Cognitive emergency communication net-works can meet the requirements of large capac-ity,high density and low delay in emergency com-munications.This paper analyzes the properties of emergency users in cognitive emerg...Cognitive emergency communication net-works can meet the requirements of large capac-ity,high density and low delay in emergency com-munications.This paper analyzes the properties of emergency users in cognitive emergency communi-cation networks,designs a multi-objective optimiza-tion and proposes a novel multi-objective bacterial foraging optimization algorithm based on effective area(MOBFO-EA)to maximize the transmission rate while maximizing the lifecycle of the network.In the algorithm,the effective area is proposed to prevent the algorithm from falling into a local optimum,and the diversity and uniformity of the Pareto-optimal solu-tions distributed in the effective area are used to eval-uate the optimization algorithm.Then,the dynamic preservation is used to enhance the competitiveness of excellent individuals and the uniformity and diversity of the Pareto-optimal solutions in the effective area.Finally,the adaptive step size,adaptive moving direc-tion and inertial weight are used to shorten the search time of bacteria and accelerate the optimization con-vergence.The simulation results show that the pro-posed MOBFO-EA algorithm improves the efficiency of the Pareto-optimal solutions by approximately 55%compared with the MOPSO algorithm and by approx-imately 60%compared with the MOBFO algorithm and has the fastest and smoothest convergence.展开更多
Airborne pulse radar and communication systems are essential for precise detection and collision avoidance,ensuring that aircraft operate safely and efficiently.A major challenge in spectrum sharing is the allocation ...Airborne pulse radar and communication systems are essential for precise detection and collision avoidance,ensuring that aircraft operate safely and efficiently.A major challenge in spectrum sharing is the allocation of resources in both the time and frequency domains,aiming to minimize inter-system interference as the available spectrum fluctuates over time.In this paper,regarding maximization of detection probability and spectrum utilization efficiency as two fundamental objectives,a novel Dynamic Spectrum and Power Allocation based on Genetic Algorithm(GA-DSPA)model is proposed,which dynamically allocates communication channel frequency and power under the constraints of pulse radar detection probability and signal-to-interferenceplus-noise ratio of communication.To solve this bi-objective model,a non-dominated sortingbased multi-objective genetic algorithm is developed.A novel environment perception strategy and offspring sorting technique based on radar echoes are integrated into the optimization framework.Simulation results indicate that by integrating environmental monitoring mechanisms and dynamic adaptation strategies,the proposed method effectively tracks the evolving Paretooptimal Fronts(Po Fs),thereby ensuring optimal performance for both co-located pulse radar and communication systems.Hardware test results confirm that within the GA-DSPA framework,the pulse radar achieves higher detection probabilities under identical conditions,while the communication system realizes increased average throughput.展开更多
Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA stra...Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.展开更多
The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and management.However,the network control failure affects the dy...The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and management.However,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low resilience.Thus,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane failures.This fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network performance.In this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and availability.By considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular controllers.The algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness.The effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline algorithms.The findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.展开更多
The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communicati...The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.展开更多
In cognitive radio networks(CRNs),multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time,i.e.,request arrivals usually show an aggregate manner....In cognitive radio networks(CRNs),multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time,i.e.,request arrivals usually show an aggregate manner.Moreover,a secondary user packet waiting in the buffer may leave the system due to impatience before it is transmitted,and this impatient behavior inevitably has an impact on the system performance.Aiming to investigate the influence of the aggregate behavior of requests and the likelihood of impatience on a dynamic spectrum allocation scheme in CRNs,in this paper a batch arrival queueing model with possible reneging and potential transmission interruption is established.By constructing a Markov chain and presenting a transition rate matrix,the steady-state distribution of the queueing model along with a dynamic spectrum allocation scheme is derived to analyze the stochastic behavior of the system.Accordingly,some important performance measures such as the loss rate,the balk rate and the average delay of secondary user packets are given.Moreover,system experiments are carried out to show the change trends of the performance measures with respect to batch arrival rates of secondary user packets for different impatience parameters,different batch sizes of secondary user packets,and different arrival rates of primary user packets.Finally,a pricing policy for secondary users is presented and the dynamic spectrum allocation scheme is socially optimized.展开更多
