In this paper,we investigate a multi-UAV aided NOMA communication system,where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication,and each UAV serves its as...In this paper,we investigate a multi-UAV aided NOMA communication system,where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication,and each UAV serves its associated users on its own bandwidth.We aim at maximizing the overall common throughput in a finite time period.Such a problem is a typical mixed integer nonlinear problem,which involves both continuous-variable and combinatorial optimizations.To efficiently solve this problem,we propose a two-layer algorithm,which separately tackles continuous-variable and combinatorial optimization.Specifically,in the inner layer given one user association scheme,subproblems of bandwidth allocation,power allocation and trajectory design are solved based on alternating optimization.In the outer layer,a small number of candidate user association schemes are generated from an initial scheme and the best solution can be determined by comparing all the candidate schemes.In particular,a clustering algorithm based on K-means is applied to produce all candidate user association schemes,the successive convex optimization technique is adopted in the power allocation subproblem and a logistic function approximation approach is employed in the trajectory design subproblem.Simulation results show that the proposed NOMA scheme outperforms three baseline schemes in downlink common throughput,including one solution proposed in an existing literature.展开更多
In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem i...In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.展开更多
In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Se...In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.展开更多
Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations...Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.展开更多
Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for t...Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.展开更多
Serving multiple cell-edge mobile terminals poses multifaceted challenges due to the increased transmission power and interferences, which could be overcome by relay communications. With the recent advancement of 5G t...Serving multiple cell-edge mobile terminals poses multifaceted challenges due to the increased transmission power and interferences, which could be overcome by relay communications. With the recent advancement of 5G technologies, non-orthogonal multiple access(NOMA) has been used at relay node to transmit multiple messages simultaneously to multiple cell-edge users. In this paper, a Collaborative NOMA Assisted Relaying(CNAR) system for 5G is proposed by enabling the collaboration of source-relay(S-R) and relay-destination(R-D) NOMA links. The relay node of the CNAR decodes the message for itself from S-R NOMA signal and transmits the remaining messages to the multiple cell-edge users in R-D link. A simplified-CNAR(S-CNAR) system is then developed to reduce the relay complexity. The outage probabilities for both systems are analyzed by considering outage behaviors in S-R and R-D links separately. To guarantee the data rate, the optimal power allocation among NOMA users is achieved by minimizing the outage probability. The ergodic sum capacity in high SNR regime is also approximated. Our mathematical analysis and simulation results show that CNAR system outperforms existing transmission strategies and S-CNAR reaches similar performance with much lower complexity.展开更多
This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre...This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.展开更多
The bandwidth resources allocation strategies of the existing Internet of Vehicles(IoV) are mainly base on the communication architecture of the traditional 802.11 x in the wireless local area network(WLAN). The tradi...The bandwidth resources allocation strategies of the existing Internet of Vehicles(IoV) are mainly base on the communication architecture of the traditional 802.11 x in the wireless local area network(WLAN). The traditional communication architecture of IoV will easily cause significant delay and low Packet Delivery Ratio(PDR) for disseminating critical security beacons under the condition of high-speed movement, distance-varying communication, and mixed traffic. This paper proposes a novel bandwidth-link resources cooperative allocation strategy to achieve better communication performance under the road conditions of intelligent transportation systems(ITS). Firstly, in traffic scenarios, based on the characteristic to predict the relative position of the mobile transceivers, a strategy is developed to cooperate on the mobile cellular network and the Dedicated Short-Range Communications(DSRC). Secondly, by adopting the general network simulator NS3, the dedicated mobile channel models that are suitable for the data interaction of ITS, is applied to confirm the feasibility and reliability of the strategy. Finally, by the simulation, comparison, and analysis of some critical performance parame-ters, we conclude that the novel strategy does not only reduce the system delay but also improve the other communication performance indicators, such as the PDR and communication capacity.展开更多
