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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]

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]

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 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]

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]

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]

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]

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]

To develop sustainable fifth generation (5G) wireless networks and utilize the unused spectrum, this paper focuses on cognitive radio (CR) based wireless information and energy transmissions from an unmanned aerial vehicle (UAV) to multiple low-power ground terminals (GTs). By practically considering the location-dependent air-to-ground (A2G) channel states and the non-linear energy harvesting (EH), we propose a dynamic fly-hover-transmit scheme, where the UAV successively flies between GTs, and hovers close to each GT for efficient wireless energy transfer (WET) or wireless information transfer (WIT) when the primary user (PU) is idle. By causally and optimally determining the UAV’s mobility and transmit power for each selected transmission mode (WIT, WET, or being silent), we formulate the UAV’s sum-throughput maximization over all GTs as a constrained Markov decision process (MDP) problem with battery energy constraints at all GTs and the UAV. Due to the infinitely large MDP system state space, this problem is difficult to solve. We then decompose this problem into two subproblems, by first deciding the UAV’s transmission mode and power above a given GT, and then optimizing the UAV movement policy over multiple GTs. In the first subproblem, we propose an approximate to the complicated MDP value function of low complexity in closed-form, and then analytically derive the threshold-based suboptimal transmission policies. In the second subproblem, we optimally solve a simple-but-fundamental two-GT case, and then extend the general location-dependent GT weight design to an efficient suboptimal UAV movement policy. Simulation results show the significantly improved system performance under the proposed suboptimal policies over various benchmarks in dynamic networks. [ABSTRACT FROM AUTHOR]

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]

With the advent of smarter technologies in LTE, often the bands used for lower technologies remain unoccupied in cellular networks (CN). To utilize those, in this paper, a new paradigm of cognitive radio has been proposed for IoT, where the nodes of a self-organized opportunistic ad hoc network, act as the secondary users (SU) to use the white spaces of the co-existing cellular network. Each SU of the ad hoc network, in a self-organized fashion, collaborates with other SUs to reduce the impact of cognitive users on the primary network and also to improve the performance of the ad hoc network. Since it does not require the control of the core network, the device to device communication proposed in 5G CN, also can apply this technique for sharing the cellular spectrum without the control of the core network. We have developed distributed algorithms for cooperative channel sharing to reduce the spectrum latency as well as to improve the channel utilization. The proposed algorithms not only improve the system performance in offloading the traffic but also reduce the control overhead of the backbone network. Simulation results show that the proposed cooperative techniques perform better in terms of channel utilization, energy-efficiency, and call block/drop rate with respect to other cooperative approaches with insignificant additional message overhead at users' end. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and spectrum sharing (SS) are two emerging multiple access technologies for efficient spectrum utilization in future wireless communications standards. In this paper, we present the performance analysis of a NOMA-based cooperative relaying system (CRS) in an underlay spectrum sharing scenario, considering a peak interference constraint (PIC), where the peak interference inflicted by the secondary (unlicensed) network on the primary-user (licensed) receiver (PU-Rx) should be less than a predetermined threshold. In the proposed system the relay and the secondary-user receiver (SU-Rx) are equipped with multiple receive antennas and apply selection combining (SC), where the antenna with highest instantaneous signal-to-noise ratio (SNR) is selected, and maximal-ratio combining (MRC), for signal reception. Closed-form expressions are derived for the average achievable rate and outage probabilities for SS-based CRS-NOMA. These results show that for large values of peak interference power, the SS-based CRS-NOMA outperforms the CRS with conventional orthogonal multiple access (OMA) in terms of spectral efficiency. The effect of the interference channel on the system performance is also discussed, and in particular, it is shown that the interference channel between the secondary-user transmitter (SU-Tx) and the PU-Rx has a more severe effect on the average achievable rate as compared to that between the relay and the PU-Rx. A close agreement between the analytical and numerical results confirm the correctness of our rate and outage analysis. [ABSTRACT FROM AUTHOR]

