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This article investigates legitimate eavesdropping and secure communication in a multicarrier interference network, where a suspicious link coexists with an unsuspicious link, in the presence of a full-duplex monitor. It is assumed that the unsuspicious users (UUs) are under illegal eavesdropping threat from the suspicious users (SUs), and the monitor can send jamming signals to interfere with the SUs. The UUs are required to cooperate with the monitor for reducing the secrecy outage probability of the UUs to be lower than that without the monitor’s jamming. Under this secrecy outage constraint along with the transmit power constraints, the problem for maximizing the successful eavesdropping probability of the monitor by jointly allocating the transmit powers of the monitor and the UUs is investigated. The problem is solved with the Lagrange duality method by proving the time-sharing property holds, where two algorithms are proposed for obtaining the dual function based on the block coordinate descent method and the successive convex approximation method. Simulation results verify that the proposed algorithms are superior to the noncooperative strategy and the passive eavesdropping strategy, and achieve high successful eavesdropping probability and low secrecy outage probability simultaneously.

Recently, wireless powered cooperative cognitive radio networks (CRNs), which combine the technologies of radio frequency (RF) energy harvesting and CR, have drawn great attention. In such networks, energy cooperation between the cognitive users (CUs) and the wireless powered primary users (PUs) can be performed, where the CUs can charge the PUs wirelessly in exchange for the spectrum access. Specifically, energy cooperation and information transmission is executed in two phases, where the CUs transmit their data signals and the PUs harvest energy from these signals in the first phase, and the PUs transmit their data using the harvested energy in the second phase. In particular, we consider two multiple access schemes for the CUs, namely non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA). For both NOMA and TDMA, the PU sum-rate maximization problems under the minimum CU sum-rate constraint are first simplified by exploring particular problem structure, then are transformed to convex problems, and finally are solved optimally. The PU sum-rates of the two schemes are compared theoretically as well as numerically. It is revealed that the circuit power consumption at the CUs, the required minimum CU sum-rate, and the PU energy harvesting sensitivity and saturation thresholds play key roles in the PU performance comparison of the two schemes.

In this letter, we investigate the secrecy outage performance of a cooperative non-orthogonal multiple access (NOMA) network, where a base station (BS) communicates with multiple cell-centre users and a cell-edge user in the presence of an eavesdropper. It is assumed that there is no direct link between the BS and the cell-edge user, and thus one of the cell-centre users is selected to act as a relay for the cell-edge user. Particularly, the selected cell-centre user adopts the simultaneous wireless information and power transfer (SWIPT) for decoding information and harvesting energy for forwarding. The closed-form approximate expression for the system secrecy outage probability is derived under the max-min user selection (MMUS) strategy. The theoretical results are verified by simulations, and it is shown that the MMUS strategy outperforms various benchmark strategies.

In this letter, proactive eavesdropping of an orthogonal frequency division multiplexing (OFDM)-based bidirectional point-to-point suspicious communication link is studied. The problem of joint successive interference cancelation (SIC) decoding ordering and jamming power allocation at the monitor for maximizing the weighted sum eavesdropping rate is formulated as mixed-integer nonlinear programming and then transformed to more tractable zero-one integer programming. A heuristic suboptimal algorithm and an optimal algorithm based on the branch-and-bound method are proposed. Simulation results show the advantageous of the proposed algorithms compared to the benchmarks, and also show that both algorithms are low-complex, and the suboptimal algorithm has further lower complexity and achieves almost the same performance as the optimal algorithm.