Dynamic resource allocation(DRA) is a key technology to improve system performances in GEO multi-beam satellite systems. And, since the cache resource on the satellite is very valuable and limited, DRA problem under r...Dynamic resource allocation(DRA) is a key technology to improve system performances in GEO multi-beam satellite systems. And, since the cache resource on the satellite is very valuable and limited, DRA problem under restricted cache resources is also an important issue to be studied. This paper mainly investigates the DRA problem of carrier resources under certain cache constraints. What's more, with the aim to satisfy all users' traffic demands as more as possible, and to maximize the utilization of the bandwidth, we formulate a multi-objective optimization problem(MOP) where the satisfaction index and the spectrum efficiency are jointly optimized. A modified strategy SA-NSGAII which combines simulated annealing(SA) and non-dominated sorted genetic algorithm-II(NSGAII) is proposed to approximate the Pareto solution to this MOP problem. Simulation results show the effectiveness of the proposed algorithm in terms of satisfaction index, spectrum efficiency, occupied cache, and etc.展开更多
This paper investigates the achievable uplink spectral efficiency(SE) of a massive multi-input multi-output(MIMO) system with a mixed analog-to-digital converter(ADC) receiver architecture, in which some antennas are ...This paper investigates the achievable uplink spectral efficiency(SE) of a massive multi-input multi-output(MIMO) system with a mixed analog-to-digital converter(ADC) receiver architecture, in which some antennas are equipped with full-resolution ADCs while others are deployed with low-resolution ADCs. We derive the theoretical results and corresponding approximate expressions of the achievable SE in multi-cell systems with maximum ratio combining(MRC) detector and in single-cell systems with zero-forcing(ZF) detector. Based on approximated results, the effects of physical parameters, including the transmit power, the number of antennas, the proportion of full-resolution ADCs and the quantization precision of the low-resolution ADCs on the achievable SE are revealed. Furthermore, we propose the power allocation algorithms based on the lower bound and upper bound of approximate achievable SE. Our results show that the total achievable SE improves by increasing the number of BS antennas, the signal-to-noise ratio(SNR), and the quantization precision. Results showcase that proposed power allocation algorithms remarkably improve the total achievable SE comparing to the equal power allocation algorithm, which verifies the effectiveness of our proposed schemes.展开更多
Beam hopping technology provides a foundation for the flexible allocation and efficient utilization of satellite resources,which is considered as a key technology for the next generation of high throughput satellite s...Beam hopping technology provides a foundation for the flexible allocation and efficient utilization of satellite resources,which is considered as a key technology for the next generation of high throughput satellite systems.To alleviate the contradiction between resource utilization and co-frequency interference in beam hopping technology,this paper firstly studies dynamic clustering to balance traffic between clusters and proposes cluster hopping pool optimization method to avoid inter-cluster interference.Then based on the optimization results,a novel joint beam hopping and precoding algorithm is provided to combine resource allocation and intra-cluster interference suppression,which can make efficient utilization of system resources and achieve reliable and near-optimal transmission capacity.The simulation results show that,compared with traditional methods,the proposed algorithms can dynamically adjust to balance demand traffic between clusters and meet the service requirements of each beam,also eliminate the co-channel interference to improve the performance of satellite network.展开更多
The high mobility of unmanned aerial vehicles(UAVs)could bring abundant degrees of freedom for the design of wireless communication systems,which results in that UAVs,especially UAV swarm,have attracted considerable a...The high mobility of unmanned aerial vehicles(UAVs)could bring abundant degrees of freedom for the design of wireless communication systems,which results in that UAVs,especially UAV swarm,have attracted considerable attention.This paper considers a UAV Swarm enabled relaying communication system,where multiple UAV relays are organized via coordinated multiple points(CoMP)as a UAV swarm to enhance physical layer security of the system in the presence of an eavesdropper.In order to maximize achievable secrecy rate of downlink,we jointly optimize the beamforming vector of the virtual array shaped by the UAV swarm and bandwidth allocation on it for receiving and forwarding,and both amplify-and-forward(AF)and decode-andforward(DF)protocols are considered on the UAV swarm.Due to the non-convexity of the joint optimization problem,we propose an alternating optimization(AO)algorithm to decompose it into two subproblems utilizing block coordinate descent technique,then each subproblem is solved by successive convex optimization method.Simulation results demonstrate that DF has competitive performance advantage compared with AF and the superiority of the proposed secure transmission strategy with optimal beamforming and bandwidth allocation compared with benchmark strategies.展开更多
