Dynamic Update Scheme of Spectrum Information Based on Spectrum Opportunity Incentive in the Database-Assisted Dynamic Spectrum Management

To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic update mechanism of SI based on spectrum opportunity incentive is established, in which spectrum users are encouraged to actively assist the database to update SI in real time. Secondly, the information update contribution(IUC) of spectrum opportunity is defined to describe the cost of accessing spectrum opportunity for heterogeneous spectrum users, and the profit of SI update obtained by the database from spectrum allocation. The process that the database determines the IUC of spectrum opportunity and spectrum user selects spectrum opportunity is mapped to a Hotelling model. Thirdly, the process of determining the IUC of spectrum opportunities is further modelled as a Stackelberg game by establishing multiple virtual spectrum resource providers(VSRPs) in the database. It is proved that there is a Nash Equilibrium in the game of determining the IUC of spectrum opportunities by VSRPs. Finally, an algorithm of determining the IUC based on a genetic algorithm is designed to achieve the optimal IUC. The-oretical analysis and simulation results show that the proposed method can quickly find the optimal solution of the IUC, and ensure that the spectrum resource provider can obtain the optimal profit of SI update.

Distributed Matching Theory-Based Task Re-Allocating for Heterogeneous Multi-UAV Edge Computing

Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

Joint Optimization for on-Demand Deployment of UAVs and Spectrum Allocation in UAVs-Assisted Communication

To improve the efficiency and fairness of the spectrum allocation for ground communication assisted by unmanned aerial vehicles(UAVs),a joint optimization method for on-demand deployment and spectrum allocation of UAVs is proposed,which is modeled as a mixed-integer non-convex optimization problem(MINCOP).An algorithm to estimate the minimum number of required UAVs is firstly proposed based on the pre-estimation and simulated annealing.The MINCOP is then decomposed into three sub-problems based on the block coordinate descent method,including the spectrum allocation of UAVs,the association between UAVs and ground users,and the deployment of UAVs.Specifically,the optimal spectrum allocation is derived based on the interference mitigation and channel reuse.The association between UAVs and ground users is optimized based on local iterated optimization.A particle-based optimization algorithm is proposed to resolve the subproblem of the UAVs deployment.Simulation results show that the proposed method could effectively improve the minimum transmission rate of UAVs as well as user fairness of spectrum allocation.

Self-Powered Absorptive Reconfigurable Intelligent Surfaces for Securing Satellite-Terrestrial Integrated Networks

Satellite communications have attracted significant interests due to its advantages of large footprint and massive access.However,the commonly used onboard beamforming is hard to achieve reliable security because of the highly correlated legitimate and wiretap downlink channels.We exploit the benefits of satellite-terrestrial integrated network(STIN)and a novel absorptive reconfigurable intelligent surface(RIS)for improving the security of satellite downlink communications(SDC)in the presence of eavesdroppers(Eves).This paper aims to maximize the achievable secrecy rate of the earth station(ES)while satisfying the signal reception constraints,harvested power threshold at the RIS,and total transmit power budget.To solve this nonconvex problem,we propose a penalty-function based dual decomposition scheme,which firstly transforms the original problem into a two-layer optimization problem.Then,the outer layer and inner problems are solved by utilizing the successive convex approximation,Lagrange-dual and Rayleigh quotient methods to obtain the beamforming weight vectors and the reflective coefficient matrix.Finally,simulation results verify the effectiveness of the proposed scheme for enhancing the SDC security.

Local-State Routing in Satellite Constellation Networks from the Perspective of Complex Network

Routing algorithms in satellite constellation networks usually make use of the local state information to adapt to the topology and traffic dynamics,since it’s difficult to obtain the global states in time due to the spatial large-scale feature of constellation networks.Furthermore,they use different range of local states and give these states distinct weights.However,the behind design criterion is ambiguous and often based on experience.This paper discusses the problem from the perspective of complex network.A universal local-state routing model with tunable parameters is presented to generalize the common characteristics of local-state routing algorithms for satellite constellation networks.Based on this,the impacts of localstate routing algorithms on performance and the correlation between routing and traffic dynamics are analyzed in detail.Among them,the tunable parameters,the congestion propagation process,the critical packet sending rate,and the network robustness are discussed respectively.Experimental results show that routing algorithms can achieve a satisfactory performance by maintaining a limited state awareness capability and obtaining the states in a range below the average path length.This provides a valuable design basis for routing algorithms in satellite constellation networks.