The rapid growth in demand for broadband wireless services coupled with the recent developmental work on wireless communications technology and the static allocation of the spectrum have led to the artificial scarcity...The rapid growth in demand for broadband wireless services coupled with the recent developmental work on wireless communications technology and the static allocation of the spectrum have led to the artificial scarcity of the radio spectrum. The traditional command and control model (Static allocation) of spectrum allocation policy allows for severe spectrum underutilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a hybrid access scheme based on CSMA/CA and TDMA MAC protocols for use in the TV bands. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that, the proposed schemes protect the primary user from harmful Interference from the secondary user. In terms of delay, it was found that packet arrival rates, data rates and the number of secondary users have significant effects on delay.展开更多
The growing demand for wireless services coupled with the limited availability of suitable electromagnetic spectrum is increasing the need for more efficient RF spectrum utilization. Spectrum allocated to TV operators...The growing demand for wireless services coupled with the limited availability of suitable electromagnetic spectrum is increasing the need for more efficient RF spectrum utilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a dynamic spectrum access scheme for use in the TV bands which uses cognitive radio techniques to determine the spectrum availability. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that the proposed scheme protects the primary users from harmful interference from the secondary users. In comparison with the 802.11 MAC protocol, the scheme improves spectrum utilization by about 27% while limiting the interference imposed on the primary receiver.展开更多
Purpose:Community detection of dynamic networks provides more effective information than static network community detection in the real world.The mainstream method for community detection in dynamic networks is evolut...Purpose:Community detection of dynamic networks provides more effective information than static network community detection in the real world.The mainstream method for community detection in dynamic networks is evolutionary clustering,which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals.However,the error accumulation issues limit the effectiveness of evolutionary clustering.While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework,the traditional multi-objective evolutionary approach lacks self-adaptability.Design/methodology/approach:This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods.In this approach,a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.Findings:Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.Originality/value:To enhance the clustering results,adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D(A Multiobjective Optimization Evolutionary Algorithm based on Decomposition)to dynamically adjust the focus of different evolutionary stages.展开更多
Enabling cellular access for unmanned aerial vehicles(UAVs)is a practically appealing solution to realize their high-quality communications with the ground for ensuring safe and efficient operations.In this paper,we s...Enabling cellular access for unmanned aerial vehicles(UAVs)is a practically appealing solution to realize their high-quality communications with the ground for ensuring safe and efficient operations.In this paper,we study the trajectory design for a cellular-connected UAV that needs to fly from given initial to final locations,while communicating with the ground base stations(GBSs)subject to a minimum signal-to-noise ratio(SNR)requirement along its flight.However,due to various practical considerations such as GBSs’locations and coverage range as well as UAV’s trajectory and mobility constraints,the SNR target may not be met at certain time periods during the flight,each termed as an outage duration.In this paper,we first propose a general outage cost function in terms of outage durations in the flight,which includes the two commonly used metrics,namely total outage duration and maximum outage duration as special cases.Based on it,we formulate a UAV trajectory optimization problem to minimize its mission completion time,subject to a constraint on the maximum tolerable outage cost.To tackle this challenging(non-convex)optimization problem,we first transform it into a tractable form and thereby reveal some useful properties of the optimal trajectory solution.Based on these properties,we further simplify the problem and propose efficient algorithms to check its feasibility and obtain optimal as well as low-complexity suboptimal solutions for it by leveraging graph theory and convex optimization techniques.Numerical results show that our proposed trajectory designs outperform that by the conventional method of dynamic programming,in terms of both performance and complexity.展开更多
基金National Natural Sci-ence Foundation of China(Grant Nos.61871241 and 61771263)Science and Technology Program of Nantong(Grant No.JC2019117).
