Starlet:Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things.To guarantee the network's overall security,we present a network defense resource allocation with multi-armed bandits to maximize the network's overall benefit.Firstly,we propose the method for dynamic setting of node defense resource thresholds to obtain the defender(attacker)benefit function of edge servers(nodes)and distribution.Secondly,we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of nodes.Subsequently,we use the decomposability and Lipschitz conti-nuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference theoretically.Finally,experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

Resource Allocation based on Shared Criterion in OFDMA Distributed Radio Access Network

Distributed radio access network (DRAN) is a novel wireless access architecture and can solve the problem of the available spectrum scarcity in wireless communications. In this paper, we investigate resource allocation for the downlink of OFDMA DRAN. Unlike previous exclusive criterion based algorithms that allocate each subcarrier to only one user in the system, the proposed algorithms are based on shared criterion that allow each subcarrier to be allocated to multiple users through different antennas and to only one user through same antenna. First, an adaptive resource allocation algorithm based on shared criterion is proposed to maximize total system rate under each user's minimal rate and each antenna's maximal power constraints. Then we improve the above algorithm by considering the influence of the resource allocation scheme on single user. The simulation results show that the shared criterion based algorithm provide much higher total system rate than that of the exclusive criterion based algorithm at the expense of the outage performance and the fairness, while the improved algorithm based on shared criterion can achieve a good tradeoff performance.

A dynamic and resource sharing virtual network mapping algorithm

Network virtualization can effectively establish dedicated virtual networks to implement various network functions.However,the existing research works have some shortcomings,for example,although computing resource properties of individual nodes are considered,node storage properties and the network topology properties are usually ignored in Virtual Network(VN)modelling,which leads to the inaccurate measurement of node availability and priority.In addition,most static virtual network mapping methods allocate fixed resources to users during the entire life cycle,and the users’actual resource requirements vary with the workload,which results in resource allocation redundancy.Based on the above analysis,in this paper,we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE,first,we construct a new,more realistic network framework in which the properties of nodes include computing resources,storage resources and topology properties.In the node mapping process,three properties of the node are used to measure its mapping ability.Second,we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties,so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link mapping.Finally,we divide the resource requirements of Virtual Network Requests(VNRs)into basic subrequirements and variable sub-variable requirements to complete dynamic resource allocation.The former represents monopolizing resource requirements by the VNRs,while the latter represents shared resources by many VNRs with the probability of occupying resources,where we keep a balance between resource sharing and collision among users by calculating the collision probability.Simulation results show that the proposed NMAPRS-VNE can increase the average acceptance rate and network revenue by 15%and 38%,and reduce the network cost and link pressure by 25%and 17%.

Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks

In this paper,maximizing energy efficiency(EE)through radio resource allocation for renewable energy powered heterogeneous cellular networks(HetNet)with energy sharing,is investigated.Our goal is to maximize the network EE,conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources.We define the objective function as a sum weighted EE of all links in the HetNet.We formulate the resource allocation problem in terms of subcarrier assignment,power allocation and energy sharing,as a mixed combinatorial and non-convex optimization problem.We propose an energy efficient resource allocation scheme,including a centralized resource allocation algorithm for iterative subcarrier allocation and power allocation in which the power allocation problem is solved by analytically solving the Karush-Kuhn-Tucker(KKT)conditions of the problem and a water-filling problem thereafter and a low-complexity distributed resource allocation algorithm based on reinforcement learning(RL).Our numerical results show that both centralized and distributed algorithms converge with a few times of iterations.The numerical results also show that our proposed centralized and distributed resource allocation algorithms outperform the existing reference algorithms in terms of the network EE.

Utility-based and fairness-aware radio resource allocation in OFDMA cellular relay networks with traffic prioritization

The combination of orthogonal frequency division multiple access(OFDMA) with relaying techniques provides plentiful opportunities for high-performance and cost-effective networks.It requires intelligent radio resource management schemes to harness these opportunities.This paper investigates the utility-based resource allocation problem in a real-time and non-real-time traffics mixed OFDMA cellular relay network to exploit the potentiality of relay.In order to apply utility theory to obtain an efficient tradeoff between throughput and fairness as well as satisfy the delay requirements of real-time traffics,a joint routing and scheduling scheme is proposed to resolve the resource allocation problem.Additionally,a low-complexity iterative algorithm is introduced to realize the scheme.The numerical results indicate that besides meeting the delay requirements of real-time traffic,the scheme can achieve the tradeoff between throughput and fairness effectively.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Extended Virtual Boundary and Its Application in Dynamic Spectrum Allocation

This paper presents an efficient dynamic spectrum allocation （DSA） scheme in a flexible spectrum licensing environment where multiple networks coexist and interfere with each other. In particular, an extension of virtual boundary concept in DSA is proposed, which is spectrally efficient than the previous virtual boundary concept applied to donor systems only. Here, the same technique is applied to both donor and rental systems so as to further reduce the occurrences where the insertion of guard bands is obligatory and as a result provides better spectral efficiency. The proposed extension improves the spectrum utilization without any compromise on interference and fairness issues.