In this paper, spectrum sharing incentive for legitimate wireless information surveillance at the physical layer is investigated. The concept behind this is that authorized government agencies want to eavesdrop the communication between a suspicious source-destination pair, and stimulate the help of another source-destination pair (referred to as secondary users) looking for transmission opportunities. We thus propose to let the secondary receiver act as a monitor to eavesdrop the suspicious communication and let the secondary transmitter act as a friendly jammer to improve the eavesdropping performance, such that they are granted to share the spectrum of the suspicious users, as a reward. To guarantee the eavesdropping performance, the successful eavesdropping probability is required to be larger than that with passive eavesdropping. The problems of jointly optimizing the transmit power of the secondary transmitter and the successive interference cancellation (SIC) decoding order at the secondary receiver to maximize the average secondary transmission rate and minimize the secondary transmission outage probability are investigated. Two scenarios are considered for the availability of the channel state information (CSI). The first scenario assumes perfect CSI, and the optimal strategies for the transmit power control and the SIC decoding ordering are derived. The second scenario assumes partial CSI, where the instantaneous CSI of the secondary transmission link and the statistical CSI of the links related to the suspicious communication are available, and the optimal strategies for maximizing an approximate average secondary transmission rate and minimizing an approximate secondary transmission outage probability are proposed. Extensive simulation results validate the effectiveness of the proposed strategies.

In this letter, legitimate surveillance of a suspicious multichannel decode-and-forward (DF) relay network is investigated, where the monitor eavesdrops, jams or assists the suspicious communication on each subchannel. The problem of mode selection and transmit power allocation of the monitor for maximizing the eavesdropping rate is first transformed to a more tractable form, and then an optimal algorithm and a suboptimal algorithm are proposed. Simulation results confirm the superiority of the proposed algorithms compared to benchmark algorithms, and show that the suboptimal algorithm is low-complex and achieves the performance very closed to the high-complex optimal algorithm.

In this letter, we investigate the outage performance of a joint transmission coordinated multipoint cooperative non-orthogonal multiple access (JT-CoMP-CNOMA) network. It is assumed that there is no direct links between the base stations and the cell-edge user, and thus the cell-centre users act as relays for the cell-edge user. The cell-centre users are assumed to be energy-constrained and adopt the simultaneous wireless information and power transfer (SWIPT) for decoding information and harvesting energy for forwarding. The closed-form approximate expressions for the outage probabilities of the users are derived. The theoretical results are verified by conducting Monte-Carlo simulations. It is shown that significant performance gain for the cell-edge user is achieved by JT-CoMP-CNOMA compared to the conventional CNOMA and the CoMP-CNOMA with opportunistic scheduling.

This letter investigates legitimate surveillance of suspicious communication with a battery-aided full-duplex wireless powered monitor. The suspicious communication consists of multiple time slots, and the aim of the monitor is to maximize the number of slots that can be successfully wiretapped. To enhance the surveillance performance, the monitor can send jamming signals using the energy stored in the battery. It is assumed that the power splitting is used by the monitor for simultaneous energy harvesting and information decoding, and the harvested energy is accumulated in the battery for usage in future slots. The power splitting ratio and the jamming transmit power are optimized with the energy causality constraint under both the offline and online settings of the channel power gains. It is shown that all the proposed algorithms outperform the benchmark algorithm, and the proposed low-complexity suboptimal offline algorithm and the online algorithm are close to optimal.

In this correspondence, legitimate surveillance of a downlink suspicious non-orthogonal multiple access (NOMA) network with multiple groups of suspicious users (SUs), in the presence of a full-duplex monitor equipped with a successive interference cancellation (SIC) decoder, is investigated. Proactive eavesdropping is adopted, i.e., the monitor simultaneously eavesdrops the suspicious communications, and sends jamming signals. The problem of maximizing the number of successfully eavesdropped SUs by jointly optimizing the jamming power allocation and the decoding order at the monitor is investigated considering both imperfect full-duplex self-interference cancellation, and imperfect SIC. By exploring the particular problem structure, a heuristic iterative algorithm is proposed, where the optimal decoding at the monitor is proved to be equal to that at the SUs. Extensive simulations confirm that the proposed algorithm achieves performance very close to the optimal one but with significantly lower complexity, and outperforms various benchmark algorithms.

This letter investigates legitimate surveillance of a suspicious interference network in the presence of a full-duplex monitor. To improve the surveillance performance, jamming from the monitor is applied to interfere with the suspicious users and successive interference cancellation (SIC) decoder is equipped at the monitor to better decode signals from multiple suspicious users. To maximize the number of successfully eavesdropped suspicious users, the jamming power as well as the SIC decoding order at the monitor are optimized. By exploring the particular problem structure, high-complexity optimal and low-complexity suboptimal algorithms are proposed. It is shown that the suboptimal algorithm is practically optimal and the proposed algorithms outperform the benchmark algorithms with conventional SIC decoding order.

