Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allo...In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allowing user equipment(UE)of terrestrial networks to share frequencies with satellite networks.In order to protect the satellite terminal(ST),the base station(BS)needs to control the transmit power and frequency resources of the UE.The optimization problem involves maximizing the achievable throughput while satisfying the interference protection constraints of the ST and the quality of service(QoS)of the UE.However,this problem is highly nonconvex,and we decompose it into power allocation and frequency resource scheduling subproblems.In the power allocation subproblem,we propose a power allocation algorithm based on interference probability(PAIP)to address channel uncertainty.We obtain the suboptimal power allocation solution through iterative optimization.In the frequency resource scheduling subproblem,we develop a heuristic algorithm to handle the non-convexity of the problem.The simulation results show that the combination of power allocation and frequency resource scheduling algorithms can improve spectrum utilization.展开更多
Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protectin...Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.展开更多
The weapon transportation support scheduling problem on aircraft carrier deck is the key to restricting the sortie rate and combat capability of carrier-based aircraft.This paper studies the problem and presents a nov...The weapon transportation support scheduling problem on aircraft carrier deck is the key to restricting the sortie rate and combat capability of carrier-based aircraft.This paper studies the problem and presents a novel solution architecture.Taking the interference of the carrier-based aircraft deck layout on the weapon transportation route and precedence constraint into consideration,a mixed integer formulation is established to minimize the total objective,which is constituted of makespan,load variance and accumulative transfer time of support unit.Solution approach is developed for the model.Firstly,based on modeling the carrier aircraft parked on deck as convex obstacles,the path library of weapon transportation is constructed through visibility graph and Warshall-Floyd methods.We then propose a bi-population immune algorithm in which a population-based forward/backward scheduling technique,local search schemes and a chaotic catastrophe operator are embedded.Besides,the randomkey solution representation and serial scheduling generation scheme are adopted to conveniently obtain a better solution.The Taguchi method is additionally employed to determine key parameters of the algorithm.Finally,on a set of generated realistic instances,we demonstrate that the proposed algorithm outperforms all compared algorithms designed for similar optimization problems and can significantly improve the efficiency,and that the established model and the bi-population immune algorithm can effectively respond to the weapon support requirements of carrier-based aircraft under different sortie missions.展开更多
Satellite communication systems provide a cost-effective solution for global internet of things(IoT)applications due to its large coverage and easy deployment.This paper mainly focuses on Satellite networks system,in ...Satellite communication systems provide a cost-effective solution for global internet of things(IoT)applications due to its large coverage and easy deployment.This paper mainly focuses on Satellite networks system,in which low earth orbit(LEO)satellites network collect sensing data from the user terminals(UTs)and then forward the data to ground station through geostationary earth orbit(GEO)satellites network.Considering the limited uplink transmission resources,this paper optimizes the uplink transmission scheduling scheme over LEO satellites.A novel transmission scheduling algorithm,which combined Algorithms of Simulated Annealing and Monte Carlo(SA-MC),is proposed to achieve the dynamic optimal scheduling scheme.Simulation results show the effectiveness of the proposed SA-MC algorithm in terms of cost value reduction and fast convergence.展开更多
The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated netw...The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated networks with the 5G system(5GS)as a TSN bridge are promising to provide the required communication service.To guarantee the endto-end(E2E)QoS(Quality of Service)performance of traffic is a great challenge in 5G-TSN integrated networks.A dynamic QoS mapping method is proposed in this paper.It is based on the improved K-means clustering algorithm and the rough set theory(IKCRQM).The IKC-RQM designs a dynamic and loadaware QoS mapping algorithm to improve its flexibility.An adaptive semi-persistent scheduling(ASPS)mechanism is proposed to solve the challenging deterministic scheduling in 5GS.It includes two parts:one part is the persistent resource allocation for timesensitive flows,and the other part is the dynamic resource allocation based on the max-min fair share algorithm.Simulation results show that the proposed IKC-RQM algorithm achieves flexible and appropriate QoS mapping,and the ASPS performs corresponding resource allocations to guarantee the deterministic transmissions of time-sensitive flows in 5G-TSN integrated networks.展开更多