In this paper, we present a power control algorithm in cognitive radio multiple-input–multiple-output (MIMO) system over time-varying fading channels based on the utility-based framework. In particular, a more general objective function framework is proposed under a long- or short-term transmitting and interference power constraints for secondary users, which results the optimization problem is nonconvex. A stochastic optimization algorithm is proposed based on an improved quantum-behaved particle swarm optimization (IQPSO), which can solve the nonconvex optimization problem efficiently. The acceleration coefficients are adjusted with time-varying equations which can help the particles jump out the local optimum. Due to the characteristics of wave function of quantum Delta potential well model, IQPSO can get better optimal solutions in search space for the stochastic optimization problem. To show the efficiency of the proposed algorithm, the utilities of the MIMO CR system got by IQPSO are compared with other approaches in the literature in different case studies, which show that the proposed algorithm can solve the nonconvex problem efficiently and achieve significant throughput. [ABSTRACT FROM AUTHOR]

In this paper, we study a generalized framework that combines the three major techniques for 5G communication systems such as the multi-user multi-input multi-output (MuMIMO) techniques for spectral efficiency enhancement, the cognitive radio (CR) techniques for spectrum sharing, and the simultaneous wireless information and power transfer (SWIPT) techniques for convenient power supplies, which is called a MuMIMO-CR-SWIPT network. In this system, we have one base-station that simultaneously supports multiple information decoding (ID) and energy harvesting users under a condition that interference power to the primary ID (P-ID) receivers stays below a certain threshold. With this scenario, our goal is to design an optimal precoder that maximizes the sum-utility cost function for the ID users while satisfying the transmit power constraint at the BS, the energy requirement at each EH user, and the interference power constraint at each P-ID user. As we consider a general sum-utility cost function that puts together different target utilities in a general MuMIMO-CR-SWIPT environment, the previous works for each of the MuMIMO, CR, and SWIPT systems are casted as particular solutions of our framework. The problem has been considered to be challenging, since the weighted minimum mean-squared error problem transformation no longer resolves the non-convexity of the original problem. In this paper, we settle such an issue by demonstrating that the WMMSE transformation guarantees zero-duality gap between the primal and dual problems. Based on the observation, we attain the optimal precoder by solving the dual problem through the sub-gradient ellipsoid method. We also propose a simplified algorithm for the case of a single ID user, which is shown to achieve the globally optimum. Finally, we demonstrate the optimality and efficiency of the proposed algorithms through numerical simulation results. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing (OFDM) is a promising technology for communication systems. This paper investigates the impacts of unknown noise variance on the popular spectrum sensing scheme, i.e., energy detection, for OFDM cognitive radios over multipath fading channels. To study the effects of unknown parameters on the energy detector, a new maximum-likelihood estimation of noise and signal powers employing the cyclic prefix of OFDM is presented in this paper. The mean values and Cram $\acute{\text{e}}$ r–Rao lower bounds of the estimation are obtained. Furthermore, the performances of the energy detector for both hypotheses, i.e., false-alarm rate and detection probability, under the influence of unknown noise variance are validated by both simulation and analytical results. The assessment on the required number of samples for the proposed energy detector is conducted, which indicates that an amount of 40–50% samples can be saved compared to the conventional energy detector. [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, the secondary users (SUs) sense the spectrum licensed to the primary users (PUs) to identify and possibly transmit over temporarily unoccupied channels. Cooperative sensing was proposed to improve the sensing accuracy, but in heterogeneous scenarios, SUs do not contribute equally to the cooperative sensing result because they experience different received PU signal quality at their sensors. In this paper, a two-layer cooperative game is developed for distributed sensing and access in multichannel CR ad hoc networks, where the SUs’ transmission opportunities are commensurate with their sensing contributions, thus fostering cooperation and eliminating free-riders. Numerical results show that the proposed two-layer game is computationally efficient and outperforms previously investigated collaborative sensing and spectrum access approaches in heterogeneous multichannel CR scenarios in terms of energy efficiency, throughput, SU fairness, and complexity. Moreover, it is demonstrated that this game is robust to changes in the network topology and the number of SUs. Finally, a new physical-layer approach is proposed to distribute the network-level miss-detection constraints fairly among the interfering SUs for guaranteed PU protection and demonstrate the performance advantages of the AND-rule combining of spectrum sensing results for heterogeneous SUs. [ABSTRACT FROM AUTHOR]

