) is the most serious concern in Cognitive Radio (CR). The PUEA are malevolent users attempt to imitate primary signals and confuse CR users to prevent them from accessing vacant frequency bands. The proposed technique detects energy and assigns an appropriate threshold value for identifying attackers in the network using unique Smart Threshold detection (STD). The free space propagation model and two ray ground models are considered for finding the attacks. The authentication confirmation process (ACP) is carried out for detecting multiple PUEAs; ACP uses DNA sequencing using Binary to Excess One (BEO). The objective of this paper is to identify the PUEAs from the network and not providing the vacant frequency bands to the PUEAs and the frequent bands should be used by the primary and the secondary user efficiently. Here the secondary user will find out whether the PUEA or primary user is accessing the vacant bands using the STD-ACP technique. The simulation process is executed in the MATLAB platform. The Proposed STD-ACP finds out the attack strength, probability of detection, probability of error, probability of false alarms, and identifies the number of PUEAs. By simulating the performance of the primary user will be increased while the PUEAs can be detached from the network. The proposed STD-ACP approach is compared with attack-aware threshold selection (AATS), optimal voting rule, and K-out-of-N rule methods respectively. [ABSTRACT FROM AUTHOR]

Energy harvesting (EH) from radio frequency realizes simultaneous wireless information delivery and energy transfer, which greatly reduces energy consumption in the energy-constrained Internet of things (IoT) networks. On the other hand, cognitive radio (CR) enables the unlicensed secondary user (SU) to access the spectrum resource authorized to the licensed primary user (PU) to improve spectrum efficiency. Thererfore, the integration between EH and CR can address two critical problems of energy limitation and spectrum scarcity in massive IoT wireless networks. In the state-of-art power splitting-based overlay spectrum sharing strategy, the secondary transmitter superimposes its own signals on the primary signals received and forwards the combined signals with the harvested power from the primary transmitter. However, due to low energy harvested only from one primary transmitter for the combined signal, PU system performance e.g., outage probability (OP) and system throughput, declines seriously. Moreover, a perfect direct link between the secondary transmitter and the secondary receiver must also be required in the scenario. An alternative spectrum sharing strategy based on time switching can increase harvested energy, but cooperative communication cannot be conducted at the EH period, which also reduces the overall system throughput. Based on the consideration, a Two-User Relay protocol in this paper is presented that an energy-constrained relay node, as a center hub, harvests energy from two transmitter, i.e., primary transmitter and secondary transmitter, and perform cooperative communication concurrently for PU and SU systems satisfying the PU's performance requirements (e.g., required target rate and OP). The expressions of OP and system throughput are derived and numerical simulations are made under the different system parameters, e.g., power splitting factor, power assignment factor, transmitting signal-to-noise ratio, channel fading coefficient, and required target rate. The results prove that our proposed Two-User Relay protocol improve spectrum sharing performance. Besides, the relay node can coordinate the harvested energy distribution between PU's signals and SU's signals to achieve the maximum system performance. Generally, the paper provides a new relaying concept for two-user communications in the future IoT wireless networks. [ABSTRACT FROM AUTHOR]

Summary: This paper analyzes the performance of a cooperative nonorthogonal multiple access (NOMA) in an underlay cognitive radio network aided by an energy harvesting relay. A secondary source transmits signal for two users, where a near user acts as a relay for the far user. The far user applies the selection combining (SC) approach on the signals which were relayed by the near user and received via direct path from the secondary source. We analytically derive the outage probability (OP) of each user separately, the overall system OP, and the throughput of the system. The impact of the power allocation coefficient of NOMA and energy harvesting parameters on outage is indicated. Further, the performance of the network is investigated with imperfection in successive interference cancellation (SIC), maximal ratio combining (MRC) at relay, and Nakagami‐m fading. The results derived analytically are supported by simulation in MATLAB. [ABSTRACT FROM AUTHOR]