文摘Cognitive emergency communication net-works can meet the requirements of large capac-ity,high density and low delay in emergency com-munications.This paper analyzes the properties of emergency users in cognitive emergency communi-cation networks,designs a multi-objective optimiza-tion and proposes a novel multi-objective bacterial foraging optimization algorithm based on effective area(MOBFO-EA)to maximize the transmission rate while maximizing the lifecycle of the network.In the algorithm,the effective area is proposed to prevent the algorithm from falling into a local optimum,and the diversity and uniformity of the Pareto-optimal solu-tions distributed in the effective area are used to eval-uate the optimization algorithm.Then,the dynamic preservation is used to enhance the competitiveness of excellent individuals and the uniformity and diversity of the Pareto-optimal solutions in the effective area.Finally,the adaptive step size,adaptive moving direc-tion and inertial weight are used to shorten the search time of bacteria and accelerate the optimization con-vergence.The simulation results show that the pro-posed MOBFO-EA algorithm improves the efficiency of the Pareto-optimal solutions by approximately 55%compared with the MOPSO algorithm and by approx-imately 60%compared with the MOBFO algorithm and has the fastest and smoothest convergence.
基金co-supported by the National Natural Science Foundation of China(No.62293495)the National Key Research and Development Program of China(No.2023YFB3306900)the Academic Excellence Foundation of BUAA for ph.D Students,China。
文摘Airborne pulse radar and communication systems are essential for precise detection and collision avoidance,ensuring that aircraft operate safely and efficiently.A major challenge in spectrum sharing is the allocation of resources in both the time and frequency domains,aiming to minimize inter-system interference as the available spectrum fluctuates over time.In this paper,regarding maximization of detection probability and spectrum utilization efficiency as two fundamental objectives,a novel Dynamic Spectrum and Power Allocation based on Genetic Algorithm(GA-DSPA)model is proposed,which dynamically allocates communication channel frequency and power under the constraints of pulse radar detection probability and signal-to-interferenceplus-noise ratio of communication.To solve this bi-objective model,a non-dominated sortingbased multi-objective genetic algorithm is developed.A novel environment perception strategy and offspring sorting technique based on radar echoes are integrated into the optimization framework.Simulation results indicate that by integrating environmental monitoring mechanisms and dynamic adaptation strategies,the proposed method effectively tracks the evolving Paretooptimal Fronts(Po Fs),thereby ensuring optimal performance for both co-located pulse radar and communication systems.Hardware test results confirm that within the GA-DSPA framework,the pulse radar achieves higher detection probabilities under identical conditions,while the communication system realizes increased average throughput.
基金supported in part by the National Natural Sciences Foundation of China (NSFC) under Grant 61525103the National Natural Sciences Foundation of China under Grant 61501140the Shenzhen Fundamental Research Project under Grant JCYJ20150930150304185
文摘Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.
基金the Organization for Women in Science for the Developing World(OWSD)and the Swedish International Development Cooperation Agency(SIDA)under grant No.3240291613 for their financial support.
文摘The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and management.However,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low resilience.Thus,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane failures.This fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network performance.In this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and availability.By considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular controllers.The algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness.The effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline algorithms.The findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.
文摘The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.
基金supported in part by National Natural Science Foundation of China under Grant Nos.61872311,61973261 and 62006069supported in part by MEXT,Japan.Also。
文摘In cognitive radio networks(CRNs),multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time,i.e.,request arrivals usually show an aggregate manner.Moreover,a secondary user packet waiting in the buffer may leave the system due to impatience before it is transmitted,and this impatient behavior inevitably has an impact on the system performance.Aiming to investigate the influence of the aggregate behavior of requests and the likelihood of impatience on a dynamic spectrum allocation scheme in CRNs,in this paper a batch arrival queueing model with possible reneging and potential transmission interruption is established.By constructing a Markov chain and presenting a transition rate matrix,the steady-state distribution of the queueing model along with a dynamic spectrum allocation scheme is derived to analyze the stochastic behavior of the system.Accordingly,some important performance measures such as the loss rate,the balk rate and the average delay of secondary user packets are given.Moreover,system experiments are carried out to show the change trends of the performance measures with respect to batch arrival rates of secondary user packets for different impatience parameters,different batch sizes of secondary user packets,and different arrival rates of primary user packets.Finally,a pricing policy for secondary users is presented and the dynamic spectrum allocation scheme is socially optimized.
