Determining the minimal distance between the target state and the convex combination of given states is a fundamental problem in quantum resource theory,offering critical guidance for experimental implementations.In t...Determining the minimal distance between the target state and the convex combination of given states is a fundamental problem in quantum resource theory,offering critical guidance for experimental implementations.In this paper,we embark on an in-depth exploration of the use of a quantum state prepared by the convex combination of given qubit states to optimally approximate the l_(1)-norm of coherence of the target quantum state,striving to make the prepared state and the target state as similar as possible.Here,we present the analytical solution for the optimal distance for any N given quantum states.We find that the optimal approximation problem for any N>4 quantum states can be transformed into an optimal approximation problem for no more than four quantum states,which not only significantly streamlines the problem but also proves advantageous for laboratories in terms of material conservation.Ultimately,a one-to-one comparison between the analytical and numerical solutions verifies the effectiveness of our approach.展开更多
This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying delays.By i...This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying delays.By introducing two adjustable parameters and two free variables,a novel convex function greater than or equal to the quadratic function is constructed,regardless of the sign of the coefficient in the quadratic term.The developed lemma can also be degenerated into the existing quadratic function negative-determination(QFND)lemma and relaxed QFND lemma respectively,by setting two adjustable parameters and two free variables as some particular values.Moreover,for a linear system with time-varying delays,a relaxed stability criterion is established via our developed lemma,together with the quivalent reciprocal combination technique and the Bessel-Legendre inequality.As a result,the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay systems.Finally,the superiority of our results is illustrated through three numerical examples.展开更多
Results regarding best approximation and best Simultaneous approximation on convex metric spaces are Obtained.Existence of fixed points for an ultimately nonexpansive semigroup of mappings is also shown.
In this note we obtain generalization of well known results of carbone and Conti,Sehgal and Singh and Tanimoto concerning the existence of best approximation and simultaneous best approximation of continuous Junctions...In this note we obtain generalization of well known results of carbone and Conti,Sehgal and Singh and Tanimoto concerning the existence of best approximation and simultaneous best approximation of continuous Junctions from the set up of a normed space to the case of a Hausdorff locally convex space.展开更多
In this paper, we study the characterization of f-Chebyshev radius and f-Chebyshev centers and the existence of f-Chebyshev centers in locally convex spaces.
In this paper,an Unmanned Aerial Vehicle(UAV)-assisted relay communication system is studied,where a UAV is served as a flying relay to maintain a communication link between a mobile source node and a remote destinati...In this paper,an Unmanned Aerial Vehicle(UAV)-assisted relay communication system is studied,where a UAV is served as a flying relay to maintain a communication link between a mobile source node and a remote destination node.Specifically,an average outage probability minimization problem is formulated firstly,with the constraints on the transmission power of the source node,the maximum energy consumption budget,the transmission power,the speed and acceleration of the flying UAV relay.Next,the closed-form of outage probability is derived,under the hybrid line-of-sight and non-line-of-sight probability channel model.To deal with the formulated nonconvex optimization,a long-term proactive optimization mechanism is developed.In particular,firstly,an approximation for line-of-sight probability and a reformulation of the primal problem are given,respectively.Then,the reformulated problem is transformed into two subproblems:one is the transmission power optimization with given UAV’s trajectory and the other is the trajectory optimization with given transmission power allocation.Next,two subproblems are tackled via tailoring primal–dual subgradient method and successive convex approximation,respectively.Furthermore,a proactive optimization algorithm is proposed to jointly optimize the transmission power allocation and the three-dimensional trajectory.Finally,simulation results demonstrate the performance of the proposed algorithm under various parameter configurations.展开更多