This paper presents the design of an arc-shaped near-isotropic self-complementary antenna for spectrum sensing application. An arc-shaped dipole with horizontal and vertical arms is used to achieve a near isotropic radiation pattern. The radiation pattern improved by adjusting the horizontal and vertical arm lengths. Simulated and experimental results show that the proposed antenna has an impedance bandwidth of 146% (2.4–18.4 GHz) for VSWR ≤ 2 with a good radiation pattern. In order to quantify the antenna performance, antenna gain variation, bandwidth, efficiency, and size have been compared with previously reported designs. It is shown that the proposed arc-shaped antenna can achieve nearly isotropic radiation patterns with a maximum radiation efficiency of 92%. The isotropic performance of the antenna has been characterized by observing the radiation pattern and solid angle. The FR4 substrate is used as a dielectric with relative permittivity 4.4 and loss tangent of 0.02. ( ε r = 4.4, h = 1.6 mm) The simulated and measured results are in good comparison, and the proposed design is a suitable candidate for spectrum sensing. [ABSTRACT FROM AUTHOR]

Day by day, the demand for wireless systems is increasing while the available spectrum resources are not sufficient. To fulfil the demand for wireless systems, the spectrum hole (spectrum vacant) should be found and utilised very effectively. Cognitive radio (CR) is a device which intelligently senses the spectrum through various spectrum-sensing detectors. Based on the complexity and licensed user's information present with CR, the appropriate detector should be utilised for spectrum sensing. In this paper, a hybrid detector (HD) is proposed to determine the spectrum hole from the available spectrum resources. HD is designed based on an energy detector (ED) and matched detector (MD). Unlike a single detector such as ED or MD, HD can sense the signal more precisely. Here, HD can work on both conditions whether the primary user (PU) information is available or not. HD is analysed under heterogeneous environments with and without cooperative spectrum sensing (CSS). For CSS, four users were used to implement OR, AND, and majority schemes under low SNR walls. To design the HD, specifications were chosen based on the IEEE Wireless Regional Area Network (WRAN) 802.22 standard for accessing TV spectrum holes. For the HD model, we achieved the best results through OR rule. Under the low SNR circumstances at −20 dB SNR, the probability of detection (PD) is maximised to 1 and the probability of a false alarm (PFA) is reduced to 0 through the CSS environment. [ABSTRACT FROM AUTHOR]

In this paper, we consider an intelligent reflecting surface (IRS)-assisted downlink cognitive radio (CR) system, in which a secondary access point (SAP) communicates with multiple secondary users (SUs) without affecting multiple primary users (PUs) in the primary network and all nodes are equipped with multiple antennas. Our design objective is to maximize the achievable weighted sum rate (WSR) of SUs subject to the total transmit power constraint at the SAP and the interference constraints at PUs, by jointly optimizing the transmit precoding at the SAP and the reflecting coefficients at the IRS. To deal with the complex objective function, the problem is reformulated by employing the well-known weighted minimum mean-square error (WMMSE) method and an alternating optimization (AO)-based algorithm is proposed. Furthermore, a special scenario with only a single PU and multiple SUs is considered and AO algorithm is adopted again. It is worth mentioning that the proposed algorithm has a much lower computational complexity than the above algorithm without the performance loss. Finally, some numerical simulations have been provided to demonstrate that the proposed algorithm outperforms other benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an underlay cognitive non-orthogonal multiple access (NOMA)-based coordinated direct and relay transmission network with imperfect successive interference cancellation, imperfect channel state information, and co-channel interference caused by a multi-antenna primary transmitter. In the secondary network, a source communicates with a near user via direct link and with a far user through the assistance of multiple relays subject to transmit power constraints. Four relay selection schemes are proposed to enhance the performance of NOMA users and the overall system throughput. In our analysis, exact closed-form expressions for the outage probability (OP) of NOMA users and for the overall system throughput are derived. To provide further insights, a performance floor analysis is carried out considering two power-setting scenarios: (i) the transmit powers at the secondary source and relays go to infinity and (ii) the peak interference constraint goes to infinity. Towards real-time configurations, we also design a deep learning (DL) framework for the OP and system throughput prediction. Our results show that the deep neural network exhibits the lowest run-time prediction and root-mean-square error among the proposed DL models. Furthermore, the predicted results based on DL framework match with those of the analysis and simulation. [ABSTRACT FROM AUTHOR]