文摘The rapid growth in demand for broadband wireless services coupled with the recent developmental work on wireless communications technology and the static allocation of the spectrum have led to the artificial scarcity of the radio spectrum. The traditional command and control model (Static allocation) of spectrum allocation policy allows for severe spectrum underutilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a hybrid access scheme based on CSMA/CA and TDMA MAC protocols for use in the TV bands. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that, the proposed schemes protect the primary user from harmful Interference from the secondary user. In terms of delay, it was found that packet arrival rates, data rates and the number of secondary users have significant effects on delay.
文摘The growing demand for wireless services coupled with the limited availability of suitable electromagnetic spectrum is increasing the need for more efficient RF spectrum utilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a dynamic spectrum access scheme for use in the TV bands which uses cognitive radio techniques to determine the spectrum availability. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that the proposed scheme protects the primary users from harmful interference from the secondary users. In comparison with the 802.11 MAC protocol, the scheme improves spectrum utilization by about 27% while limiting the interference imposed on the primary receiver.
基金supported by the Key Project of Science and Technology Innovation(2030)supported by the Ministry of Science and Technology of China(Grant No.2018AAA0101301)the Key Research Platforms and Projects of High School in Guangdong Province(No.2023ZDZX1028,2023ZDZX1050)+1 种基金Dongguan Social Development Science and Technology Project(No.20211800904722)Dongguan Science and Technology Special Commissioner Project(No.2021180050007).
文摘Purpose:Community detection of dynamic networks provides more effective information than static network community detection in the real world.The mainstream method for community detection in dynamic networks is evolutionary clustering,which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals.However,the error accumulation issues limit the effectiveness of evolutionary clustering.While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework,the traditional multi-objective evolutionary approach lacks self-adaptability.Design/methodology/approach:This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods.In this approach,a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.Findings:Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.Originality/value:To enhance the clustering results,adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D(A Multiobjective Optimization Evolutionary Algorithm based on Decomposition)to dynamically adjust the focus of different evolutionary stages.
文摘Enabling cellular access for unmanned aerial vehicles(UAVs)is a practically appealing solution to realize their high-quality communications with the ground for ensuring safe and efficient operations.In this paper,we study the trajectory design for a cellular-connected UAV that needs to fly from given initial to final locations,while communicating with the ground base stations(GBSs)subject to a minimum signal-to-noise ratio(SNR)requirement along its flight.However,due to various practical considerations such as GBSs’locations and coverage range as well as UAV’s trajectory and mobility constraints,the SNR target may not be met at certain time periods during the flight,each termed as an outage duration.In this paper,we first propose a general outage cost function in terms of outage durations in the flight,which includes the two commonly used metrics,namely total outage duration and maximum outage duration as special cases.Based on it,we formulate a UAV trajectory optimization problem to minimize its mission completion time,subject to a constraint on the maximum tolerable outage cost.To tackle this challenging(non-convex)optimization problem,we first transform it into a tractable form and thereby reveal some useful properties of the optimal trajectory solution.Based on these properties,we further simplify the problem and propose efficient algorithms to check its feasibility and obtain optimal as well as low-complexity suboptimal solutions for it by leveraging graph theory and convex optimization techniques.Numerical results show that our proposed trajectory designs outperform that by the conventional method of dynamic programming,in terms of both performance and complexity.