In this paper,an expression for the user’s achievable data rate in the multi-user multiple-input multiple-output(MU-MIMO)system with limited feedback(LF)of channel state information(CSI)is derived.The energy efficien...In this paper,an expression for the user’s achievable data rate in the multi-user multiple-input multiple-output(MU-MIMO)system with limited feedback(LF)of channel state information(CSI)is derived.The energy efficiency(EE)is optimized through power allocation under quality of service(QoS)constraints.Based on mathematical equivalence and Lagrange multiplier approach,an energy-efficient unequal power allocation(EEUPA)with LF of CSI scheme is proposed.The simulation results show that as the number of transmitting antennas increases,the EE also increases which is promising for the next generation wireless communication networks.Moreover,it can be seen that the QoS requirement has an effect on the EE of the system.Ultimately,the proposed EEUPA with LF of CSI algorithm performs better than the existing energy-efficient equal power allocation(EEEPA)with LF of CSI schemes.展开更多
Mobile edge computing(MEC)emerges as a paradigm to free mobile devices(MDs)from increasingly dense computing workloads in 6G networks.The quality of computing experience can be greatly improved by offloading computing...Mobile edge computing(MEC)emerges as a paradigm to free mobile devices(MDs)from increasingly dense computing workloads in 6G networks.The quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC servers.Renewable energy harvested by energy harvesting equipments(EHQs)is considered as a promising power supply for users to process and offload tasks.In this paper,we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage energy.We investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC system.We propose an effective joint partial computation offloading and resource allocation(CORA)algorithm which is based on deep reinforcement learning(DRL)to obtain the optimal scheduling without prior knowledge of task arrival,renewable energy arrival as well as channel condition.The simulation results verify the efficiency of the proposed algorithm,which undoubtedly minimizes the cost of MDs compared with other benchmarks.展开更多
A low-complexity optimization scheme is proposed to balance the tradeoff between system capacity and proportional fairness in orthogonal frequency division multiple access(OFDMA) based multicast systems. The major cha...A low-complexity optimization scheme is proposed to balance the tradeoff between system capacity and proportional fairness in orthogonal frequency division multiple access(OFDMA) based multicast systems. The major challenge is to solve the non-convexity optimization problem with strict proportional fairness. Constrained team progress algorithm(CTPA) solves this non-convexity problem by allocating sub-channels to each group based on sub-channel gains and proportional fairness constraint. Mapping power algorithm(MPA) guarantees strict proportional fairness with efficient power allocation which utilizes the mapping relation between power and throughput. CTPA-MPA is analyzed in three aspects: complexity, fairness and efficiency. We numerically show that when the system capacity is slightly increased in lower power region compared with several previous approaches, CTPA-MPA improves the proportional fairness in a typical scenario with 4 groups over 16 sub-channels, while reducing the complexity from exponential to linear in the number of sub-channels. It is also proved available in a more complicated system.展开更多
A new reliability allocation model has been built for engine system, which is a repairable system, and consists of a large number of mechanical components. The cost and reliability are taken as objective function and ...A new reliability allocation model has been built for engine system, which is a repairable system, and consists of a large number of mechanical components. The cost and reliability are taken as objective function and constraint condition respectively. The parameters of components lifetime distribution are given as decision variables, and the component lifetimes are assumed to follow that Weibull distribution. The allocation is separated into two steps to reduce calculated amount of one allocation. Genetic algorithm and Monte Carlo method are applied to solve distribution parameters and system cost separately.展开更多