In this paper, we study a cognitive radio (CR)-enabled wireless powered communication network (WPCN) with an intelligent reflecting surface (IRS). A cognitive wireless powered communication network (CWPCN) consisting of one secondary wireless powered communication network, in which a hybrid access point broadcasts wireless energy to multiple users on downlink and receives information signals on uplink, shares the same spectrum with the existing primary wireless communication network. In this paper, we consider an underlay CWPCN in which the secondary network is regulated by a given interference temperature constraint (ITC) such that interference with the primary network is kept no greater than a predefined threshold. To enhance the performance of the CWPCN, we consider an IRS-assisted network in which multiple passive reflection elements are configured to reflect the signals in any desired phase/direction. Our main goal is to maximize the sum throughput of secondary users while managing the interference constraint. For this, we jointly optimize the uplink and downlink phase shift matrices of the IRS elements with optimal time slots for wireless energy transfer (WET) on downlink and wireless information transfer on uplink. In finding the optimal solution, the formulated optimization problem is non-convex, complex, and intractable. In this paper, we propose an alternating optimization (AO)-based solution with a successive convex approximation (SCA) technique. We show through simulation results that the proposed IRS-assisted network provides a significant enhancement in performance over the conventional CWPCN.

The integration of cognitive radio (CR) technology and non-orthogonal multiple access (NOMA) techniques, referred to as CR-based NOMA systems, has been recently configured as a promising solution to meet the requirements of beyond fifth-generation (B5G) networks, especially those related to Internet-of-Things (IoT) applications. With such integration, power domain NOMA allows multiple users to share the same orthogonal resource blocks. At the same time, CR technology enables opportunistic bandwidth utilization by permitting secondary users (SUs) to access the licensed spectrum frequency without interrupting the primary users’ (PUs) activities. To support the massive connectivity requirements of IoT-based networks, several multiple-access techniques have been recently combined with CR-based NOMA systems, including orthogonal multiple access (OMA) and multiple-antenna techniques. For example, in CR-based OMA-NOMA systems, the licensed frequency band is split into several channels, and a set of SUs is served on each channel using the NOMA technique. This paper provides an overview and analysis of the state-of-the-art CR-based NOMA network architecture. It summarizes the main design challenges related to the practical implementation of such systems. Furthermore, this paper presents the advances of combining CR-based NOMA with recent multiple-access techniques. The potential capabilities and the design challenges of such integrated systems are also investigated and discussed. On the other hand, this paper demonstrates the potential capabilities of deploying recent technologies in CR-based NOMA networks. The technologies include intelligent reflecting surfaces, terahertz communications, machine learning, unmanned aerial vehicles, and hybrid NOMA systems. Finally, future research directions and open issues are provided and discussed.

For a multiuser multiple-input–multiple-output (MIMO) overlay cognitive radio (CR) network, an opportunistic interference alignment (IA) technique has been proposed that allows spectrum sharing between primary users (PUs) and secondary users (SUs) while ensuring zero interference to the PU. The CR system consists of one PU and K SUs where the PU uses space-time water-filling (ST-WF) algorithm to optimize its transmission and in the process, frees up unused eigenmodes that can be exploited by the SU. The SUs make use of an optimal power allocation algorithm to align their transmitted signals in such a way their interference impairs only the PUs unused eigenmodes. Since the SUs optimal power allocation algorithm turns out to be an optimal beamformer with multiple eigen-beams, this work initially proposes combining the diversity gain property of space-time block codes, the zero-forcing function of IA and beamforming to optimize the SUs transmission rates. This proposed solution requires availability of channel state information (CSI), and to eliminate the need for CSI, this work then combines Differential Space-Time Block Coding (DSTBC) scheme with optimal IA precoders (consisting of beamforming and zero-forcing) to maximize the SUs data rates. Simulation results confirm the accuracy of the proposed solution. [ABSTRACT FROM AUTHOR]

Security is an important consideration when delivering information-aware messages to vehicles that are far away from the current location of the information-sending vehicle. This information helps the receiver to save fuel and time by making wise decisions to avoid damaged or blocked roads. To ensure the safety and security of this type of information using blockchain technology, we propose a new cognitive vehicular communication scheme to transfer messages from source to destination. Due to spectrum scarcity in vehicular networks, there needs to be a wireless medium available for every communication link since vehicles require it to communicate. The primary user (PU) makes a public announcement about a free channel to all secondary users nearby and only gives it to authentic vehicles. The authenticity of vehicles is guaranteed by a roadside unit (RSU) that offers secure keys to any vehicle that joins this blockchain network. Those who participate in this network must pay a certain amount and receive rewards for their honesty that exceed the amount spent. To test the performance of various parameters, the proposed scheme utilizes the Ethereum smart contract and compares them to blockchain and non-blockchain methods. Our results show a minimum delivery time of 0.16 s and a minimum overhead of 350 bytes in such a dynamic vehicle environment. [ABSTRACT FROM AUTHOR]

Abstract With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance.