Spectrum sensing is a prominent functionality to enable dynamic spectrum access (DSA) in cognitive radio (CR) networks. It provides protection to primary users (PUs) from interference and creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce the number of false alarms and missed detections. Continuous sensing for a long time incurs cost in terms of increased energy consumption; thus, spectrum sensing ought to be energy efficient to ensure the prolonged existence of CR devices. This paper focuses on using of history to help achieve energy-efficient spectrum sensing in infrastructure-based CR networks. The scheme employs an iteratively developed history processing database that is used by CRs to make decisions about spectrum sensing, subsequently resulting in reduced spectrum scanning and improved energy efficiency. Two conventional spectrum sensing schemes, i.e., energy detection (ED) and cyclostationary feature detection (CFD), are enriched by history to demonstrate the effectiveness of the proposed scheme. System-level simulations are performed to investigate the sensitivity of the proposed history-based scheme by performing detailed energy consumption analysis for the aforementioned schemes. Results demonstrate that the employment of history ensued in improved energy efficiency due to reduced spectrum scanning. This paper also suggests which spectrum sensing scheme can be the best candidate in a particular scenario by looking into computational complexity before comparative analysis is presented with other states of the art. [ABSTRACT FROM PUBLISHER]

This paper presents a comprehensive cognitive management framework for spectrum selection in cognitive radio (CR) networks. The framework uses a belief vector concept as a means to predict the interference affecting the different spectrum blocks (SBs) and relies on a smart analysis of the scenario dynamicity to properly determine an adequate observation strategy to balance the tradeoff between achievable performance and measurement requirements. In this respect, the paper shows that the interference dynamics in a given SB can be properly characterized through the second highest eigenvalue of the interference state transition matrix. Therefore, this indicator is retained in the proposed framework as a relevant parameter to drive the selection of both the observation strategy and spectrum selection decision-making criterion. This paper evaluates the proposed framework to illustrate the capability to properly choose among a set of possible observation strategies under different scenario conditions. Furthermore, a comparison against other state-of-the-art solutions is presented. [ABSTRACT FROM PUBLISHER]

In this paper, we characterize the worst-case interference for a finite-area TV white space heterogeneous network using the tools of stochastic geometry. We derive closed-form expressions on the probability distribution function (PDF) and an average value of the aggregate interference for various values of path loss exponent under Rayleigh fading channel. The proposed characterization of the interference is simple and can be used in improving the spectrum access techniques. Using the derived PDF, we demonstrate the performance gain in the spectrum detection of an eigenvalue-based detector for cognitive radio networks. [ABSTRACT FROM AUTHOR]

In this paper, a multifunctional reconfigurable filter that can work in three operating modes has been presented. The three operating modes are: all-pass filter, tunable bandpass filter, and tunable band-reject filter. The developed filter has been integrated with an ultrawideband antenna that results in a reconfigurable filtenna. Such four filtennas are arranged on a single substrate, and sufficient isolation is maintained between them by using reflectors to form a four-port multiple-input multiple-output (MIMO) filtenna that can work for midband 5G cognitive radio (CR) applications. By controlling the operating modes of the filter, the final developed structure gives sensing and communicating antennas for spectrum-interweave and spectrum-underlay CRs. Simulated results have been validated by carrying out the measurement. MIMO performance of the proposed antenna has been evaluated by measuring an envelope correlation coefficient and found suitable for practical applications. [ABSTRACT FROM AUTHOR]