The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated.An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DA...The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated.An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DADCS)is proposed,whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system.Firstly,considering the information fusion rules and different working modes of the netted radar system,a two-factor jamming effectiveness evaluation function is constructed,detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode,respectively.Then a nonconvex optimization model for cooperatively jamming netted radar system is established.Finally,a dynamic adaptive discrete cuckoo search algorithm(DADCS)is constructed by improving path update strategies and introducing a global learning mechanism,and a three-step solution method is proposed subsequently.Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.展开更多
In this paper, we propose a parameter allocation scheme in a parallel array bistable stochastic resonance-based communication system(P-BSR-CS) to improve the performance of weak binary pulse amplitude modulated(BPAM) ...In this paper, we propose a parameter allocation scheme in a parallel array bistable stochastic resonance-based communication system(P-BSR-CS) to improve the performance of weak binary pulse amplitude modulated(BPAM) signal transmissions. The optimal parameter allocation policy of the P-BSR-CS is provided to minimize the bit error rate(BER)and maximize the channel capacity(CC) under the adiabatic approximation condition. On this basis, we further derive the best parameter selection theorem in realistic communication scenarios via variable transformation. Specifically, the P-BSR structure design not only brings the robustness of parameter selection optimization, where the optimal parameter pair is not fixed but variable in quite a wide range, but also produces outstanding system performance. Theoretical analysis and simulation results indicate that in the P-BSR-CS the proposed parameter allocation scheme yields considerable performance improvement, particularly in very low signal-to-noise ratio(SNR) environments.展开更多
In order to maximize system energy efficiency(EE) under user quality of service(Qo S) restraints in Long Term Evolution-Advanced(LTE-A) networks,a constrained joint resource optimization allocation scheme is presented...In order to maximize system energy efficiency(EE) under user quality of service(Qo S) restraints in Long Term Evolution-Advanced(LTE-A) networks,a constrained joint resource optimization allocation scheme is presented,which is NP-hard. Hence,we divide it into three sub-problems to reduce computation complexity,i.e.,the resource block(RB) allocation,the power distribution,and the modulation and coding scheme(MCS) assignment for user codewords. Then an enhanced heuristic approach GAPSO is proposed and is adopted in the RB and power allocation respectively to reduce computational complexity further on. Moreover,a novel MCS allocation scheme is put forward,which could make a good balance between the system reliability and availability under different channel conditions. Simulation results show that the proposed GAPSO could achieve better performance in convergence speed and global optimum searching,and that the joint resource allocation scheme could improve energy efficiency effectively under user Qo S requirements.展开更多
基金supported by Beijing Natural Science Fund–Haidian Original Innovation Joint Fund(L232040 and L232045).
文摘In this paper,we investigate a multi-UAV aided NOMA communication system,where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication,and each UAV serves its associated users on its own bandwidth.We aim at maximizing the overall common throughput in a finite time period.Such a problem is a typical mixed integer nonlinear problem,which involves both continuous-variable and combinatorial optimizations.To efficiently solve this problem,we propose a two-layer algorithm,which separately tackles continuous-variable and combinatorial optimization.Specifically,in the inner layer given one user association scheme,subproblems of bandwidth allocation,power allocation and trajectory design are solved based on alternating optimization.In the outer layer,a small number of candidate user association schemes are generated from an initial scheme and the best solution can be determined by comparing all the candidate schemes.In particular,a clustering algorithm based on K-means is applied to produce all candidate user association schemes,the successive convex optimization technique is adopted in the power allocation subproblem and a logistic function approximation approach is employed in the trajectory design subproblem.Simulation results show that the proposed NOMA scheme outperforms three baseline schemes in downlink common throughput,including one solution proposed in an existing literature.
基金supported by the Fundamental Research Funds for the Central Universities of NUAA(No.kfjj20200414)Natural Science Foundation of Jiangsu Province in China(No.BK20181289).
文摘In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.
基金supported by the National Natural Science Foundation of China(Grant No.61971057).
文摘In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.
基金supported by National Natural Science Foundation of China(U2066209)。
文摘Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.
基金supported by National Natural Science Foundation of China No.62231012Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001Heilongjiang Province Postdoctoral General Foundation under Grant AUGA4110004923.
文摘Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.
