Search

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]

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]

In this paper, we investigate the effect of imperfect sensing on the performance of opportunistic spectrum access (OSA) in cognitive radio (CR) networks. We consider a system modeled as a continuous-time Markov chain (CTMC), and then evaluate its performance in terms of the probabilities of users being blocked or dropped. Our results demonstrate that the performance of the underlying system significantly degrades when imperfect sensing is considered; thus, there is a pressing need for a reliable spectrum sensing scheme to improve the overall quality of service in practical scenarios. A simulation study is presented to corroborate the analytical results and to demonstrate the performance of OSA under imperfect sensing conditions. [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 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]

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]

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]

This paper presents a fully integrated ultra wideband (UWB)-based cognitive radio (CR) transceiver for 1\,\textGb/s data-rate energy-efficient short-range wireless connectivity by scavenging triple discrete inactive frequency bands in 3.1–10.6\,\textGHz ISM band. The transceiver including receivers (RXs), transmitters (TXs), spectrum sensors, and synthesizers is implemented in 1\,\textV 65\,\textnm standard CMOS technology. A novel pulse generator (PG) enables the TX to meet UWB emission mask and to reduce spectral sidelobe peak by < -40\,\textdBc with low power consumption of 3.6\,\textmW. A dual-mode RX front-end provides quadrature analog correlation (QAC) for analog domain matched filtering during communication and analog wavelet-based energy detection (ED) during spectrum sensing (SS) without changing circuitry. The transceiver achieves the minimum energy consumption of 59.7\,\textpJ/b with 1.97 \times 10^-4\,\textbit error rate (BER) and the maximum energy consumption of 102.3\,\textpJ/b with 1.25 \times 10^-3\,\textBER. Die area is $4.6\,{{\text{mm}}^{2}}$ with on-die phase-locked loops (PLLs) and pads. [ABSTRACT FROM PUBLISHER]

In this paper, we study cognitive radio (CR) networks that consist of multiple secondary TV band device (TVBD) station–user pairs and one primary digital TV (DTV) station–user pair, where each terminal has a single antenna. Specifically, we consider spectrum sharing (SS)-based secondary TVBD networks to effectively utilize limited spectrum resources in the presence of cochannel primary DTV interference. We first present an opportunistic single secondary network selection scheme to maximize the mutual information over all secondary TVBD networks under the constraint of the availability of the primary DTV service. Then, the performance bound of CR networks with the secondary network selection is obtained through the derivation of an exact and closed-form outage probability. It is shown that the derived outage probability is given as a function of transmission power, channel coefficients, and target rates of both the primary DTV network and the secondary TVBD networks. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

In this paper, the ergodic sum rate of a fading cognitive multiple-access channel (C-MAC) is studied, where a secondary network (SN) with multiple secondary users (SUs) transmitting to a secondary base station shares the spectrum band with a primary user (PU). An interference power constraint (IPC) is imposed on the SN to protect the PU. Under such a constraint and the individual transmit power constraint (TPC) imposed on each SU, we investigate the power allocation strategies to maximize the ergodic sum rate of a fading C-MAC without successive interference cancelation (SIC). In particular, this paper considers two types of constraints: 1) average TPC and average IPC and 2) peak TPC and peak IPC. For the first case, it is proved that the optimal power allocation is dynamic time-division multiple access (D-TDMA), which is exactly the same as the optimal power allocation to maximize the ergodic sum rate of the fading C-MAC with SIC under the same constraints. For the second case, it is proved that the optimal solution must be at the extreme points of the feasible region. It is shown that D-TDMA is optimal with high probability when the number of SUs is large. Moreover, we show that when the SUs can be sorted in a certain order, an algorithm with linear complexity can be used to find the optimal power allocation. [ABSTRACT FROM PUBLISHER]

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 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]

Broadcasting is an important operation in wireless networks where control information is usually propagated as broadcasts for the realization of most networking protocols. In traditional ad hoc networks, broadcasts are conducted on a common channel, which is shared by all nodes in the network. However, in cognitive radio (CR) ad hoc networks, unlicensed users may observe heterogeneous spectrum availability, which is unknown to other unlicensed users before the control information was broadcast. Thus, it is extremely challenging that broadcasts can be successfully conducted without knowing the spectrum availability information in advance. In addition, since broadcast collisions (i.e., simultaneous reception of broadcast messages at the same node) often lead to the waste of network resources, they should be efficiently mitigated in multihop scenarios. In this paper, a quality-of-service (QoS)-based broadcast protocol under Blind Information for multihop CR ad hoc networks, i.e., \QB^2\IC, is proposed with the aim of having a high success rate and short broadcast delay. In our design, we do not assume that unlicensed users are aware of the network topology, the spectrum availability information, and time synchronization information. To the best of our knowledge, this is the first paper that investigates the broadcast issue in multihop CR ad hoc networks under blind information. Simulation results show that our proposed \QB^2\IC protocol outperforms other broadcast schemes in terms of a higher success rate and shorter average broadcast delay. [ABSTRACT FROM PUBLISHER]