智慧校园学习环境信息服务资源配置与共享机制研究

随着信息技术的快速进步,智慧校园的构建已成为推进教育现代化的重要途径。本文的主要目标在于研究智慧校园学习环境中信息服务资源的合理配置和共享机制,以提升学习效率、优化教育资源的利用,同时推动学生综合素质的全面提升。通过对智慧校园的背景和发展现状进行深入研究,结合信息技术和教育理论,本文提出了有效的资源配置策略和共享机制,旨在为智慧校园的建设提供有益的指导和启示。

基于算力-能量全分布式在线共享的5G网络负荷管理策略

5G与边缘计算等信息基础设施海量部署造成运营商用电成本上升,需推动边缘网络与电网的能量互动以节能降本。现有研究重点关注边缘网络参与日前经济调度,未考虑可再生能源和网络流量双重随机性造成的网络能量供需不平衡问题。针对强随机环境下的网络负荷管理问题,提出面向虚拟化边缘网络的能量实时管理策略。首先,以网络用能成本最小化为目标,构建联合网络资源管理、储能充放电与能量共享模型。其次,针对未来网络信息未知无法直接求解的问题,提出基于随机对偶次梯度法的在线管理策略。然后,针对资源共享涉及运营商隐私问题,提出全分布式的计算资源与能量协同共享算法。最后,仿真验证表明,所提在线算法在无需先验知识的前提下有效减少了5G边缘网络的用能成本。

基于加权二部图与干扰聚类的D2D通信上下行联合资源分配

设备对设备(D2D, device-to-device)通信是一种能够有效提高蜂窝网络频谱效率的短距离通信技术。联合考虑上下行子信道全频域资源复用,针对蜂窝网络中“多对多”的复杂情况(即一个子信道可以分配给多对D2D用户设备(DUE, D2D user equipment),一对DUE也可以同时使用多个子信道),提出了一种两阶段子信道和功率联合分配方案。第一阶段,提出基于加权二部图匹配的资源分配(WBGM-RA, weighted bipartite graph matching-based resource allocation)算法,将系统中的全部子信道分配给所有蜂窝用户设备(CUE, cellular user equipment),以最大化CUE和速率。第二阶段,提出基于干扰聚类的资源分配(IC-RA, interference clusteringbased resource allocation)算法,根据共享同一子信道的UE之间的干扰关系构建干扰矩阵,在确保DUE不会对CUE造成严重干扰的情况下,将分配给CUE的资源再次分配给DUE,同时调整DUE的发射功率,以最大化系统和速率。该方案形成的新型的上下行信道联合资源分配与多对多信道复用机制,使得DUE的频谱接入机会与网络的总体频谱效率均大幅度提高。仿真结果表明,与现有典型算法相比,该算法可以有效地提高系统和速率,增加系统中通信链路数量并提高DUE接入率。

Sub-channel shared resource allocation for multi-user distributed MIMO-OFDM systems

Well-controlled resource allocation is crucial for promoting the performance of multiple input multiple output orthogonal frequency division multiplexing(MIMO-OFDM) systems. Recent studies have focused primarily on traditional centralized systems or distributed antenna systems(DASs), and usually assumed that one sub-carrier or sub-channel is exclusively occupied by one user. To promote system performance, we propose a sub-channel shared resource allocation algorithm for multiuser distributed MIMO-OFDM systems. Each sub-channel can be shared by multiple users in the algorithm, which is different from previous algorithms. The algorithm assumes that each user communicates with only two best ports in the system. On each sub-carrier, it allocates a sub-channel in descending order, which means one sub-channel that can minimize signal to leakage plus noise ratio(SLNR) loss is deleted until the number of remaining sub-channels is equal to that of receiving antennas. If there are still sub-channels after all users are processed, these sub-channels will be allocated to users who can maximize the SLNR gain. Simulations show that compared to other algorithms, our proposed algorithm has better capacity performance and enables the system to provide service to more users under the same capacity constraints.

数字经济时代NGIT驱动财务共享服务效率的提升机制

数字经济时代,新一代信息技术(NGIT)的蓬勃兴起为实体经济高质量发展提供了新动能。以2012—2022年有正式运营财务共享服务中心的沪深两市A股上市公司为样本,实证分析新一代信息技术驱动财务共享服务的效率提升机制。研究发现:新一代信息技术正向驱动财务共享服务效率,人力资源结构和资源配置水平在新一代信息技术对财务共享服务效率的驱动关系中有中介作用。进一步研究发现:组织结构复杂性弱化了新一代信息技术对财务共享服务效率的驱动关系,组织关系复杂性和组织行为复杂性强化了新一代信息技术对财务共享服务效率的驱动关系,且组织结构复杂性、组织关系复杂性和组织行为复杂性在新一代信息技术对财务共享服务效率的驱动关系中有调节效应。本文的研究有助于强化新一代信息技术对财务共享服务的驱动作用,能为财务共享服务的数智化转型提供决策依据。

