To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlin...To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.展开更多
This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization pr...This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.展开更多
Multi-user detection techniques are currently being studied as highly promising technologies for improving the performance of unsourced multiple access systems. In this paper, we propose joint multi-user detection sch...Multi-user detection techniques are currently being studied as highly promising technologies for improving the performance of unsourced multiple access systems. In this paper, we propose joint multi-user detection schemes with weighting factors for unsourced multiple access. First, we introduce bidirectional weighting factors in the extrinsic information passing process between the multi-user detector based on belief propagation (BP) and the LDPC decoder. Second, we incorporate bidirectional weighting factors in the message passing process between the MAC nodes and the user variable nodes in BP- based multi-user detector. The proposed schemes select the optimal weighting factors through simulations. The simulation results demonstrate that the proposed schemes exhibit significant performance improvements in terms of block error rate (BLER) compared to traditional schemes. .展开更多
In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustai...In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.展开更多
在移动边缘计算的物联网(Mobile Edge Computing-enabled Internet of Things Networks,IoT-MEC)中,物联终端的高移动性、服务请求的随机到达性以及网络流量的实时变化,导致原有应用场景下的资源配置与服务部署不再完全匹配。如何有效...在移动边缘计算的物联网(Mobile Edge Computing-enabled Internet of Things Networks,IoT-MEC)中,物联终端的高移动性、服务请求的随机到达性以及网络流量的实时变化,导致原有应用场景下的资源配置与服务部署不再完全匹配。如何有效利用网络提供的资源以实现服务功能链(Service Function Chain,SFC)的实时部署和重构是一个重要的挑战。针对用户的高移动性和网络流量的实时变化造成的SFC性能需求和已分配资源不匹配的问题,提出IoT-MEC网络中基于用户移动和资源需求预测的SFC重构策略。建立以SFC的端到端时延和重构成本最小化为目标的整数线性规划模型;设计基于注意力机制的Encoder-Decoder移动用户轨迹预测模型和基于长短期记忆(Long Short-Term Memory,LSTM)网络的虚拟网络功能(Virtual Network Function,VNF)实例资源需求预测模型,分别准确预测用户移动轨迹和节点负载;基于预测结果提出SFC主动重构(Predict-based SFC Active Reconfiguration,PSAR)启发式算法,确保在服务质量(Quality of Service,QoS)下降之前,提前完成VNF迁移和路由更新,实现SFC的主动重构和无缝迁移,保证网络的一致性高质量服务。仿真结果表明,所提算法有效降低了SFC端到端时延和重构成本。展开更多
随着智能电网系统中移动终端的增加,其对传输数据低时延、大带宽和高可靠性的需求尤为紧迫。为解决其中无线传输、信息处理和可靠性不足等问题,文章采用“切片分组网(sliced packet network,SPN)+可信无线局域网(wireless local area ne...随着智能电网系统中移动终端的增加,其对传输数据低时延、大带宽和高可靠性的需求尤为紧迫。为解决其中无线传输、信息处理和可靠性不足等问题,文章采用“切片分组网(sliced packet network,SPN)+可信无线局域网(wireless local area network,WLAN)”通信新技术网络架构,建立多种移动终端设备安全无线传输和计算任务卸载的总时延优化卸载模型,提出一种基于交替优化技术的算法。仿真结果表明,该策略不仅保证设备安全高效地接入网络,还可显著降低传输时延,具有优异的成本效益。展开更多
基金supported by the National Natural Science Foundation of China(No.62071354)the Key Research and Development Program of Shaanxi(No.2022ZDLGY05-08)supported by the ISN State Key Laboratory。
文摘To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2022JBGP003in part by the National Natural Science Foundation of China(NSFC)under Grant 62071033in part by ZTE IndustryUniversity-Institute Cooperation Funds under Grant No.IA20230217003。
文摘This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.
文摘Multi-user detection techniques are currently being studied as highly promising technologies for improving the performance of unsourced multiple access systems. In this paper, we propose joint multi-user detection schemes with weighting factors for unsourced multiple access. First, we introduce bidirectional weighting factors in the extrinsic information passing process between the multi-user detector based on belief propagation (BP) and the LDPC decoder. Second, we incorporate bidirectional weighting factors in the message passing process between the MAC nodes and the user variable nodes in BP- based multi-user detector. The proposed schemes select the optimal weighting factors through simulations. The simulation results demonstrate that the proposed schemes exhibit significant performance improvements in terms of block error rate (BLER) compared to traditional schemes. .
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant No.62071306in part by Shenzhen Science and Technology Program under Grants JCYJ20200109113601723,JSGG20210802154203011 and JSGG20210420091805014。
文摘In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.
文摘在移动边缘计算的物联网(Mobile Edge Computing-enabled Internet of Things Networks,IoT-MEC)中,物联终端的高移动性、服务请求的随机到达性以及网络流量的实时变化,导致原有应用场景下的资源配置与服务部署不再完全匹配。如何有效利用网络提供的资源以实现服务功能链(Service Function Chain,SFC)的实时部署和重构是一个重要的挑战。针对用户的高移动性和网络流量的实时变化造成的SFC性能需求和已分配资源不匹配的问题,提出IoT-MEC网络中基于用户移动和资源需求预测的SFC重构策略。建立以SFC的端到端时延和重构成本最小化为目标的整数线性规划模型;设计基于注意力机制的Encoder-Decoder移动用户轨迹预测模型和基于长短期记忆(Long Short-Term Memory,LSTM)网络的虚拟网络功能(Virtual Network Function,VNF)实例资源需求预测模型,分别准确预测用户移动轨迹和节点负载;基于预测结果提出SFC主动重构(Predict-based SFC Active Reconfiguration,PSAR)启发式算法,确保在服务质量(Quality of Service,QoS)下降之前,提前完成VNF迁移和路由更新,实现SFC的主动重构和无缝迁移,保证网络的一致性高质量服务。仿真结果表明,所提算法有效降低了SFC端到端时延和重构成本。
文摘随着智能电网系统中移动终端的增加,其对传输数据低时延、大带宽和高可靠性的需求尤为紧迫。为解决其中无线传输、信息处理和可靠性不足等问题,文章采用“切片分组网(sliced packet network,SPN)+可信无线局域网(wireless local area network,WLAN)”通信新技术网络架构,建立多种移动终端设备安全无线传输和计算任务卸载的总时延优化卸载模型,提出一种基于交替优化技术的算法。仿真结果表明,该策略不仅保证设备安全高效地接入网络,还可显著降低传输时延,具有优异的成本效益。