In cognitive radio (CR) networks, the role of spectrum sensing (SS) is crucial to enable an efficient exploitation of the radio spectrum resource. The need to measure spectrum occupancy turns CRs to be de facto measurement instruments, so it becomes crucial to analyze how real instrumentation peculiarities could affect performance in spectral measurements (i.e., SS performance). In this paper, a commercial software-defined radio (SDR) is adopted as cognitive device, and its capability to accurately measure occupied spectrum is analyzed in comparison also with a simulation setup, where the same analog-to-digital converter (ADC) is modeled and simulated, and equivalent signal-to-noise ratios (SNRs) are replicated. To this aim, a suitable experimental setup has been designed, metrologically characterized, and tuned. Experimental results prove how nominal SNR typically adopted for testing the performance of SS methods is not sufficient to model the impairments of a real acquisition chain, even jointly with a modeling of the digitization process. To assure a reliable SS method validation, new approaches have to be considered for enhancing the accordance between the performance predicted in the simulation environment and the experimental one reachable on real devices. Since different architectures of CRs are present on the market, the experimental tests should be preferred for a reliable SS performance assessment. [ABSTRACT FROM AUTHOR]

Amid the dynamic spectrum access in cognitive radio networks, when complex spectrum conditions should be taken into account, how to price the spectrum in order to benefit primary systems in maximization is still under-investigated. In this paper, we devise a spectrum pricing method to address this issue in cognitive networks. In our proposed mechanism, leasing spectrum is collected for uniform selling and classified into three kinds of channels—high-quality channel, mid-quality channel and low-quality channel, respectively. They will be priced variously according to different interference characteristics caused by versatile path fading and user positions. In respond to heterogeneous channel qualities, secondary users also have own selection preferences. They can purchase one kind of channel for usage in based of channel quality and available budget. Then, we obtain the final pricing solution which is an iterative algorithm converging to a fixed point. Also, the existence of a pure Nash equilibrium is discussed to ensure the rationality of the method. In numerical results, we evaluate the effects of this proposal in spectrum pricing and primary systems' profits. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition (AMR) is an essential and challenging topic in the development of the cognitive radio (CR), and it is a cornerstone of CR adaptive modulation and demodulation capabilities to sense and learn environments and make corresponding adjustments. AMR is essentially a classification problem, and deep learning achieves outstanding performances in various classification tasks. So, this paper proposes a deep learning-based method, combined with two convolutional neural networks (CNNs) trained on different datasets, to achieve higher accuracy AMR. A CNN is trained on samples composed of in-phase and quadrature component signals, otherwise known as in-phase and quadrature samples, to distinguish modulation modes, that are relatively easy to identify. We adopt dropout instead of pooling operation to achieve higher recognition accuracy. A CNN based on constellation diagrams is also designed to recognize modulation modes that are difficult to distinguish in the former CNN, such as 16 quadratic-amplitude modulation (QAM) and 64 QAM, demonstrating the ability to classify QAM signals even in scenarios with a low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