A recent study has revealed that incorporating intelligent reflecting surfaces (IRS) into wireless systems improves their performance within various signal propagation conditions. This research paper focuses on evaluating the spectrum-sensing abilities of cognitive radio in wireless environments enhanced by IRS technology. To achieve this, the paper develops a statistical model to describe the connections between the primary user (PU) and IRS, as well as between IRS and the secondary user (SU). This model calculates a precise approximation of the probability density function (PDF) of the end-to-end instantaneous signal-to-noise ratio (SNR). The SNR considers both the PU-to-IRS link, which follows a Rayleigh fading distribution, and the IRS-to-SU link, which follows an FTR fading distribution. By utilizing this PDF distribution, the paper derives analytical expressions for the average probability of detection (APD) for single and cooperative secondary users. These APD expressions are then employed to establish the exact closed-form expression for the average area under the receiver operating characteristics (AUC), system throughput, and transmission probability of the SU. To validate these theoretical findings, the paper conducts Monte Carlo simulations. The results from these simulations indicate an improved detection probability when operating in an IRS-enhanced wireless propagation environment. Consequently, the study suggests that incorporating IRS technology can significantly enhance the reliability of communication systems based on cognitive radio principles. [ABSTRACT FROM AUTHOR]

Spectrum sensing (SS) is substantial demand for enabling cognitive radio (CR) systems. In recent years, digital beamforming (DBF) is exploited for performance enhancement of multiple antenna elements or uniform linear array (ULA) based spectrum sensing techniques. Generally, a ULA suffers from its high side lobe level (SLL) and has a relatively low realized array gain. Subsequently, the ULA-based CR receiver is compelled to provide low detection performance. Furthermore, the efforts to increase the realized array gain are still not sufficient to dramatically increase the detection performance, because the problem of high SLL remains a challenging issue. In this paper, a highly efficient approach is introduced for performance enhancement of ULA-based SS techniques using side lobe level reduction (SLLR) beamforming that is applied to the receiving ULA. Worthy, a large SLLR combined with maintaining the same half power beamwidth (HPBW) as the original array, results in a synthesized array with a significantly improved gain. Thence, both the received signal-to-noise ratio (SNR) and signal-to-interference plus noise ratio (SINR) of the combined received signal are significantly enhanced at the CR receiver, which would considerably increase the detection capability of the proposed system. The proposed approach is investigated by integrating the SLLR-based antenna array with the generalized likelihood ratio test/direction of arrival (GLRT/DOA)-based SS technique that is denoted as GD technique. In contrast to the ULA, the synthesized array's excitation coefficients are non-uniform, necessitating special signal processing to incorporate it in the received signal model and derive closed-form expressions for the probability of detection, probability of false alarm, and decision threshold, all of which are already reported in this work. Several simulation scenarios are performed to validate the supremacy of the proposed SS approach compared to the traditional single antenna element based techniques and other state of the art beamforming based techniques. [ABSTRACT FROM AUTHOR]

Abstract 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.

Intelligent reflecting surface (IRS) technology has found extensive application in the wireless communication domain, offering significant enhancements in communication performance by manipulating the reflection of electromagnetic waves. This research article delves into the domain of UAV-assisted wideband cognitive radio networks augmented by IRS and underpinned by sensing-based spectrum sharing techniques. We introduce UAVs as alternative solutions for both primary and secondary base stations to optimize the management of spectral resources. Our primary objective centers around the joint optimization of both the trajectories of the primary and secondary UAVs, power allocation at the secondary UAVs, reflection coefficients of intelligent reflecting surfaces, sensing time, and subcarrier allocation, all aimed at maximizing the achievable rate of secondary users. Given that the problem at hand is non-convex, we employ deep reinforcement learning algorithms for resolution. To address the challenge of mixed-action spaces, we implement a novel Dueling DQN-Twin Delayed Deep Deterministic policy gradient (DDQN-TD3) algorithm. The simulation outcomes illustrate that the methodology introduced in this paper substantially amplifies the performance of CR system when contrasted with benchmark methods. This is evident in the improved spectrum efficiency, elevated data transmission rates, and the minimization of interference with the primary network. [ABSTRACT FROM AUTHOR]

Abstract With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%.