In this paper, we study the tradeoff between sensing time and achievable throughput of the secondary user that employs robust eigenvalue-based spectrum sensing techniques in the presence of noise uncertainty. First, we study exact distributions of the test statistics for two types of robust eigenvalue-based sensing techniques, namely, the blind generalized likelihood ratio test (B-GLRT) detection and energy with minimum eigenvalue (EME) detection. The developed threshold setting is more accurate than benchmark methods in achieving a target constant false alarm rate (CFAR). Second, prior to the throughput analysis, the necessary asymptotic detection and false alarm probabilities under noise uncertainty are formulated for eigenvalue-based detectors such as maximum eigenvalue detection (MED) and maximum–minimum eigenvalue (MME) detection. Finally, the throughput is maximized using eigenvalue-based spectrum sensing techniques which are B-GLRT, EME, MME, and MED detectors. The results are compared with the commonly used energy detector (ED). An improved achievable throughput is obtained under low-signal-to-noise-ratio (SNR) regime by incorporating the robust eigenvalue-based techniques, which are insusceptible to noise uncertainty. [ABSTRACT FROM PUBLISHER]

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]

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]

This paper considers a new power strategy and channel-allocation optimization for secondary users (SUs) in an orthogonal frequency-division multiplexing (OFDM)-based cognitive radio (CR) network where the coverage area of the secondary network is divided into an overlay region and a hybrid region. SUs in the overlay region can adopt the overlay spectrum access method, whereas SUs in the hybrid region may adopt the underlay or sensing-free spectrum access method. We first present a general resource-allocation framework that optimizes the power and channel allocation to SUs who adopt these different spectrum access methods, depending on their locations. To enable sensing-free spectrum access, we then propose a new algorithm that incorporates an interference violation test to decide the parameters in the general framework. The proposed scheme intelligently utilizes frequency and space opportunities, avoids unnecessary spectrum sensing, and minimizes overall power consumption while maintaining the quality of service (QoS) of a primary system. Simulation results validate the effectiveness of the proposed method in terms of energy efficiency and show that enhanced performance can be obtained by utilizing spatial opportunities. [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]

This paper addresses the problem of accurately modeling the spectrum occupancy patterns of real radio communication systems, which is an essential aspect in the study of cognitive radio (CR) networks. The main drawbacks and limitations of previous works are identified, and the methodological procedures on which they rely are improved and extended. Two sophisticated measurement platforms, providing low and high time resolutions, are used to obtain extensive real-world data from a multiband spectrum measurement campaign, embracing a wide variety of spectrum bands of practical interest for CR applications. A comprehensive, systematical, and rigorous analysis of the statistical properties observed in the measurement data is then performed to find accurate models capable of capturing and reproducing, within reasonable complexity limits, the statistical properties of temporal patterns, at both short and long timescales, in real wireless systems. Innovative modeling approaches capable of simultaneously describing statistical properties at both timescales are developed as well. In summary, this paper contributes realistic and accurate time-dimension spectrum usage models for their application to the study and development of CR. [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]

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]

Sensing spectra in a reliable and efficient manner is fundamental to cognitive radio (CR). Ensuring cooperation among spectrum sensing devices is an appropriate method when a CR system is under deep shadowing and in a fading environment. In this paper, an enhanced scheme for cooperative spectrum sensing (CSS) based on efficient quantization and the Dempster–Shafer (D–S) theory of evidence is proposed. The proposed scheme includes an effective quantizer for the sensing data by utilizing special properties of the hypothesis distribution under different signal-to-noise ratios (SNRs) of the primary signal. As a result, the required bandwidth for the reporting channel is reduced while the advantage for combinations of the D–S theory is maintained. Simulation results revealed that significant improvements in the CSS gain, as well as a reduction in the system overhead, were achieved. [ABSTRACT FROM PUBLISHER]