Secure Transmission in Cognitive Radio Networks Using Full-Duplex Technique with Outdated CSI

This paper investigates the effects of the outdated channel state information(CSI)on the secrecy performance of an underlay spectrum sharing cognitive radio networks(CRNs),where the secondary user(SU)source node(Alice)aims to transmit the trusted messages to the full-duplex(FD)aided SU receiver(Bob)with the assistance of cooperative relay(Relay).Considering the impact of feedback delay,outdated CSI will aggravate the system performance.To tackle such challenge,the collaborative zero-forcing beamforming(ZFB)scheme of FD technique is further introduced to implement jamming so as to confuse the eavesdropping and improve the security performance of the system.Under such setup,the exact and asymptotic expressions of the secrecy outage probability(SOP)under the outdated CSI case are derived,respectively.The results reveal that i)the outdated CSI of the SU transmission channel will decrease the diversity gain from min(NANR,NRNB)to NRwith NA,NRand NBbeing the number of antennas of Alice,Relay and Bob,respectively,ii)the introduction of FD technique can improve coding gain and enhance system performance.

Combing Type-Aware Attention and Graph Convolutional Networks for Event Detection

Event detection(ED)is aimed at detecting event occurrences and categorizing them.This task has been previously solved via recognition and classification of event triggers(ETs),which are defined as the phrase or word most clearly expressing event occurrence.Thus,current approaches require both annotated triggers as well as event types in training data.Nevertheless,triggers are non-essential in ED,and it is time-wasting for annotators to identify the“most clearly”word from a sentence,particularly in longer sentences.To decrease manual effort,we evaluate event detectionwithout triggers.We propose a novel framework that combines Type-aware Attention and Graph Convolutional Networks(TA-GCN)for event detection.Specifically,the task is identified as a multi-label classification problem.We first encode the input sentence using a novel type-aware neural network with attention mechanisms.Then,a Graph Convolutional Networks(GCN)-based multilabel classification model is exploited for event detection.Experimental results demonstrate the effectiveness.

Dynamic Spectrum Access Based on Prior Knowledge Enabled Reinforcement Learning with Double Actions in Complex Electromagnetic Environment

The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper.The prior knowledge for the dynamic spectrum access is modeled and a reliability quantification scheme is presented to guide the use of the prior knowledge in the learning process.Furthermore,a spectrum access scheme based on the prior knowledge enabled RL(PKRL)is designed,which effectively improved the learning efficiency and provided a solution for users to better adapt to the fast-changing and high-density electromagnetic environment.Compared with the existing methods,the proposed algorithm can adjust the access channel online according to historical information and improve the efficiency of the algorithm to obtain the optimal access policy.Simulation results show that,the convergence speed of the learning is improved by about 66%with the invariant average throughput.

A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction

The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes.For entity and relation extraction in a specific domain,we propose a hybrid neural framework consisting of two parts:a span-based model and a graph-based model.The span-based model can tackle overlapping problems compared with BILOU methods,whereas the graph-based model treats relation prediction as graph classification.Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain,such as domain dictionaries and dependency structures from texts,into end-to-end neural models.We conducted extensive experiments on a Chinese military entity and relation extraction corpus.The results show that the proposed framework outperforms the baselines with better performance in terms of entity and relation prediction.The proposed method provides insight into problems with the joint extraction of entities and their relations.

Adaptive Secure Transmission for Wireless Powered Communication Networks

This paper investigates secure transmission in a wireless powered communication network(WPCN)with an energy harvesting(EH)source configured with multiple antennas.In the WPCN,the EH source harvests energy from the radio frequency(RF)signals broadcasted by a power beacon(PB),and purely relies on the harvested energy to communicate with the destination in the presence of passive eavesdroppers.It is noteworthy that the RF-EH source is equipped with a finite energy storage to accumulate the harvested energy for the future usage.Moreover,due to energy-constraint and complexitylimitation,the multi-antenna source is only configured with a single RF-chain.To enhance the security for the WPCN,we propose two adaptive transmission schemes,i.e.,energy-aware transmit antenna selection(EATAS)and energy-aware differential spatial modulation(EADSM).According to the energy status and the channel quality,the source adaptively decides whether to transmit confidential information or harvest RF energy.To evaluate the secrecy performance of the proposed schemes,we first study the evolution of the energy storage,and then derive the analytical expressions of connection outage probability(COP),secrecy outage probability(SOP)and efficient secrecy throughput(EST).Numerical results demonstrate that our proposed schemes outperform the existing schemes,i.e.,time-switching based TAS(TS-TAS)Received:May 19,2020 Revised:Sep.13,2020 Editor:Deli Qiao and accumulate-then-transmit(ATT).And,increasing the transmit power of the PB or the capacity of the source’s energy storage is helpful to improve the secrecy performance.Moreover,there exists an optimal transmission rate for each proposed scheme to achieve best secrecy performance.

Relay-Assisted Secure Short-Packet Transmission in Cognitive IoT with Spectrum Sensing

Cognitive Internet of Things(IoT)has at-tracted much attention due to its high spectrum uti-lization.However,potential security of the short-packet communications in cognitive IoT becomes an important issue.This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming(MRC/ZFB)scheme to guarantee the secrecy perfor-mance of dual-hop short-packet communications in cognitive IoT.This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ra-tio(SNR)regime and the infinite blocklength.In ad-dition,the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput.The nu-merical results verify the impact of the system pa-rameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a se-crecy constraint.It is shown that the proposed scheme achieves better secrecy performance than other bench-mark schemes.