In this paper, we investigate the downlink performance of cell-free massive multi-input multi-output non-orthogonal multiple access(CF-m MIMO-NOMA) system with conjugate beamforming precoder and compare against the or...In this paper, we investigate the downlink performance of cell-free massive multi-input multi-output non-orthogonal multiple access(CF-m MIMO-NOMA) system with conjugate beamforming precoder and compare against the orthogonal multiple access(OMA) counterpart. A novel achievable closed-form spectral efficiency(SE) expression is derived, which characterizes the effects of the channel estimation error, pilot contamination, imperfect successive interference cancellation(SIC) operation, and power optimization technique. Then, motivated by the closedform result, a sum-SE maximization algorithm with the sequential convex approximation(SCA) is proposed, subject to each AP power constraint and SIC power constraint. Numerical experiments indicate that the proposed sum-SE maximization algorithms have a fast converge rate, within about five iterations. In addition, compared with the full power control(FPC) scheme, our algorithms can significantly improve the achievable sum-SE. Moreover, NOMA outperforms OMA in many respects in the presence of the proposed algorithms.展开更多
This paper establishes a new layered flying ad hoc networks(FANETs) system of mobile edge computing(MEC) supported by multiple UAVs,where the first layer of user UAVs can perform tasks such as area coverage, and the s...This paper establishes a new layered flying ad hoc networks(FANETs) system of mobile edge computing(MEC) supported by multiple UAVs,where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members(CMs) within a cluster to its cluster head(CH). Then, we need to determine whether each CH’ tasks are executed locally or offloaded to one of the MEC UAVs for remote execution(i.e., task scheduling), and how much resources should be allocated to each CH(i.e., resource allocation), as well as the trajectories of all MEC UAVs.We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them,respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.展开更多
In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellu...In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.展开更多
The joint resource block(RB)allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle(V2V)links in the device-to-device(D2D)-enabled V2V communication system,where one fea...The joint resource block(RB)allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle(V2V)links in the device-to-device(D2D)-enabled V2V communication system,where one feasible cellular user(FCU)can share its RB with multiple V2V pairs.The problem is first formulated as a nonconvex mixed-integer nonlinear programming(MINLP)problem with constraint of the maximum interference power in the FCU links.Using the game theory,two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection,where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation.The successive convex approximation(SCA)is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links.Finally,numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.展开更多
As key technologies in 6G,Space-Air-Ground Integrated Networks(SAGIN)promises to provide seamless global coverage through a comprehensive,ubiquitous communication system,while Integrated Sensing and Communications(ISA...As key technologies in 6G,Space-Air-Ground Integrated Networks(SAGIN)promises to provide seamless global coverage through a comprehensive,ubiquitous communication system,while Integrated Sensing and Communications(ISAC)effectively addresses spectrum congestion by sharing spectrum resources and transceivers for simultaneous communication and sensing operations.However,existing ISAC research has primarily focused on terrestrial networks,with limited exploration of its applications in SAGIN environments.This paper proposes a novel SAGIN-ISAC scheme leveraging High-Altitude Platform Stations(HAPS).In this scheme,HAPS serves as a relay node that not only amplifies and forwards communication signals but also receives and processes target echo signals for parameter estimation.The satellite employs Resilient Massive Access(RMA)to provide communication services to different User Terminals(UTs).To address scenarios with an unknown number of targets,we develop a Two-threshold Detection and Parameter Multiple Signal Classification(MUSIC)algorithm(TDPM),which employs dual-threshold correlation detection to determine the number of targets and utilizes the MUSIC algorithm to estimate targets’Angle of Arrival(AoA),range,and relative velocity.Furthermore,we establish a joint optimization framework that considers both communication and sensing performance,optimizing energy efficiency,detection probability,and the Cramér-Rao bound.The power allocation coefficients are derived through Nash equilibrium,while the precoding matrix is optimized using Sequential Convex Approximation(SCA)to address the non-convex nature of the optimization problem.Experimental results demonstrate that our proposed scheme significantly enhances the overall performance of the SAGIN-ISAC system.展开更多