In this paper, a half-elliptical disc ultra-wideband (UWB) monopole antenna integrated with a reconfigurable rectangular slot antenna is presented. It consists of a half-elliptical disc radiator to operate in the UWB mode whereas the etched rectangular slot on the half-elliptical patch is designed to operate as a reconfigurable narrowband slot antenna which is fed through a microstrip line from the back side of the patch. Both the antennas are placed in such a way that they are isolated to each other. Frequency reconfigurability has been achieved by inserting three lumped elements (two PIN diodes and a varactor diode) in the slot. An approximate transmission line model of the proposed slot antenna is derived to calculate the proper positioning of the diodes, and the design has been verified through numerical simulations. The simple compact proposed antenna covers a wide frequency range from 0.75 to 12 GHz. Additionally, a wide tuning range from 0.8 to 2.9 GHz (with the frequency ratio of 3.625:1) has been achieved. This compact integrated antenna is a good choice to be implemented in Cognitive Radio (CR) devices. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a hybrid strategy which combines pattern search (PS) optimization and genetic algorithm (GA) to address the problem of power allocation in cognitive radio networks. Considering the fluctuating interference thresholds in cognitive networks, an approach for promoting the coexistence of licensed users and cognitive users is designed. Secondly, based on the analysis of transmission outage probability, a corresponding objective function with regard to the power allocation over Rayleigh fading channels is obtained. It is a difficult task to obtain this objective function directly by using traditional methods, such as common mathematical deduction or linear programming, due to the nonlinearity and complexity of the underlying optimization problem. Inspired by the concept of intelligent algorithms, we employ the scheme of combining PS optimization and GA method, which are both efficient intelligent algorithms to address this challenge. The advantage of this hybrid strategy is that it can overcome the instability problem of GA as well as the local convergency problem of PS method. Thus, the hybrid intelligent method can attain a global and steady outcome. We improve the performance of power allocation strategy with an acceptable increase in computation overhead. The numerical results are encouraging and show that the proposed approach is worthy of consideration in achieving complicated power optimization. Hence, we achieve steady and rational outcomes by applying the proposed hybrid strategy when traditional method is to be ineffective in addressing the nonlinear objective. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of cooperative spectrum sensing in cognitive radio networks using the stochastic geometry tools. In order to cope with the diversity of received signal-to-noise ratios at secondary users, a practical and efficient cooperative spectrum sensing model is proposed and investigated based on the generalized likelihood ratio test detector. In order to investigate the cooperative spectrum sensing system, the theoretical expressions of the probabilities of false alarm and detection of the local decision are derived. The optimal number of cooperating secondary users is then investigated to achieve the minimum total error rate of the final decision by assuming that the secondary users follow a homogeneous Poisson point process. Moreover, the theoretical expressions for the achievable ergodic capacity and throughput of the secondary network are derived. Furthermore, the technique of determining an appropriate number of cooperating secondary users is proposed in order to maximize the achievable ergodic capacity and throughput of the secondary network based on a target total error rate requirement. The analytical and simulation results validate the chosen optimal number of collaborating secondary users in terms of spectrum sensing, achievable ergodic capacity, and throughput of the secondary network. [ABSTRACT FROM PUBLISHER]

5G is anticipated in 2020. In this generation of mobile networks, a great deal of requirements have been set. Although, there are many strong technologies in the telecommunications' sector they do not respond to the 5G goals. On the other hand, telecommunication operators and providers do not want to invest in new equipment/architectures. Cognitive radio (CR) and software defined networking are two technologies with special and vigorous advantages. In this paper, several technical and economic models are developed. The CR is combined with the Stackelberg game. A sensitivity analysis is implemented and the parameters that impact mostly on the model are pinpointed. It is shown that the CR technology could offer all its fundamental cognitive advantages and even financial profits to the telecommunication companies. [ABSTRACT FROM AUTHOR]

Summary: One of the promising technologies for 5G cellular networks is machine‐to‐machine (M2M) communications. We propose a cognitive radio network (CR) that includes a primary cellular system and a secondary cognitive system. The primary cellular system has a primary client (PC) and the secondary cognitive system has M2M clients which are called secondary clients (SCs). In a conventional system of CR network based on orthogonal frequency division multiple access (CR‐OFDMA), when the primary client (PC) is absent, only one SC can obtain the idle spectrum. But, the SC must leave the spectrum when detecting the existence of the PC. So, the spectrum usage of this system is very low. This paper proposes a cooperative CR network based on nonorthogonal multiple access (CCR‐NOMA) for spectrum sensing using energy detection (ED) to allow multiple SCs to share the same frequency at the same time, but are differentiated according to the power domain or code domain, to improve spectrum efficiency of 5G communications and the transmission performance of CR network at the absence and presence of the PC. To evaluate the channel sensing performance of the ED technique in CCR‐NOMA, we derived a closed‐form expression between the achievable throughput and sensing time for the CCR‐NOMA system. The same analysis for the case of CR‐OFDMA is reproduced for the sake of comparison. The analysis showed that the CR‐NOMA system for M2M communication outperforms the CR‐OFDMA system for M2M communication for the same noncooperative and cooperative spectrum sensing and physical layer parameters. [ABSTRACT FROM AUTHOR]