With the expansion of the scale of the Internet of Things, the transmission distance and speed of wireless networks need to be improved. Due to the limitation of power and energy of sensor nodes, data transmission is basically carried out by multi-hop relay. Collaborative beamforming is a technology that uses multiple sensor nodes to form a virtual antenna array to transmit high-gain and directional beams, which greatly enhances the transmission range. Compared with the muilt-hop network, collaborative beamforming can reduce the numbers of hop or even transmit data to the target node at one time, thus reducing the time required for routing and forwarding. This means that collaborative beamforming can transmit data more quickly and improve data throughput. In this paper, a brief introduction of cooperative beamforming is given. Then we propose an algorithm based on Collaborative beamforming. Simulation results show that the Collaborative beamforming wireless sensor networks can achieve better throughput compared with multi-hop networks.

This article investigates physical layer security of a downlink multiuser orthogonal frequency division multiplexing (OFDM) Internet of Things (IoT) system with an access point communicating with multiple legitimate IoT devices in the presence of multiple eavesdroppers. For coordinating multiuser communication, the orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) are considered. The IoT devices are assumed to support simultaneous wireless information and power transfer (SWIPT) and can use the harvested energy to jam the eavesdroppers based on full-duplex. The resource allocation problems of maximizing the sum secrecy rate for the OFDMA and TDMA systems are investigated. We first consider the scenario that perfect channel state information (CSI) is available and derive suboptimal algorithms based on alternating optimization. Then we consider the scenario that CSI is imperfect and propose heuristic algorithms. The secrecy performances of the OFDMA and TDMA systems with jamming from the SWIPT IoT devices are compared using extensive simulations. It is shown that the secrecy performance of the OFDMA system is superior over the TDMA system. It is also shown that the algorithm with imperfect CSI is inferior over the algorithm with perfect CSI, but it can outperform the benchmark algorithm with perfect CSI and without jamming.

Background The activation degree of the orbitofrontal cortex (OFC) functional area in drug abusers is directly related to the craving for drugs and the tolerance to punishment. Currently, among the clinical research on drug rehabilitation, there has been little analysis of the OFC activation in individuals abusing different types of drugs, including heroin, methamphetamine, and mixed drugs. Therefore, it becomes urgently necessary to clinically investigate the abuse of different drugs, so as to explore the effects of different types of drugs on the human brain. Methods Based on prefrontal high-density functional near-infrared spectroscopy (fNIRS), this research designs an experiment that includes resting and drug addiction induction. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed drugs) were collected using fNIRS and analyzed by combining algorithm and statistics. Results Linear discriminant analysis (LDA), Support vector machine (SVM) and Machine-learning algorithm was implemented to classify different drug abusers. Oxygenated hemoglobin (HbO2) activations in the OFC of different drug abusers were statistically analyzed, and the differences were confirmed. Innovative findings: in both the Right-OFC and Left-OFC areas, methamphetamine abusers had the highest degree of OFC activation, followed by those abusing mixed drugs, and heroin abusers had the lowest. The same result was obtained when OFC activation was investigated without distinguishing the left and right hemispheres. Conclusions The findings confirmed the significant differences among different drug abusers and the patterns of OFC activations, providing a theoretical basis for personalized clinical treatment of drug rehabilitation in the future.

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signal collection and experimentation paradigm with electroencephalogram (EEG) and forehead high-density near-infrared spectroscopy (NIRS) device. The drug addicts are classified into mild, moderate and severe groups with reference to the suggestions of researchers and medical experts. Data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is collected, and then used to design an addiction degree testing algorithm based on decision fusion. The algorithm is used to classify mild, moderate and severe addiction. This paper pioneers to use two types of Convolutional Neural Network (CNN) to abstract the EEG and NIR data of drug addicts, and introduces batch normalization to CNN, thus accelerating training process, reducing parameter sensitivity, and enhancing system robustness. The characteristics output by two CNNs are transformed into dimensions. Two new characteristics are assigned with a weight of 50% each. The data is used for decision fusion. In the networks, 27 subjects are used as training sets, 9 as validation sets, and 9 as testing sets. The 3-class accuracy remains to be 63.15%, preliminarily justifying this method as an effective approach to measure drug addiction degree. And the method is ready to use, objective, and offers results in real time.

