Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement

In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.

Computing Power Network:A Survey

With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.

Efficient Digital Twin Placement for Blockchain-Empowered Wireless Computing Power Network

As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and untrusted device terminals.Blockchain,as a shared,immutable distributed ledger,provides a secure resource management solution for WCPN.However,integrating blockchain into WCPN faces challenges like device heterogeneity,monitoring communication states,and dynamic network nature.Whereas Digital Twins(DT)can accurately maintain digital models of physical entities through real-time data updates and self-learning,enabling continuous optimization of WCPN,improving synchronization performance,ensuring real-time accuracy,and supporting smooth operation of WCPN services.In this paper,we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models.We adopt an enumeration-based optimal placement algorithm(EOPA)and an improved simulated annealing-based near-optimal placement algorithm(ISAPA)to achieve minimum average DT synchronization latency under the constraint of DT error.Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency.

Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Multi-agent and ant colony optimization for ship integrated power system network reconfiguration

Electric power is widely used as the main energy source of ship integrated power system(SIPS), which contains power network and electric power network. SIPS network reconfiguration is a non-linear large-scale problem. The reconfiguration solution influences the safety and stable operation of the power system. According to the operational characteristics of SIPS, a simplified model of power network and a mathematical model for network reconfiguration are established. Based on these models, a multi-agent and ant colony optimization(MAACO) is proposed to solve the problem of network reconfiguration. The simulations are carried out to demonstrate that the optimization method can reconstruct the integrated power system network accurately and efficiently.

NOMA Empowered Energy Efficient Data Collection and Wireless Power Transfer in Space-Air-Ground Integrated Networks

As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Integrated Power and Single Axis Attitude Control System with Two Flywheels

The existing research of the integrated power and attitude control system（IPACS） in satellites mainly focuses on the IPACS concept,which aims at solving the coupled problem between the attitude control and power tracking.In the IPACS,the configuration design of IPACS is usually not considered,and the coupled problem between two flywheels during the attitude control and energy storage has not been resolved.In this paper,an integrated power and single axis attitude control system using two counter rotating magnetically suspended flywheels mounted to an air table is designed.The control method of power and attitude control using flywheel is investigated and the coupling problem between energy storage and attitude control is resolved.A computer simulation of an integrated power and single axis attitude control system with two flywheels is performed,which consists of two counter rotating magnetically suspended flywheels mounted to an air rotary table.Both DC bus and a single axis attitude are the regulation goals.An attitude ＆ DC bus coordinator is put forward to separate DC bus regulation and attitude control problems.The simulation results of DC bus regulation and attitude control are presented respectively with a DC bus regulator and a simple PD attitude controller.The simulation results demonstrate that it is possible to integrate power and attitude control simultaneously for satellite using flywheels.The proposed research provides theory basis for design of the IPACS.

基于神经网络的PCB电源分配网络阻抗预测方法

针对传统电源分配网络(PDN)建模及计算方法存在的局限性和高计算资源消耗问题,提出了一种基于深度学习的PDN阻抗预测方法(URPNet)。该方法在融合PCB不规则形状、多叠层信息及多种电容端口位置信息的基础上,采用U型编解码结构和残差单元来处理特征,通过多层感知机(MLP)及全连接(FC)层对特征进行解码和重构,从而提升网络的特征处理能力。实验结果显示,URPNet模型的决定系数达到0.999,均方根误差为0.431,相较于现有深度学习方法,URPNet在通用性较强的同时预测结果更准确。此外,其计算速度快,能够在不到1 s的时间内完成预测,可以有效应对PDN设计中的挑战。

Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications,sensing and computing

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources.However,the overwhelming upload traffic may lead to unacceptable uploading time.To tackle this issue,for tasks taking environmental data as input,the data perceived by roadside units(RSU)equipped with several sensors can be directly exploited for computation,resulting in a novel task offloading paradigm with integrated communications,sensing and computing(I-CSC).With this paradigm,vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading.By optimizing the computation mode and network resources,in this paper,we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task.Although this nonconvex problem can be handled by the alternating minimization(AM)algorithm that alternatively minimizes the divided four sub-problems,it leads to high computational complexity and local optimal solution.To tackle this challenge,we propose a creative structural knowledge-driven meta-learning(SKDML)method,involving both the model-based AM algorithm and neural networks.Specifically,borrowing the iterative structure of the AM algorithm,also referred to as structural knowledge,the proposed SKDML adopts long short-term memory(LSTM)networkbased meta-learning to learn an adaptive optimizer for updating variables in each sub-problem,instead of the handcrafted counterpart in the AM algorithm.Furthermore,to pull out the solution from the local optimum,our proposed SKDML updates parameters in LSTM with the global loss function.Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.