In this paper, we propose optimum and sub-optimum resource allocation and opportunistic scheduling solutions for orthogonal frequency division multiple access （OFDMA）-based multicellular systems. The applicability, ...In this paper, we propose optimum and sub-optimum resource allocation and opportunistic scheduling solutions for orthogonal frequency division multiple access （OFDMA）-based multicellular systems. The applicability, complexity, and performance of the proposed algorithms are analyzed and numerically evaluated. In the initial setup, the fractional frequency reuse （FFR） technique for inter-cell interference cancellation is applied to classify the users into two groups, namely interior and exterior users. Adaptive modulation is then employed according to the channel state information （CSI） of each user to meet the symbol error rate （SER） requirement. There then, we develop subcarrier-and-bit allocation method, which maximizes the total system throughput subject to the constraints that each user has a minimum data rate requirement. The algorithm to achieve the optimum solution requires high computational complexity which hinders it from practicability. Toward this suboptimum method with the reduced to the order of O（NIO, the total number of subcarriers end, we complexity propose a extensively where N and K denote and users, respectively. Numerical results show that the proposed algorithm approaches the optimum solution, yet it enjoys the features of simplicity, dynamic cell configuration, adaptive subearrier-and-bit allocation, and spectral efficiency.展开更多
A Dominant Resource Fairness （DRF） based scheme for job scheduling in distributed cloud computing systems which was modeled as multi-job scheduling and multi-resource allocation coupling problem is proposed, where t...A Dominant Resource Fairness （DRF） based scheme for job scheduling in distributed cloud computing systems which was modeled as multi-job scheduling and multi-resource allocation coupling problem is proposed, where the resource pool is constructed from a large number of distributed heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, storage and bandwidth. By introducing dominant resource share of jobs and virtual machines, the multi-job scheduling and multi-resource allocation joint mechanism significantly improves the cloud system＇s resource utilization, yet with a substantial reduction of job completion times. We show through experiments and case studies the superior performance of the algorithms in practice.展开更多
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
基金funded by State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster under Grant MS01240103the National Natural Science Foundation of China under Grant 62071146National 2011 Collaborative Innovation Center of Wireless Communication Technologies under Grant 2242022k60006.
文摘In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allowing user equipment(UE)of terrestrial networks to share frequencies with satellite networks.In order to protect the satellite terminal(ST),the base station(BS)needs to control the transmit power and frequency resources of the UE.The optimization problem involves maximizing the achievable throughput while satisfying the interference protection constraints of the ST and the quality of service(QoS)of the UE.However,this problem is highly nonconvex,and we decompose it into power allocation and frequency resource scheduling subproblems.In the power allocation subproblem,we propose a power allocation algorithm based on interference probability(PAIP)to address channel uncertainty.We obtain the suboptimal power allocation solution through iterative optimization.In the frequency resource scheduling subproblem,we develop a heuristic algorithm to handle the non-convexity of the problem.The simulation results show that the combination of power allocation and frequency resource scheduling algorithms can improve spectrum utilization.
基金supported by the National Natural Science Foundation of China (No. 61971077)the Natural Science Foundation of Chongqing, China (No. cstc2021jcyj-msxmX0458)+3 种基金the open research fund of National Mobile Communications Research Laboratory, Southeast University (No. 2022D06)the Fundamental Research Funds for the Central Universities (No. 2020CDCGTX074)the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu (No. BK20212001)the Natural Science Research Project of Jiangsu Higher Education Institutions (No. 21KJB510034)。
文摘Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.
