The problem of resource allocation to minimize the outage probability for the secondary user (SU) groups in a cognitive radio (CR) multicast network is investigated. We propose a joint power and rate allocation scheme that provides significant improvement over the conventional scheme in terms of outage probability.

This letter investigates the performance of a legitimate surveillance system, where a wireless powered legitimate monitor aims to eavesdrop a suspicious communication link. Power splitting technique is adopted at the monitor for simultaneous information eavesdropping and energy harvesting. In order to maximize the successful eavesdropping probability, the power splitting ratio is optimized under the minimum harvested energy constraint. Assuming that perfect channel state information (CSI) or only the channel distribution information (CDI) is available, the closed-form maximum successful eavesdropping probability is obtained in Rayleigh fading channels. It is shown that the minimum harvested energy constraint has no impact on the eavesdropping performance if the minimum harvested energy constraint is loose. It is also shown that the eavesdropping performance loss due to partial knowledge of CSI is negligible when the eavesdropping link channel condition is much better than that of the suspicious communication link channel.

Conventional Nyquist folding receiver (NYFR) uses zero crossing rising (ZCR) voltage times to control the RF sample clock, which is easily affected by noise. Moreover, the analog and digital parts are not synchronized so that the initial phase of the input signal is lost. Furthermore, it is assumed in most literature that the input signal is in a single Nyquist zone (NZ), which is inconsistent with the actual situation. In this paper, we propose an improved architecture denominated as a dual-channel NYFR with adjustable local oscillator (LOS) and an information recovery algorithm. The simulation results demonstrate the validity and viability of the proposed architecture and the corresponding algorithm.

A discriminative reference-based method for scene image categorization is presented in this letter. Reference-based image classification approach combined with K-SVD is approved to be a simple, efficient, and effective method for scene image categorization. It learns a subspace as a means of randomly selecting a reference-set and uses it to represent images. A good reference-set should be both representative and discriminative. More specifically, the reference-set subspace should well span the data space while maintaining low redundancy. To automatically select reference images, we adapt affinity propagation algorithm based on data similarity to gather a reference-set that is both representative and discriminative. We apply the discriminative reference-based method to the task of scene categorization on some benchmark datasets. Extensive experiment results demonstrate that the proposed scene categorization method with selected reference set achieves better performance and higher efficiency compared to the state-of-the-art methods.

In underlay cognitive radio (CR) multicast networks, the cognitive base station (CBS) can transmit at the lowest rate of all the secondary users (SUs) within the multicast group. Existing works showed that the sum rate of such networks saturates when the number of SUs increases. In this letter, for CR multicast networks with multiple channels, we group the SUs into different subgroups, each with an exclusive channel. Then, the problem of joint user grouping and power allocation to maximize the sum rate of all subgroups under the interference power constraint and the transmit power constraint is investigated. Compared to exponential complexity in the number of SUs required by the optimal algorithm, we proposed an efficient algorithm with only linear complexity. Simulation results confirm that the proposed algorithm achieves the sum rate very closed to that achieved by the optimal algorithm and greatly outperforms the maximum signal-to-noise-ratio based user grouping algorithm and the conventional algorithm without user grouping.

This letter considers a legitimate proactive eavesdropping scenario, where a half-duplex legitimate monitor hires a third-party jammer for jamming the suspicious communication to improve the eavesdropping performance. The interaction between the third-party jammer and the monitor is modeled as a Stackelberg game, where the jammer moves first and sets the price for jamming the suspicious communication, and then the legitimate monitor moves subsequently and determines the requested transmit power of the jamming signals. We derive the optimal jamming price and the optimal jamming transmit power. It is shown that the proposed price-based proactive eavesdropping scheme is effective in improving the successful eavesdropping probability compared to the case without jamming. It is also shown that the proposed scheme outperforms the existing full-duplex scheme when the residual self-interference cannot be neglected.

We propose a joint channel, power and rate allocation scheme to minimize the weighted group outage probability of the secondary users (SUs) in a downlink cognitive radio (CR) multicast network coexisting with a primary network, subject to the service outage constraint as well as the interference power constraint and the transmit power constraint. It is validated by simulation results that, compared to the existing schemes, the proposed scheme achieves lower group outage probability.

The outage capacity of the fading cognitive multicast channel (CMC) is investigated in this paper. Assume that the instantaneous channel state information (CSI) of the interference link between the cognitive base station (CBS) and the primary user (PU) is available at the CBS, we derive the outage capacity in Rayleigh fading environments under the interference power and the transmit power constraints. Under the condition that the interference power limit is sufficiently larger or smaller than the transmit power limit, the asymptotic outage capacity is obtained in closed-form. Assume that only the channel distribution information (CDI) of the interference link is available at the CBS, the outage capacity under the interference outage and the transmit power constraints is derived in closed-form. The theoretical results are confirmed by simulations. It is shown that the outage capacity is not degraded due to partial knowledge of the interference link when the interference power limit is sufficiently larger than the transmit power limit. It is also shown that the capacity gain due to increasing the number of the secondary users (SUs) is negligible if the number of the SUs is already large. Additionally, the case of CDI with estimation error is also investigated. Interestingly, we show that the estimation error of CDI may be a positive factor for improving the outage capacity of the CMC.

