Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is con...Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.展开更多
To guarantee secure communication against eavesdropping and malicious attack,an artificial noise(AN)-aided frequency-hopping(FH)architecture is adopted in this article.But the inevitable time misalignment between the ...To guarantee secure communication against eavesdropping and malicious attack,an artificial noise(AN)-aided frequency-hopping(FH)architecture is adopted in this article.But the inevitable time misalignment between the received signal and locally reconstructed AN will deteriorate the AN cancellation performance,yielding significant secrecy degradation at the FH receiver.In view of this,first,the AN cancellation performance under time misalignment is evaluated via signal to AN-plus-noise ratio,and the system secrecy is analyzed via secrecy rate.Then,to mitigate the performance degradation raised by time misalignment,the transmitting power allocation scheme for AN and confidential signal(CS)is optimized,and the optimal hopping period is designed.Notably,the obtained conclusions in both the performance evaluation and transmitter optimization sections hold no matter whether the eavesdropper can realize FH synchronization or not.Simulations verify that time misalignment will raise non-negligible performance degradation.Besides,the power ratio of AN to CS should decrease as time misalignment increases,and for perfect time synchronization,the transmitting power of AN and CS should be equivalent.In addition,a longer hopping period is preferred for secrecy enhancement when time misalignment gets exacerbated.展开更多
This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynami...This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed.Then,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks.It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time.In addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter.Furthermore,the results are extended to regional consensus of the MASs with the bounded control protocol.Numerical simulations show the effectiveness of the proposed approach.展开更多
To enable simultaneous transmit and receive(STAR)on the same frequency in a densely deployed space with multi-interference sources,this work proposes a digitally-assisted analog selfinterference cancellation method,wh...To enable simultaneous transmit and receive(STAR)on the same frequency in a densely deployed space with multi-interference sources,this work proposes a digitally-assisted analog selfinterference cancellation method,which can acquire reference signals through flexible wired/wireless switching access.Based on this method,the Minimum Mean Square Error algorithm with known channel state information is derived in detail,determining the upper limit of the cancellation performance,and the Adaptive Dithered Linear Search algorithm for real-time engineering cancellation is given.The correctness of theoretical analysis is verified by the practical self-interference channel measured by a vector network analyzer.Furthermore,we have designed and implemented the corresponding multiinterference cancellation prototype with the digitallyassisted structure,capable of handling multiple interferences(up to three)and supporting a large receive bandwidth of 100 MHz as well as a wide frequency coverage from 30 MHz to 3000 MHz.Prototype test results demonstrate that in the presence of three interferences,when the single interference bandwidth is 0.2/2/20 MHz(corresponding to the receive bandwidth of 2/20/100 MHz),the cancellation performance can reach 46/32/22 dB or more.展开更多
In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sl...In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sliding mode differentiator, the inherent computational complexity problem within the predefined-time backstepping framework is solved. Different from the existing command filter-based finite-time and fixed-time control strategies that the convergence time of the filtering error is adjusted through the system initial value or numerous parameters, a novel command filtering error compensation method is presented,which tunes one control parameter to make the filtering error converge in the predefined time, thereby reducing the complexity of design and analysis of processing the filtering error. Then, an improved event-triggered mechanism(ETM) that builds upon the switching threshold strategy, in which an inverse cotangent function is designed to replace the residual term of the ETM,is proposed to gradually release the controller's dependence on the residual term with increasing time. Furthermore, a tan-type nonlinear mapping technique is applied to tackle the time-varying full-state constraints problem. By the predefined-time stability theory, all signals in the uncertain nonlinear systems exhibit predefined-time stability. Finally, the feasibility of the proposed algorithm is substantiated through two simulation results.展开更多