“东数西算”背景下算力枢纽节点建设方案研究

算力是数字经济发展的关键基础设施,也是数字时代的核心竞争力,对城市数字经济建设起着至关重要的推动作用。通过研究“东数西算”的现状,综合分析西部地区算力枢纽节点建设的优势与机遇,从“东数西算”关键技术及其应用出发,提出枢纽节点建设方案,并给出“东数西算”工程建设的建议与规划,从而优化数字基础设施布局,提高西部地区的数字经济发展水平,探索可持续发展模式,丰富“东数西算”发展的理论体系。

考虑算力需求的港口综合能源系统分布式能源管理

为应对港口数字化、绿色化转型带来的算力需求急剧增长及新能源发电不确定性挑战,提出了一种考虑算力需求的港口综合能源系统分布式能源管理方法,旨在通过能源调度与协同利用,实现港口区域内电力、热能及算力的综合优化,以最大化经济效益。考虑到港口不同数据负荷下的时间延迟约束及余热回收潜力,建立港口数据中心耗能计算模型;以港口微电网运行成本、数据中心运行成本和热力系统运行成本最小化为目标,考虑港口电力、热能等多种能源形式的交互耦合机制与多样负荷需求约束,构建港口综合能源系统能源管理模型;利用基于对偶分解混合整数线性规划的分布式算法进行求解,以获得综合能源系统的最优能源管理方案,协同推进航运业及道路运输业脱碳行动。仿真分析表明,该方法可以提高港口综合能源系统的运行效率,有效应对算力需求的挑战。

多层次算力网络办公流程多轮任务防冲突调度研究

在多层次算力网络环境中,任务的构成、约束和需求复杂多变,这给任务调度带来极大的挑战。传统策略基于固定规则和算法进行任务分配,难以全面考虑任务间的依赖关系和约束条件,导致资源分配不均、任务执行冲突等问题。因此,文章提出一种多层次算力网络办公流程多轮任务防冲突调度方法。通过从任务构成、任务约束以及任务需求三个角度构建多层次算力网络办公流程多轮任务三元组模型。在调度过程中,引入势博弈机制,综合考虑任务间的依赖关系和约束条件,通过计算效用函数值选择满足任务约束和需求的虚拟机,实现多层次算力资源的高效利用和任务的优化执行。测试结果显示,所提方法显著降低冲突率,即使在任务数量大幅增加时,冲突率增幅也较小,展现出良好的稳定性和低冲突特性。

基于认知OFDM的子载波功率分配改进算法

在认知无线网络中,建立了基于认知OFDM多载波资源分配数学模型,在授权用户干扰受限条件下,以最大化传输速率为目标进行认知用户的子载波功率分配。传统注水法被证明是最优的单用户子载波功率分配算法,在传统注水法功率分配基础上,提出了两种可行的子载波功率分配改进算法,改进算法一是通过对水面值的粗略估计快速确定不分配功率子载波,改进算法二不需要通过迭代计算水面值,只通过线性计算就可以直接确定不分配功率的子载波,且对授权用户不产生干扰。仿真结果表明,在改进的两种子载波功率分配算法下,认知用户的数据传输速率优于传统注水法功率分配时认知用户的数据速率,所提改进算法具有自适应特性且计算复杂度大大降低。

Create Your Own Data and Energy Integrated Communication Network:A Brief Tutorial and a Prototype System

In order to satisfy the ever-increasing energy appetite of the massive battery-powered and batteryless communication devices,radio frequency(RF)signals have been relied upon for transferring wireless power to them.The joint coordination of wireless power transfer(WPT)and wireless information transfer(WIT)yields simultaneous wireless information and power transfer(SWIPT)as well as data and energy integrated communication network(DEIN).However,as a promising technique,few efforts are invested in the hardware implementation of DEIN.In order to make DEIN a reality,this paper focuses on hardware implementation of a DEIN.It firstly provides a brief tutorial on SWIPT,while summarising the latest hardware design of WPT transceiver and the existing commercial solutions.Then,a prototype design in DEIN with full protocol stack is elaborated,followed by its performance evaluation.