The sudden outbreak of the novel coronavirus disease in 2019, known as COVID-19 has impacted the entire globe and has forced governments of various countries to a partial or full lockdown in the fear of the rapid spread of this disease. The major lesson learned from this pandemic is that there is a need to implement a robust system by using non-pharmaceutical interventions for the prevention and control of new contagious viruses. This goal can be achieved using the platform of the Internet of Things (IoT) because of its seamless connectivity and ubiquitous sensing ability. This technology-enabled healthcare sector is helpful to monitor COVID-19 patients properly by adopting an interconnected network. IoT is useful for improving patient satisfaction by reducing the rate of readmission in the hospital. The presented work discusses the applications and technologies of IoT like smart and wearable devices, drones, and robots which are used in healthcare systems to tackle the Coronavirus pandemic This paper focuses on applications of cognitive radio-based IoT for medical applications, which is referred to as "Cognitive Internet of Medical Things" (CIoMT). CIoMT is a disruptive and promising technology for dynamic monitoring, tracking, rapid diagnosis, and control of pandemics and to stop the spread of the virus. This paper explores the role of the CIoMT in the health domain, especially during pandemics, and also discusses the associated challenges and research directions. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a novel design of a compact, wideband tunable microstrip filtenna system for effective spectrum sensing in cognitive radio (CR) applications. The proposed filtenna structure has a total bandwidth of $$1.63\,\text{GHz}$$ 1.63 GHz and flexible frequency scanning design throughout the frequency range from $$1.93\,\text{ to }\,3.56\,\text{ GHz}$$ 1.93 to 3.56 GHz with high selectivity and narrow bandwidths ranging from $$39.9\,\text{to}53\,\text{MHz}$$ 39.9 to 53 MHz . Frequency tuning is accomplished electrically via integrating a varactor diode into the filtenna construction. The filtenna is realized on a Rogers TMM4 substrate with $$h=1.52\,\text{mm}$$ h = 1.52 mm thickness and relative dielectric constant of $${\varepsilon }_{r}=4.5$$ ε r = 4.5 with dimensions of $$(25\times 35)\, {\text{mm}}^{2}$$ ( 25 × 35 ) mm 2 . The obtained gain and efficiency of the filtenna ranged from $$0.7$$ 0.7 to $$2.26\,\text{dBi}$$ 2.26 dBi and 49% to 60%, respectively, within the tuning range. Simple biasing circuitry, wideband operation, and compact planar structure are distinctive and appealing aspects of the design. For the manufactured prototypes, a significant level of agreement is found between the simulated and measured results in terms of scattering parameter $${\text{S}}_{11}$$ S 11 and radiation patterns at different operating frequencies.

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.

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]

To solve the power supply problem of battery-limited Internet of Things devices (IoDs) and the spectrum scarcity problem, simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technology were integrated into the Internet of Things (IoT) network to build a cognitive radio IoT (CRIoT) with SWIPT. In this network, secondary users (SUs) could adaptively switch between spectrum sensing, SWIPT, and information transmission to improve the total throughput. To solve the complicated multi-dimensional resource allocation problem in CRIoT with SWIPT, we propose a multi-dimensional resource allocation algorithm for maximizing the total throughput. Three-dimensional resources were jointly optimized, which are time resource (the duration of each process), power resource (the transmit power and the power splitting ratio of each node), and spectrum resource, under some constraints, such as maximum transmit power constraint and maximum permissible interference constraint. To solve this intractable mixed-integer nonlinear program (MINLP) problem, firstly, the sensing task assignment for cooperative spectrum sensing (CSS) was obtained by using a greedy sensing algorithm. Secondly, the original problem was transformed into a convex problem via some transformations with fixed-power splitting ratio and time switching. The Lagrange dual method and subgradient method were adopted to obtain the optimal power and channel allocation. Then, a one-dimensional search algorithm was used to obtain the optimal power splitting ratio and the time switching ratio. Finally, a heuristic algorithm was adopted to obtain the optimal sensing duration. The simulation results show that the proposed algorithm can achieve higher total system throughput than other benchmark algorithms, such as a greedy algorithm, an average algorithm, and the Kuhn–Munkres (KM) algorithm. [ABSTRACT FROM AUTHOR]