文摘Serving multiple cell-edge mobile terminals poses multifaceted challenges due to the increased transmission power and interferences, which could be overcome by relay communications. With the recent advancement of 5G technologies, non-orthogonal multiple access(NOMA) has been used at relay node to transmit multiple messages simultaneously to multiple cell-edge users. In this paper, a Collaborative NOMA Assisted Relaying(CNAR) system for 5G is proposed by enabling the collaboration of source-relay(S-R) and relay-destination(R-D) NOMA links. The relay node of the CNAR decodes the message for itself from S-R NOMA signal and transmits the remaining messages to the multiple cell-edge users in R-D link. A simplified-CNAR(S-CNAR) system is then developed to reduce the relay complexity. The outage probabilities for both systems are analyzed by considering outage behaviors in S-R and R-D links separately. To guarantee the data rate, the optimal power allocation among NOMA users is achieved by minimizing the outage probability. The ergodic sum capacity in high SNR regime is also approximated. Our mathematical analysis and simulation results show that CNAR system outperforms existing transmission strategies and S-CNAR reaches similar performance with much lower complexity.
基金supported by China NSF Grants(61631020)the Fundamental Research Funds for the Central Universities(NP2018103,NE2017103,NC2017003)
文摘This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.
基金supported in part by the National Natural Science Foundation of China (No.61573171)the Major Information Projects of State Ministry of Transportation (No.2013-364-836-900)
文摘The bandwidth resources allocation strategies of the existing Internet of Vehicles(IoV) are mainly base on the communication architecture of the traditional 802.11 x in the wireless local area network(WLAN). The traditional communication architecture of IoV will easily cause significant delay and low Packet Delivery Ratio(PDR) for disseminating critical security beacons under the condition of high-speed movement, distance-varying communication, and mixed traffic. This paper proposes a novel bandwidth-link resources cooperative allocation strategy to achieve better communication performance under the road conditions of intelligent transportation systems(ITS). Firstly, in traffic scenarios, based on the characteristic to predict the relative position of the mobile transceivers, a strategy is developed to cooperate on the mobile cellular network and the Dedicated Short-Range Communications(DSRC). Secondly, by adopting the general network simulator NS3, the dedicated mobile channel models that are suitable for the data interaction of ITS, is applied to confirm the feasibility and reliability of the strategy. Finally, by the simulation, comparison, and analysis of some critical performance parame-ters, we conclude that the novel strategy does not only reduce the system delay but also improve the other communication performance indicators, such as the PDR and communication capacity.
基金supported by the National Science and Technology Major Project under Grant 2018ZX03001016
文摘Dynamic resource allocation(DRA) is a key technology to improve system performances in GEO multi-beam satellite systems. And, since the cache resource on the satellite is very valuable and limited, DRA problem under restricted cache resources is also an important issue to be studied. This paper mainly investigates the DRA problem of carrier resources under certain cache constraints. What's more, with the aim to satisfy all users' traffic demands as more as possible, and to maximize the utilization of the bandwidth, we formulate a multi-objective optimization problem(MOP) where the satisfaction index and the spectrum efficiency are jointly optimized. A modified strategy SA-NSGAII which combines simulated annealing(SA) and non-dominated sorted genetic algorithm-II(NSGAII) is proposed to approximate the Pareto solution to this MOP problem. Simulation results show the effectiveness of the proposed algorithm in terms of satisfaction index, spectrum efficiency, occupied cache, and etc.
基金supported in part by the National Science Foundation(NSFC)for Distinguished Young Scholars of China with Grant 61625106the National Natural Science Foundation of China under Grant 61531011+1 种基金the Hong Kong,Macao and Taiwan Science and Technology Cooperation Program of China(2016YFE0123100)the Guangzhou University project under Grant 27000503123
文摘This paper investigates the achievable uplink spectral efficiency(SE) of a massive multi-input multi-output(MIMO) system with a mixed analog-to-digital converter(ADC) receiver architecture, in which some antennas are equipped with full-resolution ADCs while others are deployed with low-resolution ADCs. We derive the theoretical results and corresponding approximate expressions of the achievable SE in multi-cell systems with maximum ratio combining(MRC) detector and in single-cell systems with zero-forcing(ZF) detector. Based on approximated results, the effects of physical parameters, including the transmit power, the number of antennas, the proportion of full-resolution ADCs and the quantization precision of the low-resolution ADCs on the achievable SE are revealed. Furthermore, we propose the power allocation algorithms based on the lower bound and upper bound of approximate achievable SE. Our results show that the total achievable SE improves by increasing the number of BS antennas, the signal-to-noise ratio(SNR), and the quantization precision. Results showcase that proposed power allocation algorithms remarkably improve the total achievable SE comparing to the equal power allocation algorithm, which verifies the effectiveness of our proposed schemes.