There is an increasing drug resistance of animal-derived pathogens, seriously posing a huge threat to the health of animals and humans. Traditional drug resistance testing methods are expensive, have low efficiency, and are time-consuming, making it difficult to evaluate overall drug resistance. To develop a better approach to detect drug resistance, a small sample of Escherichia coli resistance data from 2003 to 2014 in Chengdu, Sichuan Province was used, and multiple regression interpolation was applied to impute missing data based on the time series. Next, cluster analysis was used to classify anti-E. coli drugs. According to the classification results, a GM(1,1)-BP model was selected to analyze the changes in the drug resistance of E. coli, and a drug resistance prediction system was constructed based on the GM(1,1)-BP Neural Network model. The GM(1,1)-BP Neural Network model showed a good prediction effect using a small sample of drug resistance data, with a determination coefficient R2 of 0.7830 and an RMSE of only 0.0527. This model can be applied for the prediction of drug resistance trends of other animal-derived pathogenic bacteria, and provides the scientific and technical means for the effective assessment of bacterial resistance.

In this letter, we consider a multiuser mobile-edge computing (MEC) system with latency constraint. In order to meet the latency requirement and save energy consumption, each user can partially offload the task to the MEC server for edge computing. Data compression is applied to compress the offloaded data before transmission to reduce the data size. The problem of jointly optimizing computation offloading, data compression and resource allocation to minimize energy consumption under the latency constraint and finite MEC computation capacity is considered. We transform the non-convex problem into a convex one and apply convex optimization to solve it. The simulation results demonstrate that our proposed scheme significantly outperforms the benchmark schemes.

This paper considers a multiuser orthogonal frequency division multiplexing (OFDM)-based wireless powered communication networks (WPCNs) with simultaneous wireless information and power transfer (SWIPT). In particular, both uplink and downlink communications are considered by dividing the time for transmission into downlink and uplink communication phases. In the downlink, SWIPT is adopted at the users with nonlinear energy harvesting model, and in the uplink, users use the harvested energy to perform uplink data transmission. We propose a suboptimal resource allocation scheme to maximize the weighted downlink and uplink sum rate by optimizing the time, subcarrier and power allocation. It is shown that the proposed scheme can well balance between the downlink rate and the uplink rate.

This paper considers a downlink multiuser orthogonal frequency division multiple (OFDM) system with simultaneous wireless information and power transfer (SWIPT), where the users decode information and harvest energy using the same received downlink signals. In particular, both power splitting (PS) and time switching (TS) SWIPT modes are considered for coordinating information transferring and energy harvesting. For multiuser information transmission, OFDM access (OFDMA) and time division multiple access (TDMA) are adopted for PS-SWIPT and TS-SWIPT, respectively. The outage performance of such a multiuser OFDM system with SWIPT is investigated. Particularly, we optimize the subcarrier allocation, power allocation and PS ratio for PS-SWIPT, and the power allocation and time allocation for TS-SWIPT, aiming at minimizing the fraction of outage over all users under the minimum harvest energy constraint along with the peak and total transmit power constraints. Suboptimal heuristic algorithms are obtained based on the Lagrange duality method. The outage performances of PS-SWIPT and TS-SWIPT are compared under various system parameter settings. We show that the outage performance of TS-SWIPT is better than that of the PS-SWIPT, except when the minimum required harvested energy is large, the total transmit power limit is small, or the number of users is small.