In cognitive radio (CR) networks, a secondary user access control (SUAC) technique has been designed to enhance spectrum efficiency, in which a jamming signal is deliberately injected to maintain reliable sensing performance of authorized secondary users (A-SUs) and degrades unauthorized secondary users (UA-SUs) spectrum sensing results. We consider the problem of jamming signal design in massive multiple-input multiple-output (MIMO) CR networks wherein each primary user has a larger number of antennas that coexists with multiple secondary users. In this paper, we propose a jamming signal design framework that combines maximizing the jammer’s influences on UA-SUs and minimizing on A-SUs. The resulting problem is a non-convex quadratically constrained quadratic programming (QCQP) problem, and a semidefinite relaxation (SDR) method can be one of the approximate solutions but cannot meet our stringent constraints and lacks jamming efficiency. We propose a novel optimization algorithm based on the $K$ -best methodology to design the jamming signal. Simulation results show the effectiveness of the proposed $K$ -best based SUAC method in improving the spectrum sensing performance of A-SUs.

Spectrum sensing (SS) is substantial demand for enabling cognitive radio (CR) systems. In recent years, digital beamforming (DBF) is exploited for performance enhancement of multiple antenna elements or uniform linear array (ULA) based spectrum sensing techniques. Generally, a ULA suffers from its high side lobe level (SLL) and has a relatively low realized array gain. Subsequently, the ULA-based CR receiver is compelled to provide low detection performance. Furthermore, the efforts to increase the realized array gain are still not sufficient to dramatically increase the detection performance, because the problem of high SLL remains a challenging issue. In this paper, a highly efficient approach is introduced for performance enhancement of ULA-based SS techniques using side lobe level reduction (SLLR) beamforming that is applied to the receiving ULA. Worthy, a large SLLR combined with maintaining the same half power beamwidth (HPBW) as the original array, results in a synthesized array with a significantly improved gain. Thence, both the received signal-to-noise ratio (SNR) and signal-to-interference plus noise ratio (SINR) of the combined received signal are significantly enhanced at the CR receiver, which would considerably increase the detection capability of the proposed system. The proposed approach is investigated by integrating the SLLR-based antenna array with the generalized likelihood ratio test/direction of arrival (GLRT/DOA)-based SS technique that is denoted as GD technique. In contrast to the ULA, the synthesized array's excitation coefficients are non-uniform, necessitating special signal processing to incorporate it in the received signal model and derive closed-form expressions for the probability of detection, probability of false alarm, and decision threshold, all of which are already reported in this work. Several simulation scenarios are performed to validate the supremacy of the proposed SS approach compared to the traditional single antenna element based techniques and other state of the art beamforming based techniques.

This paper presents a promising solution to address the scarcity of spectrum resources and enhance spectrum efficiency in the context of cognitive radio (CR)-based soft-switching free-space optical (FSO)/terahertz (THz) radio frequency (RF) dual-hop amplify-and-forward (AF)–non-orthogonal multiple access (ROMANO) links. The impact of maximum tolerable interference power in the primary network, transmit power in the secondary transmitter, and maximum relay transmission power on the link are thoroughly studied. The numerical results ultimately validate the effectiveness of this link in improving performance, and a comparative analysis is conducted with the without-CR scheme, highlighting the distinctive characteristics of the proposed link.

Communication technologies are evolving drastically in recent years. However, the scarcity of spectrum began to appear with the accelerating usage of various communication technologies, as well as the preservation of traditional channel access methods. Cognitive Radio (CR) is an innovative solution for spectrum scarcity. Spectrum sensing is a key task of the CR life-cycle that gains significance as spectrum holes can be detected during this task. This paper studies and compares the performance of the KMeans-based spectrum sensing technique with the non-cooperative spectrum sensing technique, the And-based spectrum sensing technique, and the Or-based spectrum sensing technique in stationary and mobile CR networks (CRNs). The effect of the fading channel type has also been considered. Small-scale CRNs were simulated using the third version of the network simulator. In this context, two models have been developed. The first was built based on the well-known $\kappa -\mu $ general fading model to simulate the fading effects. The latter is the noise model to simulate different noise conditions. The results reveal that spectrum sensing techniques provide better performance in stationary networks as compared to mobile networks. Further, our experimental results show that at least three secondary users and about 1500 samples are needed to reach acceptable performance. In addition, the results show that the KMeans-based technique slightly outperforms the Or-based technique, especially in highly noisy environments and under severe fading channels.