Ultra high definition radio, low latency, high bandwidth, machine to machine communication and several emergence application like e-health, IOT etc. will be dominated by 5G in future networks. Therefore there is dire need of more capacity which in turn will demand more efficient spectrum sensing. In this paper we propose a new technique of FBMC spectrum sensing cognitive radio technique in future networks. The proposed technique provides programmability that allows dynamic and real time configuration of the operating channel. The simulation results have been verified in terms of various parameters and found satisfactory results. [ABSTRACT FROM AUTHOR]

Network planning and management are challenging issues in a two-tier network. Tailoring to cognitive radio networks (CRNs), network operations and transmissions become more challenging due to the dynamic spectrum availability. This paper proposes an adaptive network management system that provides switching between different CRN management structures in response to the spectrum availability and changes in the service time required for the radio access. The considered network management system includes conventional macrocell-only structure, and centralized/distributed structures overlaid with femtocells. Furthermore, analytical expressions of per-tier successful connection probability and throughput are provided to characterize the network performance for different network managements. Spectrum access in dynamic radio environments is formulated according to the quality of service (QoS) constraint that is related to the connection probability and outage probability. Results show that the proposed intelligent network management system improves the maximum capacity and reduces the number of blocked connections by adapting between various network managements in response to free spectrum transmission slots. A road map for the deployment and management of cognitive macro/femto networks is also presented. [ABSTRACT FROM PUBLISHER]

The cooperative communication system has gained lots of interest due to effective utilization of the allotted spectrum. Spectrum sensing techniques are a key factor for the cooperative communication system. A number of sensors are deployed to sense the spectrum hole. The group or clusters of the sensor are used to make out the decision about the spectrum hole. The presence of malicious sensor affects the performance of spectrum sensing. In this paper, the trust on the group or cluster has been identified based on the fuzzy classification. The fuzzy classifier takes the input in terms of reporting cluster trust value, neighborhood trust value and primary weight index. The membership values of these three linguistic variables are used by fuzzy interference system to decide the trust value of the group or cluster. The number of malicious user has been introduced during the simulation and performance has been identified. The Eigenvalue-based spectrum sensing is also simulated in this paper and the effect of different SNRs on the decision accuracy is shown. [ABSTRACT FROM AUTHOR]

This paper addresses a residual energy maximization problem on a two-hop decode-and-forward relay-assisted cognitive radio network operating in a time-slotted mode in the presence of an eavesdropper. In the first time slot, spectrum sensing (SS) and energy harvesting (EH) from the received signal of the primary user (PU) are done simultaneously by the secondary transmitter through power splitting mode. On the next time slot, the secondary transmitter either harvests energy or transmits its own data to the relay node based on the SS decision result. The relay node subsequently either forwards this data or harvests energy based on the activity followed by the secondary transmitter node. Considering both linear and EH models, an optimization nonlinear problem of residual energy maximization is explored under the constraints of sensing reliability of the PU, secondary outage probability, and secrecy outage probability. Closed-form expressions of the sensing time and secondary and relay transmit power are derived both in the presence and absence of an eavesdropper. The impact of eavesdropping on detection probability, false alarm probability, secondary outage probability, and residual energy are analyzed. Simulation results show that about $\sim$ 21.19% and $\sim$ 42.84% gain in total residual energy values are achieved compared to the existing works. [ABSTRACT FROM AUTHOR]