This letter considers a cognitive radio (CR) network where multiple secondary downlinks coexist with a primary network. The primary user (PU) is assumed to be protected by the interference outage constraint with only channel distribution information (CDI) being available at the secondary users (SUs). The power allocation problem to maximize the sum outage capacity of the SUs under the interference outage constraint and the transmit power constraint is investigated. Due to the difficulty in obtaining the optimal solution, we propose a heuristic power allocation algorithm based on the bisection search method that can guarantee to satisfy both the interference outage and the transmit power constraints. It is shown that the proposed algorithm converges fast and outperforms other reference algorithms.

This paper studies the problem of power allocation for a two-way orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) network to maximize the ergodic capacities of the secondary users (SUs). Under the assumption that the SUs know the channel state information (CSI) of the interference links between the SUs and the primary user (PU) perfectly, the optimal power allocation algorithm under the interference and transmit power constraints is derived. In addition, we further assume that the SUs only know the channel distribution information (CDI) of the interference links and propose a heuristic algorithm under the interference outage and transmit power constraints based on Gaussian approximation. It is shown that the ergodic capacity with CDI is not degraded compared to that with perfect CSI under tight transmit power constraint. Moreover, it is shown that the interference outage constraint is satisfied even for very small number of subcarriers if an interference power margin is adopted for Gaussian approximation.

Duty-cycle MAC protocols achieve high energy-efficiency. However, duty-cycle MACs introduce significant end-to-end delivery latency. Recently proposed protocols such as RMAC and PRMAC improve the latency of duty-cycle MAC protocols by employing a mechanism of multi-hop wakeup reservation to allow a packet to be forwarded over multiple hops in a single communication cycle. However, these protocols can not efficiently handle cross traffic bursts which are common in applications with space-correlated event detection. If there are multiple packets to send in each flow, most of the data packets will be seriously postponed. This paper proposes a multi-channel pipelined routing-enhanced MAC protocol, called MPR-MAC, to handle this problem. By jointly employing channel diversity and time diversity, MPR-MAC allows cross data flows to forward multiple packets respectively in a single communication cycle without interfering with each other. Simulation results show the advantage of MPR-MAC in handling cross data flows and the significant performance upgrade in terms of end-to-end latency and energy efficiency.

A triple loss based framework for generalized zero-shot learning is presented in this letter. The approach learns a shared latent space for image features and attributes by using aligned variational autoencoders and variants of triplet loss. Then we train a classifier in the latent space. The experimental results demonstrate that the proposed framework achieves great improvement.

In this letter, a flexible and compatible with fine resolution radar frequency measurement receiver is designed. The receiver is implemented on the platform of Virtex-5 Field Programmable Grid Array (FPGA) from Xilinx. The Digital Down Conversion (DDC) without mixer based on polyphase filter has been successfully introduced in this receiver to obtain lower speed data flow and better resolution. This receiver can adapt to more modulation types and higher density of pulse flow, up to 200000 pulses per second. The measurement results indicate that the receiver is capable of detecting radar pulse signal of 0.2us to 2.5ms width with a major frequency root mean square error (RMSE) within 0.44MHz. Moreover, the wider pulse width and the higher decimation rate of DDC result in better performance. This frequency measurement receiver has been successfully used in a spaceborne radar system.

Two voltage controlled current source (VCCS) models of double-channel p-type lateral extended drain MOS (DPLEDMOS) are firstly proposed to analyze the energy recovery circuit (ERC) efficiency of PDP data driver IC. In terms of the mathematical function between ID and VDS, the VCCS models are created. The presented models can be embedded in system software Saber to simulate the ERC waveform of data driver IC. A test board and a PDP system are used to verify the accuracy of the VCCS models. The experimental measurements agree with the simulation results very well and the maximum model error is 3.89%. Simulation results also show that the ERC efficiency of PDP data driver IC is influenced by three factors: the value of charge time TERC, the drain current ID, and the capacitance of CL. In an actual PDP system, TERC is restricted and CL is changeless. The ERC efficiency of PDP data driver IC can be improved significantly by using DPLEDMOS which has higher ID capacity. The proposed VCCS models of DPLEDMOS can be used to predict the ERC efficiency accurately.

This letter studies secure communication in a wireless powered communication network with a full-duplex destination node, who applies either power splitting (PS) or time switching (TS) to coordinate energy harvesting and information decoding of received signals and transmits jamming signals to the eavesdropper using the harvested energy. The secrecy rate is maximized by optimizing PS or TS ratio and power allocation. We propose iterative algorithms with power allocation optimized by the successive convex approximation method. Simulation results demonstrate that the proposed algorithms are superior to other benchmark algorithms.