To enhance public safety, it is necessary to legitimately eavesdrop communication between suspicious users, such as terrorists and criminals. In this paper, we investigate legitimate eavesdropping in a wireless-powered suspicious communication network (WPSCN) with a wireless-powered suspicious transmitter and a suspicious receiver, who is legitimately eavesdropped by a legitimate monitor with the help from a wireless-powered friendly jammer. The “harvest-then-transmit” protocol is adopted by the suspicious communication to coordinate wireless energy transfer and information transfer. Both the suspicious receiver and the legitimate monitor can act as power sources for the suspicious transmitter and the jammer. The legitimate monitor can control the suspicious communication rate by broadcasting power signals, while the jammer can harvest energy and then interfere with the suspicious communication in collaboration with the legitimate monitor. Four different metrics are adopted to evaluate the eavesdropping performance based on different application requirements, and they are the successful eavesdropping probability, the average eavesdropping rate, the relative eavesdropping rate, and the eavesdropping energy efficiency. For each performance metric, we derive the optimal transmit power of the monitor for energy transfer and the optimal jamming transmit power of the jammer to maximize the performance metric under the peak transmit power constraint at the monitor and the energy causality constraint at the jammer. Some important insights are derived from these optimal power control policies. The simulations are conducted to verify the proposed optimal power control for legitimate eavesdropping in the WPSCN. It is shown that the proposed optimal power control policies greatly outperform other benchmark schemes.

Defect inspection plays an essential role in ensuring quality of industrial products. The most widely used human visual inspection method has some drawbacks such as high cost and low efficiency, which bring an eager demand for the application of automatic defect inspection algorithm in actual production. However, few industrial production lines use automatic detection devices due to the gap between data collected in the actual production environment and ready-made datasets. Lace is one of the industrial products which completely depends on manual defect inspection. The complex and fine texture of lace makes it difficult to extract regular patterns using the existing image-based defect inspection methods. In this paper, we propose to collect lace videos in the weaving stage and design a deep-learning-based anomaly detection framework to detect lace defects. The framework contains three stages, namely video pre-processing stage, pixel reconstruction stage and pixel classification stage. In the offline phase, only defect-free lace videos are needed to train the pixel reconstruction model and calculate the detection threshold by our adaptive thresholding method. In the online phase, the proposed framework reconstructs lace videos and performs defect inspection using reconstruction error and the pre-set threshold. As far as we know, this paper the first to detect fabric defects by videos. Experimental results on artificial defect videos demonstrate the effectiveness of the proposed framework.

This paper studies physical layer security of a three node multicarrier network with a source node, a destination node and a full-duplex proactive eavesdropper who sends jamming signals for improving its eavesdropping performance. The problem of transmit power allocation for minimizing the average secrecy outage probability on all subcarriers is investigated under the assumptions that the channel state information (CSI) related to the eavesdropper is perfectly known and only channel distribution information (CDI) is known. Algorithms are proposed for the optimization problem and are shown to greatly outperform the benchmark algorithms.

In order to study the dynamic characteristics of quayside container crane hoisting system, a simplified mechanical model and a mathematical model of quayside container crane are established based on the finite element pre-analysis. Besides, a dynamic model of quayside container crane hoisting system under the excitation of wire rope retracting displacement is established. Taking a certain type of quayside container crane as the research object, the whole hoisting dynamic process of the quayside container crane system is simulated, and the dynamic response of the quayside container crane during the hoisting process is obtained by numerical method. The hoisting condition is divided into two stages and the transient analysis is carried out. At the same time, the different rope-retracting speeds are compared and analyzed.

It is necessary to obtain the dynamic characteristics of the quayside container crane for the health monitoring of its structure system. Different working conditions will bring different dynamic characteristics. Considering the complexity of the connection boundary condition simulation of large construction machinery, this paper uses the stiffness element MATRIX27 to simulate the connection condition of the quayside container crane. Then, based on the finite element model of the crane, combined with the selected specific monitoring positions, the dynamic characteristics of the quayside container crane are analyzed considering the normal operation and the eccentric load operation. It is concluded that the eccentric load of the crane will have a greater impact on the speed and acceleration response of the traveling direction of the crane. However, it has a smaller impact on the stress distribution and the wheel pressure of the crane. The above results also provide a basis for the selection of the specific content of health monitoring.