This paper studies robust resource-allocation algorithm design for a multiuser multiple-input–single-output (MISO) cognitive radio (CR) downlink communication network. We focus on a secondary system that provides wireless unicast secure layered video information to multiple single-antenna secondary receivers. The resource-allocation algorithm design is formulated as a nonconvex optimization problem for the minimization of the total transmit power at the secondary transmitter. The proposed framework takes into account a quality-of-service (QoS) requirement regarding video communication secrecy in the secondary system, the imperfection of the channel state information (CSI) of potential eavesdroppers (primary receivers) at the secondary transmitter, and a limit for the maximum tolerable received interference power at the primary receivers. Thereby, the proposed problem formulation exploits the self-protecting architecture of layered transmission and artificial noise generation to ensure communication secrecy. The considered nonconvex optimization problem is recast as a convex optimization problem via semi-definite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. In addition, two suboptimal resource-allocation schemes are proposed for the case when the solution of the dual problem is unavailable for constructing the optimal solution. Simulation results demonstrate significant transmit power savings and robustness against CSI imperfection for the proposed optimal and suboptimal resource-allocation algorithms employing layered transmission compared to baseline schemes employing traditional single-layer transmission. [ABSTRACT FROM PUBLISHER]

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]

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users, as a large number of applications demand for more comprehensive knowledge on primary network behaviors in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship among primary nodes by introducing a Bayesian network (BN)-based framework. How to efficiently learn such a BN structure is a long-standing issue that is not fully understood even in the statistical learning community. To address such an issue in CR, this paper proposes a BN structure learning scheme consisting of a concise directional dependence checking function and a regular BN graph, which achieves significantly lower computational complexity compared with existing approaches. With this result, cognitive users could efficiently understand the statistical behavior patterns in the primary networks, such that more efficient cognitive protocols could be designed across different network layers. [ABSTRACT FROM PUBLISHER]

In this paper, a new cooperation structure for spectrum sensing in cognitive radio networks is proposed, which outperforms the existing commonly used ones in terms of energy efficiency. The efficiency is achieved in the proposed design by introducing random interruptions in the cooperation process between the sensing nodes and the fusion center, along with a compensation process at the fusion center. Regarding the hypothesis testing problem concerned, first, the proposed system behavior is thoroughly analyzed and its associated likelihood-ratio test is provided. Next, based on a general linear fusion rule, the statistics of the global test summary are derived and the sensing quality is characterized in terms of the probability of false alarm and probability of detection. Then, the optimization of the overall detection performance is formulated according to the Neyman–Pearson criterion (NPC) and it is discussed that the optimization required is indeed a decision-making process with uncertainty which incurs prohibitive computational complexity. The NPC is then modified to achieve a good affordable solution by using semidefinite programming (SDP) techniques and it is shown that this new solution is nearly optimal according to the deflection criterion. Finally, the effectiveness of the proposed architecture and its associated SDP is demonstrated by simulation results. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) is an emerging technique to improve the efficiency of spectrum resource utilization. In CR networks, the selfish behavior of secondary users (SUs) can considerably affect the performance of primary users (PUs). Accordingly, game theory, which considers the game players’ selfish behavior, has been applied to the design of CR networks. Most of the existing studies focus on the network design only from the network perspective to improve system performance, such as utility and throughput. However, the users’ experience to the service, which cannot simply be reflected by quality of service, has been largely ignored. The user-perceived multimedia quality and service can be different from the actual received multimedia quality, and thus is very important to consider the network design. To better serve the network users, quality of experience (QoE) is adopted to measure the network service from the users’ perspective and help improve the users’ satisfaction to the CR network service. As CR networks require a lot of data storage and computation for spectrum sensing, spectrum sharing, and algorithm design, cloud computation comes as a convenient solution, because it can provide massive storage and fast computation. In this paper, we propose to design a user-oriented CR cloud network for multimedia applications, where the user’s satisfaction is reflected in the CR cloud network design. In the proposed framework, the PU and SU game is formulated as Stackelberg game. In particular, a refunding term is defined in the users’ utility function to effectively consider and to reflect the network users’ QoE requirement. Our contributions are twofold: 1) a game-based CR cloud network design for multimedia transmission is proposed, and the network user’s QoE requirement is satisfied in the design and 2) the existence and the uniqueness of the Stackelberg Nash equilibrium are proved, and the design is optimal. Our simulation results demonstrate the effectiveness of the game user-oriented CR cloud network design. [ABSTRACT FROM PUBLISHER]