基金National Natural Science Foundation of China (No. 61901230, 61801445 and 91738201)Key pre-research project for civil space technology: Research project on VHTS communication technology (No. B0106)。
文摘Beam hopping technology provides a foundation for the flexible allocation and efficient utilization of satellite resources,which is considered as a key technology for the next generation of high throughput satellite systems.To alleviate the contradiction between resource utilization and co-frequency interference in beam hopping technology,this paper firstly studies dynamic clustering to balance traffic between clusters and proposes cluster hopping pool optimization method to avoid inter-cluster interference.Then based on the optimization results,a novel joint beam hopping and precoding algorithm is provided to combine resource allocation and intra-cluster interference suppression,which can make efficient utilization of system resources and achieve reliable and near-optimal transmission capacity.The simulation results show that,compared with traditional methods,the proposed algorithms can dynamically adjust to balance demand traffic between clusters and meet the service requirements of each beam,also eliminate the co-channel interference to improve the performance of satellite network.
文摘The high mobility of unmanned aerial vehicles(UAVs)could bring abundant degrees of freedom for the design of wireless communication systems,which results in that UAVs,especially UAV swarm,have attracted considerable attention.This paper considers a UAV Swarm enabled relaying communication system,where multiple UAV relays are organized via coordinated multiple points(CoMP)as a UAV swarm to enhance physical layer security of the system in the presence of an eavesdropper.In order to maximize achievable secrecy rate of downlink,we jointly optimize the beamforming vector of the virtual array shaped by the UAV swarm and bandwidth allocation on it for receiving and forwarding,and both amplify-and-forward(AF)and decode-andforward(DF)protocols are considered on the UAV swarm.Due to the non-convexity of the joint optimization problem,we propose an alternating optimization(AO)algorithm to decompose it into two subproblems utilizing block coordinate descent technique,then each subproblem is solved by successive convex optimization method.Simulation results demonstrate that DF has competitive performance advantage compared with AF and the superiority of the proposed secure transmission strategy with optimal beamforming and bandwidth allocation compared with benchmark strategies.
基金supported in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0883)in part by the Key Technologies R & D Program of Jiangsu Province (BE2018733)in part by Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT
文摘In this paper,an expression for the user’s achievable data rate in the multi-user multiple-input multiple-output(MU-MIMO)system with limited feedback(LF)of channel state information(CSI)is derived.The energy efficiency(EE)is optimized through power allocation under quality of service(QoS)constraints.Based on mathematical equivalence and Lagrange multiplier approach,an energy-efficient unequal power allocation(EEUPA)with LF of CSI scheme is proposed.The simulation results show that as the number of transmitting antennas increases,the EE also increases which is promising for the next generation wireless communication networks.Moreover,it can be seen that the QoS requirement has an effect on the EE of the system.Ultimately,the proposed EEUPA with LF of CSI algorithm performs better than the existing energy-efficient equal power allocation(EEEPA)with LF of CSI schemes.
基金supported in part by the National Natural Science Foundation of China under Grant 62072096in part by the Fundamental Research Funds for the Central Universities under Grant 2232020A12+3 种基金in part by the International S&T Cooperation Program of Shanghai Science and Technology Commission under Grant 20220713000in part by “Shuguang Program” of Shanghai Education Development Foundation and Shanghai Municipal Education Commissionin part by the Young Top-notch Talent Program in Shanghaiin part by “the Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University” under Grant CUSF-DH-D-2021058。
文摘Mobile edge computing(MEC)emerges as a paradigm to free mobile devices(MDs)from increasingly dense computing workloads in 6G networks.The quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC servers.Renewable energy harvested by energy harvesting equipments(EHQs)is considered as a promising power supply for users to process and offload tasks.In this paper,we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage energy.We investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC system.We propose an effective joint partial computation offloading and resource allocation(CORA)algorithm which is based on deep reinforcement learning(DRL)to obtain the optimal scheduling without prior knowledge of task arrival,renewable energy arrival as well as channel condition.The simulation results verify the efficiency of the proposed algorithm,which undoubtedly minimizes the cost of MDs compared with other benchmarks.