Under the background of "Wisdom Drug Rehabilitation", we introduced "Artificial Intelligence and Big Data" into "exercise rehabilitation" work of drug addicts in judicial administrative system. It is a practical innovation of drug treatment in China. This article will elaborate this innovation of the construction and application of "Exercise Rehabilitation" intelligence platform system. This system will improve mental status, alleviate physical and psychological symptoms, ensure safety in places, lighten the burden of professional police officers, make rapid analysis, make accurate decisions and improve the integrity rate of the addict.

We consider a three-node multicarrier network consisting of a source node, a wireless powered full-duplex destination node, and an eavesdropper. The destination node is assumed to adopt the power splitting technique to coordinate information decoding and energy harvesting. To improve security performance, the destination node can send jamming signals using the harvested energy. The problem for maximizing the secrecy rate by optimizing transmit power allocation at the source node, the transmit power allocation for jamming at the destination node and the power splitting ratio at the destination node is investigated and solved by a proposed iterative algorithm. Simulation results show that the proposed algorithm outperforms other reference algorithms.

This paper investigates physical layer security of a pair of legitimate source-destination nodes in the presence of an eavesdropper. Specifically, the source-destination pair hires a wireless powered jammer to interfere with the eavesdropper for improving the secrecy outage performance, and then the wireless powered jammer hires a power station for broadcasting energy. We model the interactions among the source-destination pair, the jammer and the power station as a three-stage Stackelberg game. In particular, the power station who acts as a leader prices the energy transmitted to the jammer, the jammer who acts as a follower of the power station decides the required transmit power of the power station and prices the interference energy caused to the eavesdropper, and the source-destination pair who acts as a follower of the power station and the jammer decides the required jamming power and the time allocation. An algorithm based on backward induction is proposed to achieve the Stackelberg equilibrium, and its effectiveness is verified by simulations. The proposed algorithm is shown to achieve much lower secrecy outage probability while consumes lower transmit power of the power station compared to the benchmark algorithm.

This paper investigates physical layer secure communication of a three node multicarrier network with a legitimate transmitter, a legitimate receiver, and a wireless powered full-duplex eavesdropper, where the eavesdropper splits the received signal power into two parts, one for energy harvesting and the remaining for information decoding. The eavesdropper sends jamming signals to interfere with the legitimate communication using the harvested energy to degrade the secure communication of the legitimate user. We use the non-cooperative game to model the interaction between the legitimate user and the eavesdropper, where the aim of the legitimate user is to maximize its sum secrecy rate on all subcarriers under the total transmit power constraint and the aim of the eavesdropper is to minimize the sum secrecy rate of the legitimate communication on all subcarriers under the energy causality constraint. We propose an iterative algorithm to achieve the Nash equilibrium of the non-cooperative game. Simulation results verify the proposed algorithm and show that the proposed algorithm significantly outperforms the benchmark algorithms and is resistant to the jamming from the eavesdropper.

We consider a cooperative cognitive wireless powered communication network consisting of a cognitive base station (CBS), multiples secondary users (SUs) and multiple wireless powered primary users (PUs). A two stage protocol is adopted, in which the CBS transmits information bearing signals and the PUs harvest energy from these RF signals first, and then the PUs transmit information based on the harvested energy. We assume that the CBS prices the energy transferred to the PUs. The interaction between the CBS and the PUs is modeled as a Stackelberg game, in which the CBS acts as a leader to determine the prices and the time allocation, while the PUs act as followers to determine the required transmit power of the CBS. An algorithm is proposed to achieve the Stackelberg equilibrium. It is shown that the proposed algorithm outperforms the equal-time benchmark algorithm in terms of the utility of the CBS, and is inferior to the equal-time benchmark algorithm in terms of the average utility of the PUs.

This paper considers a cognitive wireless powered communication network (CWPCN) consisting of a cognitive base station (CBS) and a set of secondary users (SUs) sharing spectrum with a primary user (PU). The transmission time is assumed to be divided into two parts, where the CBS transfers energy in the downlink during the first time part, and the SUs use the harvested energy to transmit in the uplink based on the time-division multiple access (TDMA) during the second time part. We investigate the problem for minimizing the average outage probability of the SUs by jointly optimizing the time allocation and the downlink transmit power allocation under the interference power and the transmit power constraints and propose a heuristic algorithm to solve it. Simulations show that the proposed algorithm greatly outperforms the benchmark algorithms.

