Spectrum sharing cognitive radio (CR) with maximal ratio combining (MRC) diversity under asymmetric fading is studied. Specifically, the channel on the secondary transmitter (STx) to the secondary receiver (SRx) link is Nakagami-m distributed while the channel on the STx to the primary receiver (PRx) link is Rayleigh distributed, and the channel state information (CSI) on the STx-PRx link is assumed to be outdated due to feedback delay. The outage capacity of the secondary user (SU) is derived under the average interference and peak transmit power constraints. The results supported by simulations are presented and show the effects of various system parameters on the outage capacity. Particularly, it is shown that the outdated CSI has no impact on the outage capacities in the cases of low peak transmit power constraint and zero-outage probability. It is also shown that MRC diversity can significantly improve the outage capacity especially for the zero-outage capacity and the outage capacity under low outage probability.

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.

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, we study the power control of a cognitive radio (CR) network, where the secondary user (SU) is allowed to share the spectrum with the primary user (PU) only if the signal to interference plus noise ratio (SINR) at the PU is higher than a predetermined level. Both PU fixed power control and PU adaptive power control are considered. Specifically, for the PU adaptive power control, the PU will cooperate with the SU by transmitting with adaptive power. The optimal power control schemes for the SU to maximize the SU throughput under the PU SINR constraint are derived. It is shown that the SU throughput achieved by the optimal power control with the PU adaptive power control is a significant improvement over the optimal power control with the PU fixed power control, especially under high power constraint and low SINR constraint.

As a generalization of perfect nonlinear (PN) functions, zero-difference balanced (ZDB) functions play an important role in coding theory, cryptography and communications engineering. Inspired by a foregoing work of Liu et al. [1], we present a class of ZDB functions with new parameters based on the cyclotomy in finite fields. Employing these ZDB functions, we obtain simultaneously optimal constant composition codes and perfect difference systems of sets.

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.

Mobile edge computing (MEC) is a new computing paradigm, which provides computing support for resource-constrained user equipments (UEs). In this letter, we design an effective incentive framework to encourage MEC operators to provide computing service for UEs. The problem of jointly allocating communication and computing resources to maximize the revenue of MEC operators is studied. Based on auction theory, we design a multi-round iterative auction (MRIA) algorithm to solve the problem. Extensive simulations have been conducted to evaluate the performance of the proposed algorithm and it is shown that the proposed algorithm can significantly improve the overall revenue of MEC operators.

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.

Cognitive radio (CR) in spectrum sharing mode allows secondary user (SU) to share the same spectrum simultaneously with primary user (PU), as long as the former guarantees no harmful interference is caused to the latter. This letter proposes a new type of constraint to protect the PU systems that are carrying delay-sensitive applications, namely the PU effective capacity loss constraint, which sets an upper bound on the maximum effective capacity loss of the PU due to the SU transmission. In addition, the PU effective capacity loss constraint is approximately transformed to the interference temperature (power) constraint, to make it easier to be implemented. As an example, we obtain a closed form expression of the SU effective capacity under the approximated peak interference power constraint and the results of simulations validate the proposed PU protection criterion.

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.

In this letter, we consider a cognitive radio network where multiple secondary users (SUs) share the spectrum bands with multiple primary users (PUs) who are facing security threats from multiple eavesdroppers. By adopting the PU secrecy outage constraint to protect the PUs, we optimize the joint user and power allocation for the SUs to maximize the SU ergodic transmission rate. Simulation results are presented to verify the effectiveness of the proposed algorithm. It is shown that the proposed algorithm outperforms the existing scheme, especially for a large number of PUs and a small number of SUs. It is also shown that the number of eavesdroppers has negligible impact on the performance improvement of the proposed algorithm compared to the existing scheme. In addition, it is shown that increasing the number of eavesdroppers has insignificant impact on the SU performance if the number of eavesdroppers is already large.

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.

As an optimal combinatorial object, cyclic perfect Mendelsohn difference family (CPMDF) was introduced by Fuji-Hara and Miao to construct optimal optical orthogonal codes. In this paper, we propose a direct construction of disjoint CPMDFs from the Zeng-Cai-Tang-Yang cyclotomy. Compared with a recent work of Fan, Cai, and Tang, our construction doesn't need to depend on a cyclic difference matrix. Furthermore, strictly optimal frequency-hopping sequences (FHSs) are a kind of optimal FHSs which has optimal Hamming auto-correlation for any correlation window. As an application of our disjoint CPMDFs, we present more flexible combinatorial constructions of strictly optimal FHSs, which interpret the previous construction proposed by Cai, Zhou, Yang, and Tang.

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.