This paper explores a UAV-mounted active Reconfigurable Intelligent Surface(aRIS)network designed to enhance secure downlink communication for multiple users while mitigating the impact of multiple Eavesdroppers(EVs)....This paper explores a UAV-mounted active Reconfigurable Intelligent Surface(aRIS)network designed to enhance secure downlink communication for multiple users while mitigating the impact of multiple Eavesdroppers(EVs).The focus is on optimizing the UAV’s trajectory,the Base Station’s(BS)transmit beamforming,and the power-Amplified Programmable Reflecting Elements(APREs)of the aRIS to maximize the minimum secrecy rate in the presence of EVs.This is a complex non-convex problem due to multiple optimization variables,high-dimensional matrix operations,and log-determinant objective functions,which makes it challenging to solve.Hence,a Successive Convex Approximation(SCA)-based optimization strategy is developed to efficiently solve the subproblems related to the UAV’s trajectory,aRIS’s APREs,and BS’s beamforming.By leveraging slack variables and approximation techniques,we solve the nonconvex subproblems by a sequence of convex subproblems.Simulation results demonstrate that the proposed UAV-aRIS network significantly outperforms its passive RIS counterpart in improving communication security,highlighting the effectiveness of the optimization strategy.展开更多
We consider the problem of minimizing the average of a large number of smooth component functions over one smooth inequality constraint.We propose and analyze a stochastic Moving Balls Approximation(SMBA)method.Like s...We consider the problem of minimizing the average of a large number of smooth component functions over one smooth inequality constraint.We propose and analyze a stochastic Moving Balls Approximation(SMBA)method.Like stochastic gradient(SG)met hods,the SMBA method's iteration cost is independent of the number of component functions and by exploiting the smoothness of the constraint function,our method can be easily implemented.Theoretical and computational properties of SMBA are studied,and convergence results are established.Numerical experiments indicate that our algorithm dramatically outperforms the existing Moving Balls Approximation algorithm(MBA)for the structure of our problem.展开更多
In this paper,an unmanned aerial vehicle(UAV)-aided wireless emergence communication system is studied,where a UAV is deployed to support ground user equipments(UEs)for emergence communications.We aim to maximize the ...In this paper,an unmanned aerial vehicle(UAV)-aided wireless emergence communication system is studied,where a UAV is deployed to support ground user equipments(UEs)for emergence communications.We aim to maximize the number of the UEs served,the fairness,and the overall uplink data rate via optimizing the trajectory of UAV and the transmission power of UEs.We propose a deep Q-network(DQN)based algorithm,which involves the well-known deep neural network(DNN)and Q-learning,to solve the UAV trajectory prob-lem.Then,based on the optimized UAV trajectory,we further propose a successive convex approximation(SCA)based algorithm to tackle the power control problem for each UE.Numerical simulations demonstrate that the proposed DQN based algorithm can achieve considerable performance gain over the existing benchmark algorithms in terms of fairness,the number of UEs served and overall uplink data rate via optimizing UAV’s trajectory and power optimization.展开更多
The use of a reconfigurable intelligent surface(RIS)in the enhancement of the rate performance is considered to involve the limitation of the RIS being a passive reflector.To address this issue,we propose a RIS-aided ...The use of a reconfigurable intelligent surface(RIS)in the enhancement of the rate performance is considered to involve the limitation of the RIS being a passive reflector.To address this issue,we propose a RIS-aided amplify-and-forward(AF)relay network in this paper.By jointly optimizing the beamforming matrix at AF relay and the phase-shift matrices at RIS,two schemes are put forward to address a maximizing signal-to-noise ratio(SNR)problem.First,aiming at achieving a high rate,a high-performance alternating optimization(AO)method based on Charnes–Cooper transformation and semidefinite programming(CCT-SDP)is proposed,where the optimization problem is decomposed into three subproblems solved using CCT-SDP,and rank-one solutions can be recovered using Gaussian randomization.However,the optimization variables in the CCT-SDP method are matrices,leading to extremely high complexity.To reduce the complexity,a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation(DT-SCA)is proposed,where the variables are represented in vector form,and the three decoupling subproblems are solved using DT-SCA.Simulation results verify that compared to three benchmarks(i.e.,a RIS-assisted AF relay network with random phase,an AF relay network without RIS,and a RIS-aided network without AF relay),the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance.Furthermore,it is revealed that the rate of the low-complexity DT-SCA method is close to that of the CCT-SDP method.展开更多
基金supported by the Fundamental Research Projects of Shanxi Province(Grant No.202203021222225)the National Natural Science Foundation of China(Grant Nos.12175029,12011530014,and 11775040)the Key Research and Development Project of Liaoning Province(Grant No.2020JH2/10500003).