基于Android的业务盒的设计与实现

目前,安卓(Android)应用多数为小型软件,业务集成性不强,且安卓市场,91助手等应用商店也只支持一次下载一个应用程序。针对此情况,提出了基于安卓平台的业务盒的应用设想,旨在将安卓应用,壁纸、铃音、书签、快捷方式等业务集成于一个以.zpk为后缀名的资源包,然后将此资源包一次性安装于安卓手机终端。参考美国移动运营商Sprint的Sprint ID Pack项目,并结合国内实际情况,设计并实现了一个基于安卓平台的业务盒,此系统已在安卓手机终端运行通过,从而得到了一个切实可行的多业务集成软件。

UAV-Aided Data and Energy Integrated Network: System Design and Prototype Development

Terminal devices deployed in outdoor environments are facing a thorny problem of power supply.Data and energy integrated network(DEIN)is a promising technology to solve the problem,which simultaneously transfers data and energy through radio frequency signals.State-of-the-art researches mostly focus on theoretical aspects.By contrast,we provide a complete design and implementation of a fully functioning DEIN system with the support of an unmanned aerial vehicle(UAV).The UAV can be dispatched to areas of interest to remotely recharge batteryless terminals,while collecting essential information from them.Then,the UAV uploads the information to remote base stations.Our system verifies the feasibility of the DEIN in practical applications.

基于PSASP和Power Factory的分解协调式输配一体化潮流计算

大量分布式电源的并网改变了配电网单向潮流特性,输电网和配电网之间的耦合更加紧密,电力系统呈现出主配网一体化的特点。文章提出了基于分解协调的主配一体化潮流计算方法,分别采用PSASP和Power Factory进行输电网和配电网的计算,设计了主网和配网潮流的边界协调方程,并采用Jacobian-Free Newton-GMRES(m)方法进行求解。测试结果表明,文章所提方法收敛性较好,计算精度高。

基于多粒度时间卷积网络的超短期风功率预测

针对传统风功率预测方法通常基于固定时间粒进行研究,但该类方法往往忽略了其他时间粒度对风功率的影响的问题,提出一种基于多粒度时间卷积网络(MGTCN)的超短期风功率预测方法,使用时间卷积网络来挖掘多粒度视角下的风力机数据特征,并设计多粒度特征融合模块来增强模型的鲁棒性,提高风功率预测精度。首先,利用随机森林算法(RF)得到与输出功率相关性较强的部分特征数据;然后,对筛选后的特征数据进行多粒度划分,通过时间卷积网络(TCN)提取各个粒度的独立特征。最后,使用挤压激励网络(SENet)对不同粒度特征进行自适应加权融合,得到最终预测值。采用中国某风场数据进行算例分析,结果表明相较于其他方法,所提方法在24步预测任务和6步预测任务上取得了最佳的预测性能,具有较高的准确性和稳定性。在24步预测任务上归一化均方根误差、归一化平均绝对值误差和决定系数指标分别为0.152、0.108和0.7214,在6步预测任务上各指标分别为0.1027,0.0683和0.8717。

Joint Power and Frequency Resource Allocation Algorithm for Integrated Satellite and Terrestrial Networks

In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allowing user equipment(UE)of terrestrial networks to share frequencies with satellite networks.In order to protect the satellite terminal(ST),the base station(BS)needs to control the transmit power and frequency resources of the UE.The optimization problem involves maximizing the achievable throughput while satisfying the interference protection constraints of the ST and the quality of service(QoS)of the UE.However,this problem is highly nonconvex,and we decompose it into power allocation and frequency resource scheduling subproblems.In the power allocation subproblem,we propose a power allocation algorithm based on interference probability(PAIP)to address channel uncertainty.We obtain the suboptimal power allocation solution through iterative optimization.In the frequency resource scheduling subproblem,we develop a heuristic algorithm to handle the non-convexity of the problem.The simulation results show that the combination of power allocation and frequency resource scheduling algorithms can improve spectrum utilization.