One of the most important factors that affects the performance of energy detection (ED) is the fading channel between the wireless nodes. This paper investigates the performance of ED-based spectrum sensing, for cognitive radio (CR), over two-wave with diffuse power (TWDP) fading channels. The TWDP fading model characterizes a variety of fading channels, including well-known canonical fading distributions, such as Rayleigh and Rician, as well as worse-than-Rayleigh fading conditions modeled by the two-ray fading model. Novel analytic expressions for the average probability of detection over TWDP fading that account for single-user and cooperative spectrum sensing and square law selection diversity reception are derived. These expressions are used to analyze the behavior of ED-based spectrum sensing over moderate, severe, and extreme fading conditions and to investigate the use of cooperation and diversity as a means of mitigating the fading effects. The obtained results indicate that TWDP fading conditions can significantly degrade sensing performance; however, it is shown that detection performance can be improved when cooperation and diversity are employed. The presented outcomes enable identifying the limits of ED-based spectrum sensing and quantifying the tradeoffs between detection performance and energy efficiency for CR systems deployed within confined environments, such as in-vehicular wireless networks. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a minimized error vector magnitude (M-EVM) scheme to simultaneously reduce the cubic metric (CM) and spectral sidelobe in noncontiguous orthogonal frequency-division multiplexing (NC-OFDM)-based cognitive radio (CR) systems by modifying the constellations on the secondary user (SU) subcarriers and adding canceling symbols on the primary user (PU) subcarriers. The proposed M-EVM scheme formulates an optimization problem, which minimizes the EVM with constraints of the CM and spectral sidelobe. Furthermore, we propose an iterative approximation algorithm to solve the optimization problem. Simulation results show that the M-EVM scheme offers significant reductions of the CM and spectral sidelobe. [ABSTRACT FROM PUBLISHER]

In traditional cognitive radio networks (CRNs), secondary users (SUs) typically access the spectrum of primary users (PUs) by a two-stage “listen-before-talk” (LBT) protocol, i.e., SUs sense the spectrum holes in the first stage before transmitting in the second. However, there exist two major problems: transmission time reduction due to sensing and sensing accuracy impairment due to data transmission. In this paper, we propose a “listen-and-talk” (LAT) protocol with the help of full-duplex (FD) technique that allows SUs to simultaneously sense and access the vacant spectrum. Spectrum utilization performance is carefully analyzed, with the closed-form spectrum waste ratio and collision ratio with the PU provided. In addition, with regard to the secondary throughput, we report the existence of a tradeoff between the secondary transmit power and throughput. Based on the power–throughput tradeoff, we derive the analytical local optimal transmit power for SUs to achieve both high throughput and satisfying sensing accuracy. Numerical results are given to verify the proposed protocol and the theoretical results. [ABSTRACT FROM PUBLISHER]

The dynamic spectrum access (DSA) principle, relying on the cognitive radio (CR) paradigm, allows users to access spectrum over time intervals or spatial areas where it remains unused. Due to the opportunistic nature of DSA/CR, the behavior and performance of DSA/CR networks depends on the perceived spectrum usage pattern. An accurate modeling of spectrum occupancy therefore becomes essential in the context of DSA/CR. In this context, this paper addresses the problem of accurately modeling the spectrum occupancy pattern perceived by DSA/CR users in the spatial domain. A novel spatial modeling approach is introduced to enable a simple yet practical and accurate characterization of spectrum. First, a set of models is proposed to characterize and predict the average level of occupancy perceived by DSA/CR users at various locations based on the knowledge of some simple signal parameters. An extension is then proposed to characterize not only the average occupancy level but the instantaneous channel state perceived simultaneously by DSA/CR users observing the same transmitter from different locations as well. The validity and accuracy of the theoretical models are demonstrated with results from an extensive spectrum measurement campaign. Some illustrative examples of their potential applicability are presented and discussed as well. [ABSTRACT FROM PUBLISHER]