基金the financial support of the National Natural Science Foundation of China(No.52102453)。
文摘The weapon transportation support scheduling problem on aircraft carrier deck is the key to restricting the sortie rate and combat capability of carrier-based aircraft.This paper studies the problem and presents a novel solution architecture.Taking the interference of the carrier-based aircraft deck layout on the weapon transportation route and precedence constraint into consideration,a mixed integer formulation is established to minimize the total objective,which is constituted of makespan,load variance and accumulative transfer time of support unit.Solution approach is developed for the model.Firstly,based on modeling the carrier aircraft parked on deck as convex obstacles,the path library of weapon transportation is constructed through visibility graph and Warshall-Floyd methods.We then propose a bi-population immune algorithm in which a population-based forward/backward scheduling technique,local search schemes and a chaotic catastrophe operator are embedded.Besides,the randomkey solution representation and serial scheduling generation scheme are adopted to conveniently obtain a better solution.The Taguchi method is additionally employed to determine key parameters of the algorithm.Finally,on a set of generated realistic instances,we demonstrate that the proposed algorithm outperforms all compared algorithms designed for similar optimization problems and can significantly improve the efficiency,and that the established model and the bi-population immune algorithm can effectively respond to the weapon support requirements of carrier-based aircraft under different sortie missions.
文摘Satellite communication systems provide a cost-effective solution for global internet of things(IoT)applications due to its large coverage and easy deployment.This paper mainly focuses on Satellite networks system,in which low earth orbit(LEO)satellites network collect sensing data from the user terminals(UTs)and then forward the data to ground station through geostationary earth orbit(GEO)satellites network.Considering the limited uplink transmission resources,this paper optimizes the uplink transmission scheduling scheme over LEO satellites.A novel transmission scheduling algorithm,which combined Algorithms of Simulated Annealing and Monte Carlo(SA-MC),is proposed to achieve the dynamic optimal scheduling scheme.Simulation results show the effectiveness of the proposed SA-MC algorithm in terms of cost value reduction and fast convergence.
基金supported by National Key Research and Development Project under Grant No.2020YFB1710900Sichuan International Cooperation Project of Science and Technology Innovation under Grant No.2022YFH0022。
文摘The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated networks with the 5G system(5GS)as a TSN bridge are promising to provide the required communication service.To guarantee the endto-end(E2E)QoS(Quality of Service)performance of traffic is a great challenge in 5G-TSN integrated networks.A dynamic QoS mapping method is proposed in this paper.It is based on the improved K-means clustering algorithm and the rough set theory(IKCRQM).The IKC-RQM designs a dynamic and loadaware QoS mapping algorithm to improve its flexibility.An adaptive semi-persistent scheduling(ASPS)mechanism is proposed to solve the challenging deterministic scheduling in 5GS.It includes two parts:one part is the persistent resource allocation for timesensitive flows,and the other part is the dynamic resource allocation based on the max-min fair share algorithm.Simulation results show that the proposed IKC-RQM algorithm achieves flexible and appropriate QoS mapping,and the ASPS performs corresponding resource allocations to guarantee the deterministic transmissions of time-sensitive flows in 5G-TSN integrated networks.
文摘In this paper, we propose optimum and sub-optimum resource allocation and opportunistic scheduling solutions for orthogonal frequency division multiple access （OFDMA）-based multicellular systems. The applicability, complexity, and performance of the proposed algorithms are analyzed and numerically evaluated. In the initial setup, the fractional frequency reuse （FFR） technique for inter-cell interference cancellation is applied to classify the users into two groups, namely interior and exterior users. Adaptive modulation is then employed according to the channel state information （CSI） of each user to meet the symbol error rate （SER） requirement. There then, we develop subcarrier-and-bit allocation method, which maximizes the total system throughput subject to the constraints that each user has a minimum data rate requirement. The algorithm to achieve the optimum solution requires high computational complexity which hinders it from practicability. Toward this suboptimum method with the reduced to the order of O（NIO, the total number of subcarriers end, we complexity propose a extensively where N and K denote and users, respectively. Numerical results show that the proposed algorithm approaches the optimum solution, yet it enjoys the features of simplicity, dynamic cell configuration, adaptive subearrier-and-bit allocation, and spectral efficiency.
文摘A Dominant Resource Fairness （DRF） based scheme for job scheduling in distributed cloud computing systems which was modeled as multi-job scheduling and multi-resource allocation coupling problem is proposed, where the resource pool is constructed from a large number of distributed heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, storage and bandwidth. By introducing dominant resource share of jobs and virtual machines, the multi-job scheduling and multi-resource allocation joint mechanism significantly improves the cloud system＇s resource utilization, yet with a substantial reduction of job completion times. We show through experiments and case studies the superior performance of the algorithms in practice.