In recent days there the farmers who are having enormous expanses of land are facing heavy loss due to sudden changes like monsoon and presence of high amount of CO2. Therefore, this article presents a Cognitive Radio (CR) model for implementing in agricultural field to predict the parameters like CO2 effect and strength of received signal. In order to monitor these parameters a new fanged technique with low cost implementation is necessary. Therefore, a low cost Effective CR Agricultural model has been incorporated by integrating the algorithms in two folds. The proposed method has the advantage that the strength of received signal will be higher even if the distance is too long. In addition the effect of CO2 will also be reduced. The projected technique is compared with other existing techniques where, the implementation cost is much lesser and also the CR model proves to be a precise technique for agricultural applications. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have gained much popularity for a wide variety of applications that do not require human operators, but also face the spectrum scarcity problem. To address this problem, a cognitive UAV network (CUAVN) framework based on cognitive radio (CR) technology is formulated in this article. On the basis of this, the sequential probability ratio test (SPRT) is used as a fundamental cooperative spectrum sensing (CSS) scheme to significantly reduce the average number of samples, but the uncertainty of its detection delay has a negative impact on the spectrum-sensing performance and the achievable throughput of CUAVNs, especially in the multiband spectrum sensing. Considering the location flexibility and uneven distribution density of UAVs, we design an intraframe CSS structure, with aim of achieving CSS between microsensing slots. Furthermore, a sequential maximal truncation (SMT) is proposed to realize a quick multiband spectrum sensing for delay-constraint CUAVNs. Compared to uniform tail truncation (UTT), simulation results show that the proposed quick multiband spectrum sensing with the help of SMT requires fewer sensing times in support of better sensing performance and achievable throughput, especially under a strict delay constraint. [ABSTRACT FROM AUTHOR]

This article proposes a simultaneous wireless information and power transfer (SWIPT)-enabled Internet of Things (IoT)-based cognitive nonorthogonal multiple access (NOMA) with coordinated direct and relay transmission (CDRT) system. It incorporates overlay cognitive radio (CR) and time switching (TS)-based SWIPT technology to enhance spectrum utilization and energy efficiency (EE). The proposed system comprises a primary network having a primary transmitter and its intended NOMA receivers (UE1 and UE2), accompanied by an energy-constrained secondary transmitter (ST) and its designated receiver (IoT-U). The primary transmitter communicates directly with its strong user UE1 and exploits the ST as an IoT relay to communicate with a weak user UE2. The IoT-relay node employs a TS-based receiver architecture and a decode-and-forward (DF) protocol to convey the weak user’s information along with its own information by following the NOMA principle. We evaluate the performance of the proposed system by considering both the perfect and imperfect successive interference cancellation (SIC) at the legitimate users over Nakagami- ${m}$ fading in terms of outage probability (OP), system throughput, and EE. Moreover, we propose an iterative algorithm to minimize the OP by optimizing the TS factor. Furthermore, the impact of key parameters is also highlighted, which lays the guidelines for the practical design of energy-efficient and spectrum-efficient futuristic wireless communication networks. [ABSTRACT FROM AUTHOR]

Recently, radar frequency bands have attracted attention as candidates for cognitive radio owing to their wide bandwidth but low utilization. When spectrum sensing is performed in radar bands, sliding-window-based detection can be used to exploit the sparsity of the radar pulse signal in the time domain and obtain a sufficient number of samples. The detection performance and sensing time depend on the configuration of the windows. The detection performance was optimized when the window size was equal to the pulsewidth. However, the pulsewidth is generally unknown. Another way to increase the detection performance is to obtain more samples of the window. However, this causes large computation overhead owing to the sparsity of radar signals. Therefore, we propose an adaptive-sliding-window-based detection scheme to address these problems. First, the window is adaptively applied. Second, a pulsewidth estimation algorithm is proposed to approximate the window size to the pulsewidth for each detection. Additionally, we demonstrate the performance improvement of the proposed scheme and evaluate the estimation error of the proposed algorithm via simulations. [ABSTRACT FROM AUTHOR]