Coalitional Game Based Joint Beamforming and Power Control for Physical Layer Security Enhancement in Cognitive IoT Networks

In this paper,the physical layer se-cure transmission in multi-antenna multi-user cogni-tive internet-of-thing(IoT)network is investigated,where the coalitional game based joint beamform-ing and power control scheme is proposed to im-prove the achievable security of cognitive IoT de-vices.Specifically,the secondary network consisting of a muti-antenna secondary transmitter,multiple sec-ondary users(SUs),is allowed to access the licensed spectrum resource of primary user(PU)with underlay approach in the presence of an unauthorized eaves-dropper.Based on the Merge-Split-Rule,coalitional game is formulated among distributed secondary users with cooperative receive beamforming.Then,an alter-native optimization method is used to obtain the op-timized beamforming and power allocation schemes by applying the up-downlink duality.The simulation results demonstrate the effectiveness of our proposed scheme in improving the SU’s secrecy rate and system utility while guaranteeing PU’s interference thresh-old.

Joint User Association and Satellite Selection for Satellite-Terrestrial Integrated networks

In this paper, we investigate a cooperation mechanism for satellite-terrestrial integrated networks. The terrestrial relays act as the supplement of traditional small cells and cooperatively provide seamless coverage for users in the densely populated areas.To deal with the dynamic satellite backhaul links and backhaul capacity caused by the satellite mobility, severe co-channel interference in both satellite backhaul links and user links introduced by spectrum sharing,and the difference demands of users as well as heterogeneous characteristics of terrestrial backhaul and satellite backhaul, we propose a joint user association and satellite selection scheme to maximize the total sum rate. The optimization problem is formulated via jointly considering the influence of dynamic backhaul links, individual requirements and targeted interference management strategies, which is decomposed into two subproblems: user association and satellite selection. The user association is formulated as a nonconvex optimization problem, and solved through a low-complexity heuristic scheme to find the most suitable access point serving each user. Then, the satellite selection is resolved based on the cooperation among terrestrial relays to maximize the total backhaul capacity with the minimum date rate constraints. Finally,simulation results show the effectiveness of the proposed scheme in terms of total sum rate and power efficiency of TRs' backhaul.

Cross-Modal Entity Resolution for Image and Text Integrating Global and Fine-Grained Joint Attention Mechanism

In order to solve the problem that the existing cross-modal entity resolution methods easily ignore the high-level semantic informational correlations between cross-modal data,we propose a novel cross-modal entity resolution for image and text integrating global and fine-grained joint attention mechanism method.First,we map the cross-modal data to a common embedding space utilizing a feature extraction network.Then,we integrate global joint attention mechanism and fine-grained joint attention mechanism,making the model have the ability to learn the global semantic characteristics and the local fine-grained semantic characteristics of the cross-modal data,which is used to fully exploit the cross-modal semantic correlation and boost the performance of cross-modal entity resolution.Moreover,experiments on Flickr-30K and MS-COCO datasets show that the overall performance of R@sum outperforms by 4.30%and 4.54%compared with 5 state-of-the-art methods,respectively,which can fully demonstrate the superiority of our proposed method.

无线通信“N+1维”内生抗干扰理论与技术

The existing theory and techniques of wireless communication anti-jamming have reached their performance limit recently.With this focus,by leveraging the inherent characteristics of wireless communication and referring to the principle of cyberspace endogenous security,this paper investigates the core issues of endogenous security in the electromagnetic space,namely,endogenous anti-jamming(EAJ),which can defend against unknown electromagnetic attacks effectively.Specifically,the subspace method is first adopted to establish the unified framework for the conventional spread-spectrum,intelligent,and endogenous antijamming,in which both the intrinsic development law of each technique and the internal logic between them are revealed.Then,the fundamental concept,key techniques,and development suggestions of wireless communication“N+1 dimensionality”endogenous anti-jamming are proposed to seek a disruptive breakthrough.

A Cumulative Prospect Theory Based Counterterrorism Resource Allocation Method under Interval Values

Strategic resource allocation into decision-making model plays a valuable role for the defender in mitigating damage and improving efficiency in military environments.In this paper,we develop a defensive resource allocation model based on cumulative prospect theory (CPT),which considers terrorists' psychological factors of decision-making in reality.More specifically,we extend existing models in the presence of multiple attributes and terrorists' deviations from rationality using a multi-attribute cumulative prospect theory.In addition,interval values are used to cope with uncertainties regarding gain and loss.Comparative studies are also carried out to demonstrate the differences among minmax,Nash equilibrium (NE),and traditional probability risk analysis (PRA) strategies.Results show that the defender's optimal defensive resource allocation will change along with terrorists' behaviors and the proposed model makes more sense compared with other traditional resource allocation strategies.