In this paper, a new spectrum-sharing model called sensing-based spectrum sharing is proposed for cognitive radio networks. This model consists of two phases: In the first phase, the secondary user (SU) listens to the spectrum allocated to the primary user (PU) to detect the state of the PU; in the second phase, the SU adapts its transit power based on the sensing results. If the PU is inactive, the SU allocates the transmit power based on its own benefit. However, if the PU is active, the interference power constraint is imposed to protect the PU. Under this new model, the evaluation of the ergodic capacity of the SU is formulated as an optimization problem over the transmit power and the sensing time. Due to the complexity of this problem, two simplified versions, which are referred to as the perfect sensing case and the imperfect sensing case, are studied in this paper. For the perfect sensing case, the Lagrange dual decomposition is applied to derive the optimal power allocation policy to achieve the ergodic capacity. For the imperfect sensing case, an iterative algorithm is developed to obtain the optimal sensing time and the corresponding power allocation strategy. Finally, numerical results are presented to validate the proposed studies. It is shown that the SU can achieve a significant capacity gain under the proposed model, compared with that under the opportunistic spectrum access or the conventional spectrum sharing model. [ABSTRACT FROM AUTHOR]

In cognitive radio (CR) networks, there are scenarios where the secondary (lower priority) users intend to communicate with each other by opportunistically utilizing the transmit spectrum originally allocated to the existing primary (higher priority) users. For such a scenario, a secondary user usually has to tradeoff between two conflicting goals at the same time: one is to maximize its own transmit throughput; and the other is to minimize the amount of interference it produces at each primary receiver. In this paper, we study this fundamental tradeoff from an information-theoretic perspective by characterizing the secondary user's channel capacity under both its own transmit-power constraint as well as a set of interference-power constraints each imposed at one of the primary receivers. In particular, this paper exploits multi-antennas at the secondary transmitter to effectively balance between spatial multiplexing for the secondary transmission and interference avoidance at the primary receivers. Convex optimization techniques are used to design algorithms for the optimal secondary transmit spatial spectrum that achieves the capacity of the secondary transmission. Suboptimal solutions for ease of implementation are also presented and their performances are compared with the optimal solution. Furthermore, algorithms developed for the single-channel transmission are also extended to the case of multichannel transmission whereby the secondary user is able to achieve opportunistic spectrum sharing via transmit adaptations not only in space, but in time and frequency domains as well. Simulation results show that even under stringent interference-power constraints, substantial capacity gains are achievable for the secondary transmission by employing multi-antennas at the secondary transmitter. This is true even when the number of primary receivers exceeds that of secondary transmit antennas in a CR network, where an interesting "interference diversity" effect can be e- xploited. [ABSTRACT FROM PUBLISHER]

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]

This paper investigates the performance of a cognitive hybrid satellite–terrestrial network, where the primary satellite communication network and the secondary terrestrial mobile network coexist, provided that the interference temperature constraint is satisfied. By using the Meijer-G functions, the exact closed-form expression of the outage probability (OP) for the secondary network is first derived. Then, the asymptotic result in a high-signal-to-noise-ratio (SNR) regime is presented to reveal the diversity order and coding gain of the considered system. Finally, computer simulations are carried out to confirm the theoretical results and reveal that a more loose interference constraint or heavier shadowing severity of a satellite interference link leads to a reduced OP, whereas stronger satellite interference power poses a detrimental effect on the outage performance. [ABSTRACT FROM AUTHOR]

Very recently, regulatory bodies worldwide have approved dynamic access of unlicensed networks to the TV white space (TVWS) spectrum. Hence, in the near future, multiple heterogeneous and independently operated unlicensed networks will coexist within the same geographical area over shared TVWS. Although heterogeneity and coexistence are not unique to TVWS scenarios, their distinctive characteristics pose new and challenging issues. In this paper, the problem of the coexistence interference among multiple heterogeneous and independently operated secondary networks (SNs) in the absence of secondary cooperation is addressed. Specifically, the optimal coexistence strategy, which adaptively and autonomously selects the channel maximizing the expected throughput in the presence of coexistence interference, is designed. More in detail, at first, an analytical framework is developed to model the channel selection process for an arbitrary SN as a decision process. Then, the problem of the optimal channel selection, i.e., the channel maximizing the expected throughput, is proved to be computationally prohibitive (NP-hard). Finally, under the reasonable assumption of identically distributed interference on the available channels, the optimal channel selection problem is proved not to be NP-hard, and a computationally efficient (polynomial-time) algorithm for finding the optimal strategy is designed. Numerical simulations validate the theoretical analysis. [ABSTRACT FROM PUBLISHER]