Simultaneous wireless information and power transfer (SWIPT), which belongs to energy harvesting techniques, is an important research topic. In existing literature, two SWIPT schemes, namely the time switching (TS) scheme and the power splitting (PS) scheme are adopted. For multiuser orthogonal frequency division multiple (OFDM) systems, this paper proposes a new SWIPT scheme named as the subcarrier sharing (SS) scheme. Resource allocation algorithms for the SS scheme are then proposed for maximizing the sum rate under the minimum harvested energy constraint. We show that the SS scheme outperforms the existing TS and the PS schemes.

We consider a cognitive radio network where multiple secondary users (SUs) coexist with a primary user (PU). There exist multiple eavesdroppers (EAVs) who attempt to overhear the PU confidential messages. We propose to let the SUs interfere with the EAVs to protect the PU and gain transmission opportunities at the same time. A new type of PU protection criterion, namely PU secrecy outage constraint, is proposed to guarantee that the PU secrecy outage probability does not increase due to the SU transmission. Our aim is to maximize the SU ergodic transmission rate subject to the PU secrecy outage constraint and the SU transmit power constraint. For the special single EAV and single SU with perfect channel state information (CSI) scenario, we drive the optimal power allocation strategy as well as a low-complexity closed-form suboptimal strategy. Then, using the insights gained from the special scenario, we propose heuristic joint SU scheduling and power allocation strategies for the complex scenario of multiple EAVs and SUs with perfect or imperfect CSI. Extensive simulations are conducted to verify our proposed strategies. It is shown that the proposed strategies can well protect the PU in terms of secrecy outage probability even with imperfect CSI and simultaneously provide transmission opportunities for the SUs.

Due to explosive growth of computation-intensive applications, multi-access mobile edge computing (MEC) has been proposed as an efficient technology to relieve the burden on mobile users by offloading computation tasks to MEC servers. Meanwhile, nonorthogonal multiple access (NOMA) has been proposed to improve spectrum efficiency by allowing multiple users transmitting on the same time and spectrum resource. Thus, NOMA can be naturally adopted by the MEC systems to improve the offloading performance. In this paper, we consider a NOMA-based multi-access MEC system with multiple MEC servers and multiple users. It is assumed that each user has multiple computation tasks, where each task shall be offloaded to one MEC server for computing. NOMA is applied for uploading multiple tasks to MEC servers as well as downloading multiple task results. The problem of jointly optimizing the MEC server allocation, the transmit power allocation of the users, the transmit power allocation of the MEC servers, the computation resource allocation of the MEC servers, the time allocation and the channel allocation, for minimizing the overall delay of all tasks is investigated. We then propose a heuristic algorithm to solve the problem, where the computation resource allocation of the MEC servers and the time allocation are optimized by the bisection search method, the transmit power allocations are obtained in closed-form, and the MEC server allocation and the channel allocation are optimized by the swap matching algorithm. Extensive simulations demonstrate that the proposed algorithm significantly outperforms various benchmark algorithms.

In this study, the authors investigate the physical-layer security in a cognitive radio network where both the secondary user (SU) and the primary user (PU) are facing security threats from the malicious eavesdroppers. To protect the PU, the SU acts as a friendly jammer to interfere with the eavesdroppers by splitting a certain portion of the transmit power for sending the jamming noise. The problem of optimising SU scheduling, power allocation and power splitting ratio to maximise the SU ergodic secrecy rate subject to the PU secrecy outage constraint with imperfect channel state information available at the SU is investigated based on the dual optimisation method. In addition, a greedy algorithm is also proposed for minimising the PU secrecy outage probability. Simulation results indicate that the proposed algorithms are effective in improving the PU secrecy performance in terms of secrecy outage probability as well as providing secure communications for the SU.