文摘A low-complexity optimization scheme is proposed to balance the tradeoff between system capacity and proportional fairness in orthogonal frequency division multiple access(OFDMA) based multicast systems. The major challenge is to solve the non-convexity optimization problem with strict proportional fairness. Constrained team progress algorithm(CTPA) solves this non-convexity problem by allocating sub-channels to each group based on sub-channel gains and proportional fairness constraint. Mapping power algorithm(MPA) guarantees strict proportional fairness with efficient power allocation which utilizes the mapping relation between power and throughput. CTPA-MPA is analyzed in three aspects: complexity, fairness and efficiency. We numerically show that when the system capacity is slightly increased in lower power region compared with several previous approaches, CTPA-MPA improves the proportional fairness in a typical scenario with 4 groups over 16 sub-channels, while reducing the complexity from exponential to linear in the number of sub-channels. It is also proved available in a more complicated system.
文摘A new reliability allocation model has been built for engine system, which is a repairable system, and consists of a large number of mechanical components. The cost and reliability are taken as objective function and constraint condition respectively. The parameters of components lifetime distribution are given as decision variables, and the component lifetimes are assumed to follow that Weibull distribution. The allocation is separated into two steps to reduce calculated amount of one allocation. Genetic algorithm and Monte Carlo method are applied to solve distribution parameters and system cost separately.
文摘The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated.An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DADCS)is proposed,whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system.Firstly,considering the information fusion rules and different working modes of the netted radar system,a two-factor jamming effectiveness evaluation function is constructed,detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode,respectively.Then a nonconvex optimization model for cooperatively jamming netted radar system is established.Finally,a dynamic adaptive discrete cuckoo search algorithm(DADCS)is constructed by improving path update strategies and introducing a global learning mechanism,and a three-step solution method is proposed subsequently.Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.61179027)the Qinglan Project of Jiangsu Province of China(Grant No.QL06212006)the University Postgraduate Research and Innovation Project of Jiangsu Province(Grant Nos.KYLX15_0829,KYLX15_0831)
文摘In this paper, we propose a parameter allocation scheme in a parallel array bistable stochastic resonance-based communication system(P-BSR-CS) to improve the performance of weak binary pulse amplitude modulated(BPAM) signal transmissions. The optimal parameter allocation policy of the P-BSR-CS is provided to minimize the bit error rate(BER)and maximize the channel capacity(CC) under the adiabatic approximation condition. On this basis, we further derive the best parameter selection theorem in realistic communication scenarios via variable transformation. Specifically, the P-BSR structure design not only brings the robustness of parameter selection optimization, where the optimal parameter pair is not fixed but variable in quite a wide range, but also produces outstanding system performance. Theoretical analysis and simulation results indicate that in the P-BSR-CS the proposed parameter allocation scheme yields considerable performance improvement, particularly in very low signal-to-noise ratio(SNR) environments.
基金supported in part by National Natural Science Foundation of China (No.61372070)Natural Science Basic Research Plan in Shaanxi Province of China (2015JM6324)+2 种基金Ningbo Natural Science Foundation (2015A610117)Hong Kong,Macao and Taiwan Science & Technology Cooperation Program of China (2015DFT10160)the 111 Project (B08038)
文摘In order to maximize system energy efficiency(EE) under user quality of service(Qo S) restraints in Long Term Evolution-Advanced(LTE-A) networks,a constrained joint resource optimization allocation scheme is presented,which is NP-hard. Hence,we divide it into three sub-problems to reduce computation complexity,i.e.,the resource block(RB) allocation,the power distribution,and the modulation and coding scheme(MCS) assignment for user codewords. Then an enhanced heuristic approach GAPSO is proposed and is adopted in the RB and power allocation respectively to reduce computational complexity further on. Moreover,a novel MCS allocation scheme is put forward,which could make a good balance between the system reliability and availability under different channel conditions. Simulation results show that the proposed GAPSO could achieve better performance in convergence speed and global optimum searching,and that the joint resource allocation scheme could improve energy efficiency effectively under user Qo S requirements.