文摘Determining the minimal distance between the target state and the convex combination of given states is a fundamental problem in quantum resource theory,offering critical guidance for experimental implementations.In this paper,we embark on an in-depth exploration of the use of a quantum state prepared by the convex combination of given qubit states to optimally approximate the l_(1)-norm of coherence of the target quantum state,striving to make the prepared state and the target state as similar as possible.Here,we present the analytical solution for the optimal distance for any N given quantum states.We find that the optimal approximation problem for any N>4 quantum states can be transformed into an optimal approximation problem for no more than four quantum states,which not only significantly streamlines the problem but also proves advantageous for laboratories in terms of material conservation.Ultimately,a one-to-one comparison between the analytical and numerical solutions verifies the effectiveness of our approach.
基金the National Natural Science Foundation of China(62273058,U22A2045)the Key Science and Technology Projects of Jilin Province(20200401075GX)the Youth Science and Technology Innovation and Entrepreneurship Outstanding Talents Project of Jilin Province(20230508043RC)。
文摘This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying delays.By introducing two adjustable parameters and two free variables,a novel convex function greater than or equal to the quadratic function is constructed,regardless of the sign of the coefficient in the quadratic term.The developed lemma can also be degenerated into the existing quadratic function negative-determination(QFND)lemma and relaxed QFND lemma respectively,by setting two adjustable parameters and two free variables as some particular values.Moreover,for a linear system with time-varying delays,a relaxed stability criterion is established via our developed lemma,together with the quivalent reciprocal combination technique and the Bessel-Legendre inequality.As a result,the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay systems.Finally,the superiority of our results is illustrated through three numerical examples.
文摘Results regarding best approximation and best Simultaneous approximation on convex metric spaces are Obtained.Existence of fixed points for an ultimately nonexpansive semigroup of mappings is also shown.
文摘In this note we obtain generalization of well known results of carbone and Conti,Sehgal and Singh and Tanimoto concerning the existence of best approximation and simultaneous best approximation of continuous Junctions from the set up of a normed space to the case of a Hausdorff locally convex space.
基金Research supported by the National Science Foundation of P.R.China
文摘In this paper, we study the characterization of f-Chebyshev radius and f-Chebyshev centers and the existence of f-Chebyshev centers in locally convex spaces.
基金co-supported by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20190030)the National Natural Science Foundation of China(Nos.61871398 and 61931011)the National Key R&D Program of China(No.2018YFB1801103)。
文摘In this paper,an Unmanned Aerial Vehicle(UAV)-assisted relay communication system is studied,where a UAV is served as a flying relay to maintain a communication link between a mobile source node and a remote destination node.Specifically,an average outage probability minimization problem is formulated firstly,with the constraints on the transmission power of the source node,the maximum energy consumption budget,the transmission power,the speed and acceleration of the flying UAV relay.Next,the closed-form of outage probability is derived,under the hybrid line-of-sight and non-line-of-sight probability channel model.To deal with the formulated nonconvex optimization,a long-term proactive optimization mechanism is developed.In particular,firstly,an approximation for line-of-sight probability and a reformulation of the primal problem are given,respectively.Then,the reformulated problem is transformed into two subproblems:one is the transmission power optimization with given UAV’s trajectory and the other is the trajectory optimization with given transmission power allocation.Next,two subproblems are tackled via tailoring primal–dual subgradient method and successive convex approximation,respectively.Furthermore,a proactive optimization algorithm is proposed to jointly optimize the transmission power allocation and the three-dimensional trajectory.Finally,simulation results demonstrate the performance of the proposed algorithm under various parameter configurations.
基金supported in part by the National Key Research and Development Program of China under Grant 2018YFC1314903the National Natural Science Foundation of China under Grants 61861039,61372124,and 61427801+1 种基金the Science and Technology Project Foundation of Gansu Province under Grant 18YF1GA060the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant SJKY19_0740 and KYCX20_0709。
文摘In this paper, we investigate the downlink performance of cell-free massive multi-input multi-output non-orthogonal multiple access(CF-m MIMO-NOMA) system with conjugate beamforming precoder and compare against the orthogonal multiple access(OMA) counterpart. A novel achievable closed-form spectral efficiency(SE) expression is derived, which characterizes the effects of the channel estimation error, pilot contamination, imperfect successive interference cancellation(SIC) operation, and power optimization technique. Then, motivated by the closedform result, a sum-SE maximization algorithm with the sequential convex approximation(SCA) is proposed, subject to each AP power constraint and SIC power constraint. Numerical experiments indicate that the proposed sum-SE maximization algorithms have a fast converge rate, within about five iterations. In addition, compared with the full power control(FPC) scheme, our algorithms can significantly improve the achievable sum-SE. Moreover, NOMA outperforms OMA in many respects in the presence of the proposed algorithms.