This letter focuses on cooperative resource allocation in multi-carrier cognitive radio (CR) networks where the primary user (PU) is wirelessly powered by the energy harvested from the signals transmitted by the secondary user (SU) for its own information transfer. We adopt the two-stage harvest-then-transmit protocol, where the wireless power transfer for the PU is executed first and then the information transfer for the PU takes place. Specifically, two schemes, namely subcarrier sharing (SS) and subcarrier exclusivity, are considered for coordinating the information transfer of the PU and the SU in the second stage. The resource allocation problems to maximize the SU rate subject to the PU minimum rate constraint are investigated. It is shown that the SS scheme achieves higher SU rate than the SE scheme at the cost of higher computation time when the PU minimum rate constraint is stringent and the number of subcarriers is large.

This letter considers a wireless powered underlay cognitive radio network where secondary users (SUs) first harvest energy in the downlink wireless power transfer (WPT) phase and then use the energy for data transmission in the uplink wireless information transmission phase adopting the time-division multiple access. Our aim is to maximize the sum rate of the SUs under the interference power constraint enforced by the primary user and the transmit power constraints. The optimal joint power control for the WPT phase and time allocation among the SUs is obtained. Simulation results show that the obtained optimal scheme outperforms the scheme that employs only the optimal time allocation or the optimal power control.

This paper addresses the power allocation problem for cognitive radio (CR) based on hybrid-automatic-repeat-request (HARQ) with chase combining (CC) in Nakagamimslow fading channels. We assume that, instead of the perfect instantaneous channel state information (CSI), only the statistical CSI is available at the secondary user (SU) transmitter. The aim is to minimize the SU outage probability under the primary user (PU) interference outage constraint. Using the Lagrange multiplier method, an iterative and recursive algorithm is derived to obtain the optimal power allocation for each transmission round. Extensive numerical results are presented to illustrate the performance of the proposed algorithm.

This paper investigates physical layer security of a multicarrier network with a legitimate source node, a legitimate destination node and an eavesdropper. Specifically, the eaves-dropper adopts the simultaneous wireless information and power transfer (SWIPT) by splitting the power of the received signals from the source node into two parts, one for energy harvesting and the remaining for information decoding, and a minimum harvested energy at the eavesdropper is required. We formulate a Stackelberg game, where the eavesdropper acts as a follower who aims to maximize its sum eavesdropping rate under the minimum harvested energy constraint, and the source node acts as a leader who aims to maximize its sum secrecy rate under the transmit power constraint. An algorithm is proposed to achieve the Stackelberg equilibrium. Simulation results are provided to verify the proposed algorithm. It is shown that the proposed algorithm greatly outperforms the benchmark algorithms.

We consider a multicarrier cooperative cognitive wireless powered communication network consisting of multiple secondary users (SUs) communicating to a cognitive base station (CBS), in the presence of a wireless powered primary user (PU). The transmission time is divided into two phases, where the first phase is for the SUs transmitting information bearing signals to the CBS while the PU harvesting energy from these RF signals, and the second phase is for PU transmission. It is assumed that the PU prices the interference energy from the SUs caused to the PU. We model the interaction between the SUs and the PU as a Stackelberg game. Specifically, the SUs act as followers which determine their transmit powers in the first phase, while the PU acts as a leader which determines the transmit power of the PU in the second phase, the subcarrier allocation in the first phase, the prices charged to the SUs, and the time allocation. An algorithm is proposed to achieve the Stackelberg equilibrium. The effectiveness of the proposed algorithm is verified by simulation results.

In this paper, we investigate physical layer security, where a source node communicates with a wireless powered full-duplex destination node using multiple subcarriers in the presence of an eavesdropper. The destination node adopts the time switching technique to coordinate information decoding and energy harvesting, and can use the harvested energy to send jamming signals to interfere with the eavesdropper. We investigate the problem of optimizing transmit power allocation at the source node, the transmit power allocation for jamming at the destination node and the time allocation for maximizing the secrecy rate, and propose an iterative algorithm to solve the optimization problem. It is shown by simulation results that the proposed algorithm greatly outperforms other reference algorithms.