This article proposes deep learning (DL) framework constructed using deep autoencoder (DAE) to detect the malicious nodes in an Internet of Things (IoT) network assisted by the cognitive radio (CR) technology. In the IoT era, a plethora of nodes are connected to the network for the purpose of collecting and exchanging data. CR technology finds its role in IoT applications because of its ability to efficiently exploit the available spectrum. In this article, we consider IoT nodes as secondary users that perform cooperative spectrum sensing (CSS). Specifically, these IoT nodes sense the spectrum and send their reports to the fusion center (FC) to determine whether the spectrum is occupied by the primary user or not. In such a scenario, the existence of malicious IoT nodes might mislead the FC. To determine which of these IoT nodes are benevolent and which are malicious is crucial. We adopt a DAE-based DL framework, called DAE-TRUST, to detect malicious nodes in CR-assisted IoT. The proposed DAE-TRUST is able to identify the malicious nodes, whose reports can be excluded from the spectrum detection process. Simulation results, in six different real-world environments, show that our DAE-TRUST enhances the performance of CSS in IoT applications. [ABSTRACT FROM AUTHOR]

The massive number of Machine-Type Communication Devices (MTCDs) coexisting with Cellular User Equipment (CUE), in addition to the diverse Quality-of-Service (QoS) requirements of M2M communications and cellular communications, present significant implementation challenges due to interference, congestion, and spectrum scarcity. This makes resource allocation an important but challenging problem. In this article, clustered Cognitive Machine-to-Machine (CM2M) communications underlaying cellular networks is proposed to solve this problem. In this system, MTCDs are grouped in clusters based on their spatial locations and communicate with the Base Station (BS) via a Machine-Type Communication Gateway (MTCG). Underlay Cognitive Radio (CR) is employed so that MTCDs within a cluster can share the spectrum of neighbouring CUE. A joint resource-power allocation problem is formulated and solved using a two-phase resource and power allocation scheme. The goal is to maximize the uplink sum-rate of the neighbouring CUE and clustered MTCDs while satisfying interference, power, and minimum data rate constraints. Simulation results are presented which show that the proposed scheme significantly improves the sum-rate of the network compared to other schemes while satisfying the constraints. [ABSTRACT FROM AUTHOR]

In uplink non-orthogonal multiple access (NOMA) channels, the existing cooperative successive interference cancellation (SIC) and power control (PC) schemes lack the capability of achieving the full capacity region, which restricts the outage performance of uplink NOMA users. For the uplink cognitive radio inspired NOMA system, we propose a new rate splitting (RS) strategy to maximize the achievable rate of the secondary user without deteriorating the primary user's outage performance. Based on the interference threshold and its own channel gain, the secondary user adaptively conducts RS, transmit power allocation and SIC, which utilizes the transmit power efficiently. The closed-form expression for the outage probability is derived for the secondary user. Numerical results show that the proposed RS scheme achieves the best outage performance for the secondary user among the existing cooperative SIC and PC schemes. [ABSTRACT FROM AUTHOR]

In this article, a cross-layer parameter optimization is considered for H.264 scalable video transmission in cognitive radio enabled device-to-device networks. Adaptive modulation and coding is considered at the physical layer along with video layer aware automatic repeat request (VLA-ARQ) at the link layer. The VLA-ARQ scheme is aware of video characteristics. Scalable video coding (SVC) based video streaming is carried out at the application layer. A novel cognitive radio network frame structure is proposed accommodating cooperative spectrum sensing (CSS), relaying primary user’s (PU) transmission, and secondary users’ (SU) opportunistic transmission to maximize quality of experience (QoE) and to minimize total power consumption. In SVC-based video streaming, successful reception of a particular video layer is dependent upon the successful reception of its higher layers, which influences the eventual QoE at the user end. A set of optimal values of CSS time duration, sensing decision threshold, cooperation, and transmission power control are obtained under the constraints of detection reliability, PU spectrum efficiency, and SU power budget. Extensive simulation results with respect to the system parameters show the efficacy of the proposed system. The numerical results demonstrate that the proposed technique offers more than 14% gain in overall utility with the proposed CSS and cross-layer parameter optimization strategy. [ABSTRACT FROM AUTHOR]