antagonistic interactions,data security and transient-steady state performance of the system are two key problems.To ensure data security,an intermittent privacy preservation(IPP)mechanism is proposed for the first ti...antagonistic interactions,data security and transient-steady state performance of the system are two key problems.To ensure data security,an intermittent privacy preservation(IPP)mechanism is proposed for the first time.A novel setting time initial mask function and a novel intermittent mask function are constructed.Users can implement intermittent preservation for the system according to actual requirements,which solves the irreversibility problem after conventional mask disappears and balances control accuracy and system security.To ensure transient-steady state performance,a novel error transformation function(ETF)is proposed and integrated into the predefined-time prescribed performance control strategy.Compared to conventional hyperbolic tangent type ETFs,the proposed ETF can improve the convergence accuracy of errors under the same conditions.Furthermore,a unified model of the air-sea HMASs is established,which improves the model accuracy compared with the simplified model.Finally,the proposed IPP security control strategy is applied to the air-sea delivery mission to verify its feasibility and effectiveness.展开更多
Radio frequency(RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence,which has become indispensable for drone surveillance systems.However,since ...Radio frequency(RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence,which has become indispensable for drone surveillance systems.However,since drones operate in unlicensed frequency bands,a large number of co-frequency devices exist in these bands,which brings a great challenge to traditional signal identification methods.Deep learning techniques provide a new approach to complete endto-end signal identification by directly learning the distribution of RF data.In such scenarios,due to the complexity and high dynamics of the electromagnetic environments,a massive amount of data that can reflect the various propagation conditions of drone signals is necessary for a robust neural network(NN)for identifying drones.In reality,signal acquisition and labeling that meet the above requirements are too costly to implement.Therefore,we propose a virtual electromagnetic environment modeling based data augmentation(DA)method to improve the diversity of drone signal data.The DA method focuses on simulating the spectrograms of drone signals transmitted in real-world environments and randomly generates extra training data in each training epoch.Furthermore,considering the limited processing capability of RF receivers,we modify the original YOLOv5s model to a more lightweight version.Without losing the identification performance,more hardware-friendly designs are applied and the number of parameters decreases about 10-fold.For performance evaluation,we utilized a universal software radio peripheral(USRP)X310 platform to collect RF signals of four drones in an anechoic chamber and a practical wireless scenario.Experiment results reveal that the NN trained with augmented data performs as well as that trained with practical data in the complex electromagnetic environment.展开更多
基金supported in part by the Natural Science Foundation of China (62171110,U19B2028 and U20B2070)。
文摘Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.
基金supported in part by the National Natural Science Foundation of China under Grant 62071094in part by the National Key Laboratory of Wireless Communications Foundation under Grant IFN202402in part by the Postdoctoral Fellowship Program(Grade C)of China Postdoctoral Science Foundation under Grant GZC20240217.
文摘To guarantee secure communication against eavesdropping and malicious attack,an artificial noise(AN)-aided frequency-hopping(FH)architecture is adopted in this article.But the inevitable time misalignment between the received signal and locally reconstructed AN will deteriorate the AN cancellation performance,yielding significant secrecy degradation at the FH receiver.In view of this,first,the AN cancellation performance under time misalignment is evaluated via signal to AN-plus-noise ratio,and the system secrecy is analyzed via secrecy rate.Then,to mitigate the performance degradation raised by time misalignment,the transmitting power allocation scheme for AN and confidential signal(CS)is optimized,and the optimal hopping period is designed.Notably,the obtained conclusions in both the performance evaluation and transmitter optimization sections hold no matter whether the eavesdropper can realize FH synchronization or not.Simulations verify that time misalignment will raise non-negligible performance degradation.Besides,the power ratio of AN to CS should decrease as time misalignment increases,and for perfect time synchronization,the transmitting power of AN and CS should be equivalent.In addition,a longer hopping period is preferred for secrecy enhancement when time misalignment gets exacerbated.
基金supported in part by the National Natural Science Foundation of China(51939001,61976033,62273072)the Natural Science Foundation of Sichuan Province (2022NSFSC0903)。
文摘This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed.Then,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks.It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time.In addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter.Furthermore,the results are extended to regional consensus of the MASs with the bounded control protocol.Numerical simulations show the effectiveness of the proposed approach.
基金supported in part by the National Natural Science Foundation of China under Grant 62071094in part by the National Key Laboratory of Wireless Communications Foundation under Grant IFN202402in part by the Postdoctoral Fellowship Program(Grade C)of China Postdoctoral Science Foundation under Grant GZC20240217.