Real-time and scalable multicast services are of paramount importance to Industrial Internet of Things (IIoT) applications. To realize these services, the multicast algorithm should, on the one hand, ensure the maximum delay of a multicast session not exceeding its upper delay bound. On the other hand, the algorithm should minimize session costs. As an emerging networking paradigm, Software-defined Networking (SDN) can provide a global view of the network to multicast algorithms, thereby bringing new opportunities for realizing the desired multicast services in IIoT environments. Unfortunately, existing SDN-based multicast (SDM) algorithms cannot meet the real-time and scalable requirements simultaneously. Therefore, in this paper, we focus on SDM algorithm design for IIoT environments. To be specific, the paper first converts the multicast tree construction problem for SDM in IIoT environments into a delay-bounded least-cost shared tree problem and proves that it is an NP-complete problem. Then, the paper puts forward a shared tree (ST) algorithm called SDM4IIoT to compute suboptimal solutions to the problem. The algorithm consists of five steps: 1) construct a delay-optimal shared tree; 2) divide the tree into a set of subpaths and a subtree; 3) optimize the cost of each subpath by relaxing the delay constraint; 4) optimize the subtree cost in the same manner; 5) recombine them into a shared tree. Simulation results show that the algorithm can provide real-time support that other ST algorithms cannot. In addition, it can achieve good scalability. Its cost is only 20.56% higher than the cost-optimal ST algorithm. Furthermore, its computation time is also acceptable. The algorithm can help to realize real-time and scalable multicast services for IIoT applications.

The problem of power allocation for the secondary user (SU) in a cognitive radio (CR) network is investigated in this paper. The primary user (PU) is protected by the average interference power constraint. Besides the average interference power constraint at the PU, the transmit power of the SU is also subject to the peak or average transmit power constraint. The aim is to balance between the goal of maximizing the ergodic capacity and the goal of minimizing the outage probability of the SU. Power allocation schemes are then proposed under the aforementioned setups. It is shown that the proposed power allocation schemes can achieve high ergodic capacity while maintaining low outage probability, whereas existing schemes achieve either high ergodic capacity with high outage probability or low outage probability with low ergodic capacity.

Industrial networks need to provide reliable communication services, usually in a redundant transmission (RT) manner. In the past few years, several device-redundancy-based, layer 2 solutions have been proposed. However, with the evolution of industrial networks to the Industrial Internet, these methods can no longer work properly in the non-redundancy, layer 3 environments. In this paper, an SDN-based reliable communication framework is proposed for the Industrial Internet. It can provide reliable communication guarantees for mission-critical applications while servicing non-critical applications in a best-effort transmission manner. Specifically, it first implements an RT-based reliable communication method using the Industrial Internet's link-redundancy feature. Next, it presents a redundant synchronization mechanism to prevent end systems from receiving duplicate data. Finally, to maximize the number of critical flows in it (an NP-hard problem), two ILP-based routing & scheduling algorithms are also put forward. These two algorithms are optimal (Scheduling with Unconstrained Routing, SUR) and suboptimal (Scheduling with Minimum length Routing, SMR). Numerous simulations are conducted to evaluate its effectiveness. The results show that it can provide reliable, duplicate-free services to end systems. Its reliable communication method performs better than the conventional best-effort transmission method in terms of packet delivery success ratio in layer 3 networks. In addition, its scheduling algorithm, SMR, performs well on the experimental topologies (with average quality of 93% when compared to SUR), and the time overhead is acceptable.

This letter studies physical-layer security in a cognitive radio (CR) network, where a primary user (PU) is eavesdropped by multiple collusive eavesdroppers. Under the PU secrecy outage constraint to protect the PU, the secondary users (SUs) are assumed to be allowed to transmit. The problem of joint SU scheduling and power control to maximize the SU ergodic transmission rate is investigated for both the scenarios of perfect and imperfect channel state information (CSI). It is shown that, although collusive eavesdroppers degrade the PU performance compared to non-collusive eavesdroppers, the SU performance is actually improved when the number of eavesdroppers is large. It is also shown that our proposed scheme with imperfect CSI can guarantee that the PU performance is unaffected by imperfect CSI.

The problem of power allocation in maximizing the energy efficiency of the secondary user (SU) in a delay quality-of-service (QoS) constrained CR network is investigated in this paper. The average interference power constraint is used to protect the transmission of the primary user (SU). The energy efficiency is expressed as the ratio of the effective capacity to the total power consumption. By using non-linear fractional programming and convex optimization theory, we develop an energy efficiency power allocation scheme based on the Dinkelbach method and the Lagrange multiplier method. Numerical results show that the proposed scheme outperforms the existing schemes, in terms of energy efficiency.