A cognitive radio (CR) system with retrial possibility and an admission cost for secondary users (SUs) to join the retrial group is investigated in this paper. If the SU finds the primary user (PU) band unavailable, it must decide with a probability estimate to either enter a retrial group or give up its service and leave the system. SUs in the retrial group independently retry after an exponentially distributed random time until they successfully access the spectrum. When the PU arrives, the SU's service on the band is interrupted. This interrupted SU is then assumed to occupy the PU band immediately when the PU completes its service. First, the noncooperative joining behavior of SUs that choose to maximize their benefit in a selfish distributed manner is investigated, and an inefficient Nash equilibrium is derived. Second, from the perspective of the social planner, the socially optimal joining strategy when SUs cooperate with each other is studied, and the corresponding Nash equilibrium is exactly derived. Finally, the result that an individually optimal strategy, in general, does not yield the socially optimal strategy is theoretically verified. Furthermore, to bridge the gap between the individually and socially optimal strategies, a novel strategy of imposing an admission fee on SUs to join the retrial group is proposed and investigated with the derivation of an optimal value for the admission fee. The numerical analysis indicates that the proposed admission fee as an equilibrium strategy and the socially optimal strategy of SUs improve efficiency in the utilization of the CR system. [ABSTRACT FROM AUTHOR]

In this correspondence, we propose a cooperative-generalized-sensing-based spectrum sharing approach in centralized cognitive radio networks. Differently from the conventional cooperative approaches, where the secondary base station (SBS) takes a decision on the busy/idle status of the primary user and transmits with constant/binary power, the proposed approach allows the SBS to transmit with power as a continuous function of the detection information vector. We treat the cases of lossless-compression and hard-decision information separately. Optimal sensing time, detection threshold, and power allocation are derived to maximize the achievable rate at the SBS. In this paper, we only consider the case where secondary users (SUs) are merely receivers, and the SBS uses the allocated power to communicate with the SUs; however, the conclusions can also be expanded to the case where SUs use the allocated power under the assumption of a common control channel to exchange the information between the SBS and SUs. Simulation results demonstrate that the proposed approach can improve the performance of the SBS compared with the conventional approaches. [ABSTRACT FROM AUTHOR]

In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite 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. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well. [ABSTRACT FROM AUTHOR]

Cognitive radio allows secondary users (SUs) to exploit the underutilized radio spectrum of the primary networks. To fully utilize the primary spectrum and maximize spectral efficiency, overlay and underlay transmissions, which exploit the white and gray spaces, respectively, should be used together. However, for underlay, the SUs need to transmit at low power to avoid causing harmful interference to the primary users (PUs), whereas the PUs will cause high interference to the SUs. Thus, interference mitigation is a major issue in underlay spectrum utilization. In this paper, a hybrid transmission system that exploits both overlay and underlay is proposed using multicarrier code-division multiple access due to its interference rejection and diversity exploitation capabilities. Unlike existing approaches, which separate the use of overlay and underlay spectrum, the proposed schemes utilize the entire spectrum for underlay transmission to minimize the PU interference while using the spectrum holes for overlay transmission to maximize the data rate. Two techniques that operate at full-load and overload scenarios are proposed. The overload system is then extended to the multiuser underlay case to further improve spectrum utilization. [ABSTRACT FROM AUTHOR]

In wireless communication networks, it is desirable to achieve energy efficiency, while maintaining quality of service. Overall, energy efficiency can be achieved by minimizing the power output for each communication device. This paper presents a novel energy-saving adaptive transmit power control (TPC) algorithm based on bit error rate (BER) feedback (BER-TPC) that outperforms conventional approaches based on signal-to-interference-plus-noise ratio (SINR) feedback (SINR-TPC). It is a distributed algorithm deployed in both transmitting and receiving devices that can be applied for various wireless network topologies and protocols. This paper addresses the power efficiency of adaptive TPC, in terms of reduced total transmit power of the network by smoothing transmit power transients during adaptive iterations. It is achieved using a distributed closed-loop power control system that applies heuristically estimated dual-rate power adjustments during adaptive iterations. Because the proposed system uses a mathematical formula for power adjustment, operational bounds for stability can be provided using simple analysis. It is demonstrated that the proposed system provides adequate tradeoff between performance and complexity, in terms of reduction in sensitivities and better tracking performance in following the desired reference power curves used in the simulations. Case scenarios are simulated demonstrating approximately 1.39-dB transmit power savings compared to conventional methods. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the detection of long-term evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) signals, with application to cognitive radio systems. We explore the second-order cyclostationarity of the LTE SC-FDMA signals and apply results obtained for the cyclic autocorrelation function to signal detection. The proposed detection algorithm provides a very good performance under various channel conditions, with a short observation time and at low signal-to-noise ratios, with reduced complexity. The validity of the proposed algorithm is verified using signals generated and acquired by laboratory instrumentation, and the experimental results show a good match with computer simulation results. [ABSTRACT FROM PUBLISHER]