文摘To enable simultaneous transmit and receive(STAR)on the same frequency in a densely deployed space with multi-interference sources,this work proposes a digitally-assisted analog selfinterference cancellation method,which can acquire reference signals through flexible wired/wireless switching access.Based on this method,the Minimum Mean Square Error algorithm with known channel state information is derived in detail,determining the upper limit of the cancellation performance,and the Adaptive Dithered Linear Search algorithm for real-time engineering cancellation is given.The correctness of theoretical analysis is verified by the practical self-interference channel measured by a vector network analyzer.Furthermore,we have designed and implemented the corresponding multiinterference cancellation prototype with the digitallyassisted structure,capable of handling multiple interferences(up to three)and supporting a large receive bandwidth of 100 MHz as well as a wide frequency coverage from 30 MHz to 3000 MHz.Prototype test results demonstrate that in the presence of three interferences,when the single interference bandwidth is 0.2/2/20 MHz(corresponding to the receive bandwidth of 2/20/100 MHz),the cancellation performance can reach 46/32/22 dB or more.
基金supported by the Revitalization of Liaoning Talents Program(Grant No.XLYC2203201)。
文摘In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sliding mode differentiator, the inherent computational complexity problem within the predefined-time backstepping framework is solved. Different from the existing command filter-based finite-time and fixed-time control strategies that the convergence time of the filtering error is adjusted through the system initial value or numerous parameters, a novel command filtering error compensation method is presented,which tunes one control parameter to make the filtering error converge in the predefined time, thereby reducing the complexity of design and analysis of processing the filtering error. Then, an improved event-triggered mechanism(ETM) that builds upon the switching threshold strategy, in which an inverse cotangent function is designed to replace the residual term of the ETM,is proposed to gradually release the controller's dependence on the residual term with increasing time. Furthermore, a tan-type nonlinear mapping technique is applied to tackle the time-varying full-state constraints problem. By the predefined-time stability theory, all signals in the uncertain nonlinear systems exhibit predefined-time stability. Finally, the feasibility of the proposed algorithm is substantiated through two simulation results.
基金partially supported by the National Natural Science Foundation of China(Grant No.62322307)the Sichuan Science and Technology Program(Grant No.2023NSFSC1968)+1 种基金the Basic Research Project of the Educational Department of Liaoning Province(Grant No.LJ232410167028)the Revitalization of Liaoning Talents Program(Grant No.XLYC2203201)。
文摘antagonistic interactions,data security and transient-steady state performance of the system are two key problems.To ensure data security,an intermittent privacy preservation(IPP)mechanism is proposed for the first time.A novel setting time initial mask function and a novel intermittent mask function are constructed.Users can implement intermittent preservation for the system according to actual requirements,which solves the irreversibility problem after conventional mask disappears and balances control accuracy and system security.To ensure transient-steady state performance,a novel error transformation function(ETF)is proposed and integrated into the predefined-time prescribed performance control strategy.Compared to conventional hyperbolic tangent type ETFs,the proposed ETF can improve the convergence accuracy of errors under the same conditions.Furthermore,a unified model of the air-sea HMASs is established,which improves the model accuracy compared with the simplified model.Finally,the proposed IPP security control strategy is applied to the air-sea delivery mission to verify its feasibility and effectiveness.
基金supported in part by the Guangzhou Basic and Applied Basic Research Foundation(2023A04J1740)in part by the Shaanxi Provincial Key Research and Development Program(2023-ZDLGY-33,2022ZDLGY05-03,2022ZDLGY05-04)in part by the Fundamental Research Funds for the Central Universities(XJS220116).
文摘Radio frequency(RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence,which has become indispensable for drone surveillance systems.However,since drones operate in unlicensed frequency bands,a large number of co-frequency devices exist in these bands,which brings a great challenge to traditional signal identification methods.Deep learning techniques provide a new approach to complete endto-end signal identification by directly learning the distribution of RF data.In such scenarios,due to the complexity and high dynamics of the electromagnetic environments,a massive amount of data that can reflect the various propagation conditions of drone signals is necessary for a robust neural network(NN)for identifying drones.In reality,signal acquisition and labeling that meet the above requirements are too costly to implement.Therefore,we propose a virtual electromagnetic environment modeling based data augmentation(DA)method to improve the diversity of drone signal data.The DA method focuses on simulating the spectrograms of drone signals transmitted in real-world environments and randomly generates extra training data in each training epoch.Furthermore,considering the limited processing capability of RF receivers,we modify the original YOLOv5s model to a more lightweight version.Without losing the identification performance,more hardware-friendly designs are applied and the number of parameters decreases about 10-fold.For performance evaluation,we utilized a universal software radio peripheral(USRP)X310 platform to collect RF signals of four drones in an anechoic chamber and a practical wireless scenario.Experiment results reveal that the NN trained with augmented data performs as well as that trained with practical data in the complex electromagnetic environment.