基金supported in part by the National Natural Science Foundation of China under Grant No.61931011in part by the Primary Research & Developement Plan of Jiangsu Province No. BE2021013-4+2 种基金in part by the National Natural Science Foundation of China under Grant No. 62072303in part by the National Postdoctoral Program for Innovative Talents of China No. BX20190202in part by the Open Project Program of the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space No. KF20202105。
文摘This paper establishes a new layered flying ad hoc networks(FANETs) system of mobile edge computing(MEC) supported by multiple UAVs,where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members(CMs) within a cluster to its cluster head(CH). Then, we need to determine whether each CH’ tasks are executed locally or offloaded to one of the MEC UAVs for remote execution(i.e., task scheduling), and how much resources should be allocated to each CH(i.e., resource allocation), as well as the trajectories of all MEC UAVs.We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them,respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.
基金supported by National Natural Science Foundation of China (No.61501028)Beijing Institute of Technology Research Fund Program for Young Scholars
文摘In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.
基金the National Natural Scientific Foundation of China(61771291,61571272)the Major Science and Technological Innovation Project of Shandong Province(2020CXGC010109).
文摘The joint resource block(RB)allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle(V2V)links in the device-to-device(D2D)-enabled V2V communication system,where one feasible cellular user(FCU)can share its RB with multiple V2V pairs.The problem is first formulated as a nonconvex mixed-integer nonlinear programming(MINLP)problem with constraint of the maximum interference power in the FCU links.Using the game theory,two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection,where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation.The successive convex approximation(SCA)is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links.Finally,numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.
基金supported in part by the Natural Science Foundation of Chongqing under Grant CSTB2023NSCQ-LZX0118in part by the National Natural Science Foundation of China under Grant 62471052in part by the Beijing University of Posts and Telecommunications(BUPT)Excellent Ph.D.Students Foundation under Grant CX2023139.
文摘As key technologies in 6G,Space-Air-Ground Integrated Networks(SAGIN)promises to provide seamless global coverage through a comprehensive,ubiquitous communication system,while Integrated Sensing and Communications(ISAC)effectively addresses spectrum congestion by sharing spectrum resources and transceivers for simultaneous communication and sensing operations.However,existing ISAC research has primarily focused on terrestrial networks,with limited exploration of its applications in SAGIN environments.This paper proposes a novel SAGIN-ISAC scheme leveraging High-Altitude Platform Stations(HAPS).In this scheme,HAPS serves as a relay node that not only amplifies and forwards communication signals but also receives and processes target echo signals for parameter estimation.The satellite employs Resilient Massive Access(RMA)to provide communication services to different User Terminals(UTs).To address scenarios with an unknown number of targets,we develop a Two-threshold Detection and Parameter Multiple Signal Classification(MUSIC)algorithm(TDPM),which employs dual-threshold correlation detection to determine the number of targets and utilizes the MUSIC algorithm to estimate targets’Angle of Arrival(AoA),range,and relative velocity.Furthermore,we establish a joint optimization framework that considers both communication and sensing performance,optimizing energy efficiency,detection probability,and the Cramér-Rao bound.The power allocation coefficients are derived through Nash equilibrium,while the precoding matrix is optimized using Sequential Convex Approximation(SCA)to address the non-convex nature of the optimization problem.Experimental results demonstrate that our proposed scheme significantly enhances the overall performance of the SAGIN-ISAC system.
基金co-supported by Technology Key Project of Guangdong Province,China(No.HZJBGS-2021001)the Shanghai Sailing Scholar,China(No.23YF1412700)+1 种基金the National Natural Science Foundation of China(No.61901254)the Shanghai Technical Service Computing Center of Science and Engineering,Shanghai University,China.