As vehicular communication becomes a widespread phenomenon, there will be an increase in spectrum scarcity. Cognitive radio provides an effective solution but requires a robust sensing mechanism that entails a large overhead; this additional sensing data could be detrimental to a system already lacking in bandwidth. This paper proposes novel ways of limiting sensing overhead by improving upon current methods which use cooperative mechanisms and adjustable double thresholds (DTHs). Based on a sliding variable, the proposed thresholds can react to changes in the environment, providing the required primary user detection and false alarm probabilities while limiting the number of vehicles reporting sensing data. Three new DTHs have been proposed: detection-based DTH, decision-based DTH, and independent-threshold DTH. Each has unique properties that make it suited for different environments. Simulations were run on all proposed thresholds to test their validity and endurance under environmental changes. The results indicate that the DTHs would greatly benefit high-contention, dense vehicular networks. [ABSTRACT FROM AUTHOR]

A novel compact single-substrate planar multiband five-element multiple-input multiple-output (MIMO) antenna system is presented in this paper. The tunable two-element folded meandered MIMO antenna covers the long-term evolution frequency bands below 1 GHz (687–813 MHz) and radio frequency identification bands centered around 2.4 and 5.8 GHz. The other two-element compact MIMO antennas operate over 754–971 MHz, 1.65–1.83 GHz, 2–3.66 GHz, and 5.1–5.6 GHz frequency bands. Furthermore, the proposed antenna elements are integrated with a wideband sensing antenna for the spectrum sensing in 0.668–1.94 and 3–4.6 GHz, which also acts as the ground plane for the MIMO elements in the cognitive radio application environment. The antenna is fabricated on a 65 mm $\times \,\, 120$ mm $\times \,\, 1.56$ mm low-cost FR-4 substrate. The antenna’s radiation characteristics are experimentally verified, and the results are in agreement with the full-wave simulation. The 3-D radiation pattern-based envelope correlation coefficient of the MIMO antennas is also experimentally verified which is below the desired value of 0.5. Finally, to show its utility at the Internet-of-Things platform, the antenna is tested in the realistic application environment. [ABSTRACT FROM AUTHOR]

Limited linearity and local oscillator (LO) harmonics mixing are two major issues that ultra-wideband receivers, and a cognitive radio (CR) in particular, are dealing with. Direct-conversion schemes, based on current-driven passive mixers, have shown to improve the linearity but unable to resolve the LO harmonic mixing problem. They are usually limited to third and fifth harmonics rejection or require a very complex and power hungry circuitry for a higher number of harmonics. This paper presents a heterodyne conversion scheme in the 180-nm CMOS technology for CR applications (54–862 MHz band) that robustly mitigates the harmonic mixing issue for all the harmonics, while by employing an active feedback loop, a comparable state-of-the-art $\mathrm {IIP_{3}}$ of better than +10 dBm is achieved. Measurements show an average noise figure of 7.5 dB when the active feedback loop is OFF (i.e., in the absence of destructive interference) and of 15.5 dB when the feedback loop is active and a 0-dBm interferer is applied, respectively. [ABSTRACT FROM AUTHOR]

For wireless networks in the Internet of Things (IoT), cognitive radio (CR) is a promising way to obtain the available spectrum for objects. Wide-band spectrum sensing plays an important role in building such CR networks of IoT. In this paper, we propose a novel dynamic compressive wide-band spectrum sensing method based on channel energy reconstruction. After a bank of wide-band random filters is employed to measure the channel energy, rather than to recover all the channel energy in the whole spectrum, only the channel energy with a changing occupancy status in consecutive time slots is recovered. Furthermore, it is unnecessary to use reconstruction algorithm unless there are two or more channels changing their occupancy status. Compared to the existing methods, our proposed schemes bear significant improvements in the probability of detection and reduction of probability of false alarms. Simulation results also show its fast speed and robustness to noise. [ABSTRACT FROM AUTHOR]

Real-time spectrum sensing refers to searching for possible signals at a specific time and location, which is applicable to cognitive radio for primary signal detection. The simplicity and low sensing time of phaser-based spectrum sensors, implemented in a discrete manner previously, provided the incentive for this paper. In this paper, an integrated CMOS wideband real-time spectrum sensor with a novel on-chip phaser in 57–354-MHz band, as part of VHF/UHF TV broadcast bands, is presented. The proposed approach provides a fast, simple, area-efficient analog solution for real-time spectrum sensing with low noise figure and power consumption. The integrated chip has been fabricated in a standard 0.18- \mu \textm CMOS IBM technology and has achieved a sensing time of as low as 2.5 \mu \texts for 27-MHz frequency resolution. [ABSTRACT FROM PUBLISHER]

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.