面向共享单车调配的带约束游牧算法研究

共享单车调配是优化城市交通资源配置的重要手段,但目前的最优路径调配方法往往对单车系统规模敏感。为此,研究一种分时段、区域间调配的共享单车投放方法,提出了带约束的游牧算法(nomad algorithm with constraints,NCA)求解调配模型的最优解。将单车调配问题建模为以单车流量为约束、以最小化运营损耗为目标的优化问题;提出求解上述模型的NCA算法,预测投放区域单车存量和区域间转移量,相比无约束的原游牧算法,改进了局部搜索和全局寻优策略,优化了部落初定位方法;基于预测的存量和转移量得出分时段区域间单车的调配方案。在上海和纽约相关数据集上的对比实验结果表明,运行时长约为其他方法的15%,租赁需求响应率高于分支定界算法0.15%,单车总数和运营损耗比遗传算法降低了约10%,验证了该方法具有更高的优化效率和用户需求响应率。

税收分成、地方政府行为与企业资源配置效率

税收分成作为分税制财政体制的核心内容,为地方政府提供了更广阔的财政自主权限,不仅深刻影响着地方政府的政策制定和行为选择,还会进一步对企业的资源配置过程产生影响,能在一定程度上引导资源的流动方向。本文使用2010—2021年A股制造业上市公司数据,深入探讨了税收分成如何影响企业资源配置效率这一问题。结果表明,随着地方政府税收分成比例提高,企业的资源配置效率显著提升。异质性分析发现,对于承担的事权较多、政府经济干预较大地区的企业以及国有企业,税收分成改善资源配置效率的作用更明显。从机制分析看,地方政府税收分成比例的提高会降低企业的税收负担、优化营商环境和帮助企业获取信贷资金,最终使企业的资源配置效率提升。本文的研究丰富了税收分成经济效应的评估视角,对优化政府行为以及提升企业资源配置效率提供了参考。

面向星地一体化网络的频谱共享方法研究

星地一体化网络是B5G/6G网络发展的重要趋势,业界正积极推动星地一体化网络的发展。随着卫星业务广泛进入消费市场,L、S等频段的频谱短缺问题日益严重。星、地网络间频谱共享是解决该问题的有效方案之一,但会导致系统间严重干扰。为此,提出一种基于干扰感知的频率资源共享分组方法,该方法在约束地面网络干扰功率的前提下,最大化星地一体化网络的系统效用,并通过局部搜索算法求解非线性整数规划问题。仿真结果表明,所提方案能够有效降低来自地面网络的干扰,并提升频率资源利用率。

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

强化高校科研设施和仪器开放共享管理探析

科研设施和仪器是高校科学研究、学科建设和人才培养的重要基础条件,推进科研设施和仪器开放共享符合国家的要求、社会的需求、高校师生的诉求。讨论了国内高校在科研设施和仪器开放共享工作中面临的使用效益偏低、服务能力较差、仪器使用方向单一等问题,在分析其原因的基础上,结合先进高校的管理经验,从完善管理制度建设、优化共享网络平台、增加实验技术队伍活力等方面进行了探讨,为提升科研设施和仪器开放共享效益,更好地服务于教学、科研和社会提供参考。

共享经济助推共同富裕的逻辑、机制与路径

作为数字时代催生的新经济业态,共享经济的核心形态是所有权与使用权分离,其倡导集约利用、共享发展、遵从以人为本和可持续发展的核心思想。强调供给侧与需求侧的精准匹配,促进生产服务和消费使用的深度融合,实现效率与公平的高效发展。与传统业态相比,共享经济通过共享数据生产资料,有效改变生产关系、调整分配关系,强调机会公平和结果公平,共享经济正在成为推动共同富裕的重要力量。立足于新发展阶段,为充分释放共享经济扩大内需的潜力,应立足于共享经济的时代特征与内在本质,营造共享经济的有效发展环境,优化平台生态,保障用户权益;引导数字经济与实体经济深度融合,以技术创新激活经济新发展点、促进全行业高质量发展;健全数字化公共服务机制,缩小社会差距、实现共同富裕。

差异性的制度化:国家间制度性不平等探源

国家间制度性不平等根源于国家间客观差异被转化为制度安排。因此,以“差异性的制度化”逻辑为核心构建的理论模型,可以有效揭示国家间制度性不平等的生产与再生产原理。首先,通过成员标准、资源分配及制度扩散等微观机制,国家间平等或不平等的制度关系得以生产和再生产;其次,对国家间差异的价值判断,即权势逻辑或责任逻辑,是微观机制能够引发宏观的平等或不平等效应的重要动力;最后,权势逻辑和责任逻辑均以国家实力为基础,为发达国家以责任之名行权势之实提供了便利。结合政治、经济与可持续发展三个维度的简要历史考察,可以证明理论框架的有效性,并揭示国家间制度性不平等的发展趋势,即可持续发展的兴起正在破解国家间制度性不平等。在国际力量对比朝着有利于发展中国家方向发展的背景下,中国积极推动构建人类命运共同体,已经并将继续为破解国家间制度性不平等贡献中国智慧和中国方案。