文摘This paper explores a UAV-mounted active Reconfigurable Intelligent Surface(aRIS)network designed to enhance secure downlink communication for multiple users while mitigating the impact of multiple Eavesdroppers(EVs).The focus is on optimizing the UAV’s trajectory,the Base Station’s(BS)transmit beamforming,and the power-Amplified Programmable Reflecting Elements(APREs)of the aRIS to maximize the minimum secrecy rate in the presence of EVs.This is a complex non-convex problem due to multiple optimization variables,high-dimensional matrix operations,and log-determinant objective functions,which makes it challenging to solve.Hence,a Successive Convex Approximation(SCA)-based optimization strategy is developed to efficiently solve the subproblems related to the UAV’s trajectory,aRIS’s APREs,and BS’s beamforming.By leveraging slack variables and approximation techniques,we solve the nonconvex subproblems by a sequence of convex subproblems.Simulation results demonstrate that the proposed UAV-aRIS network significantly outperforms its passive RIS counterpart in improving communication security,highlighting the effectiveness of the optimization strategy.
文摘We consider the problem of minimizing the average of a large number of smooth component functions over one smooth inequality constraint.We propose and analyze a stochastic Moving Balls Approximation(SMBA)method.Like stochastic gradient(SG)met hods,the SMBA method's iteration cost is independent of the number of component functions and by exploiting the smoothness of the constraint function,our method can be easily implemented.Theoretical and computational properties of SMBA are studied,and convergence results are established.Numerical experiments indicate that our algorithm dramatically outperforms the existing Moving Balls Approximation algorithm(MBA)for the structure of our problem.
基金The associate editor coordinating the review of this paper and approving it for publication was J.Zhang.
文摘In this paper,an unmanned aerial vehicle(UAV)-aided wireless emergence communication system is studied,where a UAV is deployed to support ground user equipments(UEs)for emergence communications.We aim to maximize the number of the UEs served,the fairness,and the overall uplink data rate via optimizing the trajectory of UAV and the transmission power of UEs.We propose a deep Q-network(DQN)based algorithm,which involves the well-known deep neural network(DNN)and Q-learning,to solve the UAV trajectory prob-lem.Then,based on the optimized UAV trajectory,we further propose a successive convex approximation(SCA)based algorithm to tackle the power control problem for each UE.Numerical simulations demonstrate that the proposed DQN based algorithm can achieve considerable performance gain over the existing benchmark algorithms in terms of fairness,the number of UEs served and overall uplink data rate via optimizing UAV’s trajectory and power optimization.
基金Project supported by the National Natural Science Foundation of China(Nos.U22A2002,62071234)the Hainan Province Science and Technology Special Fund,China(No.ZDKJ2021022)the Scientific Research Fund Project of Hainan University,China(No.KYQD(ZR)-21008)。
文摘The use of a reconfigurable intelligent surface(RIS)in the enhancement of the rate performance is considered to involve the limitation of the RIS being a passive reflector.To address this issue,we propose a RIS-aided amplify-and-forward(AF)relay network in this paper.By jointly optimizing the beamforming matrix at AF relay and the phase-shift matrices at RIS,two schemes are put forward to address a maximizing signal-to-noise ratio(SNR)problem.First,aiming at achieving a high rate,a high-performance alternating optimization(AO)method based on Charnes–Cooper transformation and semidefinite programming(CCT-SDP)is proposed,where the optimization problem is decomposed into three subproblems solved using CCT-SDP,and rank-one solutions can be recovered using Gaussian randomization.However,the optimization variables in the CCT-SDP method are matrices,leading to extremely high complexity.To reduce the complexity,a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation(DT-SCA)is proposed,where the variables are represented in vector form,and the three decoupling subproblems are solved using DT-SCA.Simulation results verify that compared to three benchmarks(i.e.,a RIS-assisted AF relay network with random phase,an AF relay network without RIS,and a RIS-aided network without AF relay),the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance.Furthermore,it is revealed that the rate of the low-complexity DT-SCA method is close to that of the CCT-SDP method.
