Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless connectivity.It also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays,Unmanned Aerial Vehicles(UAVs)are a significant part of the mobile network due to their continuously growing use in various applications.For better coverage,cost-effective,and seamless service connectivity and provisioning,UAVs have emerged as the best choice for telco operators.UAVs can be used as flying base stations,edge servers,and relay nodes in mobile networks.On the other side,Multi-access EdgeComputing(MEC)technology also emerged in the 5G network to provide a better quality of experience(QoE)to users with different QoS requirements.However,UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing,path planning,optimization,QoS assurance,mobilitymanagement,etc.The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging.So,an automated Artificial Intelligence(AI)enabled QoSaware solution is needed for trajectory planning and optimization.Therefore,this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network.It has an efficient Deep Reinforcement Learning(DRL)algorithm for real-time and proactive trajectory planning and optimization.It also fulfills QoS-aware service provisioning.A greedypolicy approach is used to maximize the long-term reward for serving more users withQoS.Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

Multi-access performance of DS UWB systemunder indoor dense multi-path channel

The performance of multi-user code to direct spreading bi-phase shift keying （DS-BPSK） direct impulse ultra wideband （UWB） systems under indoor multi-user and multi-path environment is analyzed and simulated. The system output signals with Rake receiver are derived, then a simple and practical code selection scheme is given; i. e., with a large occupation to empty ratio of the repeating pulses, directly choosing those random or pseudo-random user codes with enough length and good co-relative orthogonal features will make the performance of DS-BPSK approximate the optimum and, so there is no need to carefully design the code or its type. The system multi-access performances are simulated using Gold sequence and PN codes as multi-user codes under CMI-CM4 multi-path channels. Simulation results prove that the proposed scheme is feasible.

Deep Reinforcement Learning-Based Computation Offloading for 5G Vehicle-Aware Multi-Access Edge Computing Network

Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios.Meanwhile,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to appear.Compared with traditional Internet business,these computation tasks have higher processing priority and lower delay requirements.In this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network(VAMECN)and propose a joint optimization problem of minimizing total system cost.In view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication bandwidth.And,the mixed integer nonlinear programming problem is described as a Markov Decision Process.Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.

Intelligent Immunity Based Security Defense System for Multi-Access Edge Computing Network

In this paper,the security problem for the multi-access edge computing(MEC)network is researched,and an intelligent immunity-based security defense system is proposed to identify the unauthorized mobile users and to protect the security of whole system.In the proposed security defense system,the security is protected by the intelligent immunity through three functions,identification function,learning function,and regulation function,respectively.Meanwhile,a three process-based intelligent algorithm is proposed for the intelligent immunity system.Numerical simulations are given to prove the effeteness of the proposed approach.

Integration of Communication and Computing in Blockchain-Enabled Multi-Access Edge Computing Systems

Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and management mechanism,blockchain technology achieves the reliable transmis-sion of data and value.While as a new computing paradigm,multi-access edge computing enables the high-frequency interaction and real-time transmission of data.The integration of communication and com-puting in blockchain-enabled multi-access edge com-puting networks has been studied without a systemat-ical view.In the survey,we focus on the integration of communication and computing,explores the mu-tual empowerment and mutual promotion effects be-tween the blockchain and MEC,and introduces the resource integration architecture of blockchain and multi-access edge computing.Then,the paper sum-marizes the applications of the resource integration ar-chitecture,resource management,data sharing,incen-tive mechanism,and consensus mechanism,and ana-lyzes corresponding applications in real-world scenar-ios.Finally,future challenges and potentially promis-ing research directions are discussed and present in de-tail.

A Review in the Core Technologies of 5G: Device-to-Device Communication, Multi-Access Edge Computing and Network Function Virtualization

5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.

New strategies for collision resolution of multi-access channel

The truncated binary exponential back-off algorithm is one of the most effective methods applied in collision resolution process of random multi-access channel.In this study,two new strategies are presented to improve the capability of the truncated binary exponential back-off algorithm.In the new strategies,the sizes of the initial window size or the operating window sizes are adjusted dynamically,which always bring a significant improvement for the self-adaptability of the original algorithm.A series of experiments are simulated and the results verify that the new strategies can make the implementation more stable and effective than the original algorithm.

A Random Multi-Access Method for Data Services in CDMA Cellular System

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基于高空平台的边缘计算卸载:网络、算法和展望

高空平台(high altitude platform,HAP)技术与多接入边缘计算(multi-access edge computing,MEC)技术的结合将MEC服务器部署区域由地面扩展到空中,打破传统地面MEC网络的局限性,为用户提供无处不在的计算卸载服务。针对基于HAP的MEC卸载研究进行综述,首先,从HAP计算节点的优势、网络组成部分、网络结构、主要挑战及其应对技术4个方面分析基于HAP的MEC网络;其次,分别从图论、博弈论、机器学习、联邦学习等理论的角度对基于HAP的MEC卸载算法进行横向分析和纵向对比;最后,指出基于HAP的MEC卸载技术目前存在的问题,并对该技术的未来研究方向进行展望。

Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction

The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

重型施工机械安拆吊装作业视觉可达性BIM仿真分析

为提高重型施工机械安拆吊装作业安全工效,改善安拆作业过程中人员的可视性,提出基于可视锥法的多主体视觉可达性建筑信息模型(BIM)仿真方法。首先,通过重型施工机械视觉任务分解,得到重型施工机械多主体视觉任务;其次,以wk-35型电铲安拆作业为研究对象,分析起重驾驶司机-信号工-司索工多主体视觉任务,运用计算机辅助三维交互应用(CATIA)人因仿真模块,构建安拆吊装作业多主体视觉的BIM仿真场景;然后,采用可视锥法,计算安拆吊装作业过程多主体视觉可达性量值,测度多主体视觉可达性水平,构建重型机械施工机械多主体视觉评价模型;最后,调节BIM模型的过程参数,推演多主体视觉变化趋势,评价安拆吊装作业多主体视觉可达性。结果表明:安拆作业中起重司机在起升下降过程中视觉可达性较差,在平移过程中视觉可达性较好;信号工和司索工的位置与其视觉可达性联系紧密,当信号工处于视野刚好覆盖起重司机和司索工的位置时,其视觉可达性最好,而司索工需不断调整位置,使其视觉可达性达到最佳。

基于可达性的多层动态航空网络鲁棒性分析

为更准确地描述航空网络以分析其鲁棒性,提出1种以同一始发机场及其离场航班为子网络层,随出发时刻动态变化的多层动态航空网络模型。针对我国国内(不含港澳台)多层动态航空网络,以可达性、度中心性和加权介数中心性为节点特征指标,网络效率为网络性能测度指标,从随机攻击和蓄意攻击2种策略下对网络进行鲁棒性分析。研究结果表明:我国国内(不含港澳台)航空网络的鲁棒性主要由20%的机场提供,且在不同时间段内鲁棒性不同,在2023年夏秋航季,7∶00~20∶00间鲁棒性较好,4∶00~7∶00和20∶00~24∶00鲁棒性较差;用可达性指标衡量航空网络中机场节点的重要程度比度中心性和加权介数中心性指标更准确。研究结果可为保障航空运输网络安全提供一定参考。

校企共建多网融合下的“5G+智慧校园”研究

以5G技术为信息化新基建载体,设计“5G+智慧校园”的实施框架。从运用5G技术全面赋能高校智慧校园实现共建、共融、共享为切入点,剖析智慧校园视域下5G智慧应用的现实需求,分析“5G+智慧应用”的作用与研究意义。结合某高校 “5G+智慧应用”多类应用场景实践,从部署模式、分流及认证、信息安全保障三个方面探究校园5G专网模式下基建新生态关键技术方案,为构筑泛在接入、多网融合、部署灵活、安全可控的5G创新应用提供支撑。

大型实验室网络敏感数据访问认证方法仿真

由于大型实验室网络中的敏感数据认证过程繁琐复杂,且对其安全性要求较高,增加用户访问敏感数据的复杂性和时间成本。为此,提出一种大型实验室网络敏感数据访问多阶段认证算法。利用互补集合经验模态分解(Coplementary Ensemble Empirical Mode Decomposition,CEEMD),分解含有噪声的大型实验室网络数据,获取不同本征模态函数,通过改进小波阈值去噪,重构本征模态函数(Intrinsic Mode Functions,IMF)信息,获取降噪处理后的数据。通过Apriori算法和互信息理论,分析不同数据之间的相关性,挖掘其关联性,实现大型实验室网络敏感数据访问多阶段认证。通过仿真分析证实,采用所提算法可以精准完成大型实验室网络敏感数据访问多阶段认证,准确性在90%以上。

可达性对城市群多模式交通碳排放的空间异质性影响

当前,在交通行业碳减排存在巨大挑战以及未来中国城市群交通发展长远规划双重背景下,如何通过改善交通可达性提高居民出行效率和减少碳排放是亟待解决的关键问题之一。本文基于城际出行手机信令数据,从居民出行的角度提出城际多模式交通客运碳排放量方法,并采用梯度提升决策树(GBDT)模型及多尺度地理加权回归(MGWR)模型探讨可达性对区域碳排放量的空间异质性影响。以关中平原城市群为例进行验证,结果表明:城际公路客运碳排放量远大于铁路,呈现沿交通基础设施线路分布的特征;在整体区域范围内,可达性指标对碳排放水平具有一定的正向边际效应;MGWR能够刻画碳排放与可达性指标关系的空间异质性及尺度差异;经济潜能可达性、介数中心性及接近中心性对城际碳排放具有显著的正向空间异质性影响,但影响尺度不同;公路客运碳排放对介数中心性及接近中心性要素较为敏感,经济潜能对碳排放的影响较为平稳;铁路出行可达性的提升对中心城市的影响效应低于周边区县城市。

多无人机辅助MEC环境中基于Wardrop路由博弈的计算卸载

无人机(Unmanned Aerial Vehicles,UAVs)与多接入边缘计算(Multi-access Edge Computing,MEC)技术的结合突破了传统地面通信的局限性,已成为解决MEC中任务卸载问题的重要手段。由于单无人机可提供的计算资源和能量有限,为了应对日益扩大的网络规模,考虑了多无人机辅助MEC环境中的任务卸载问题。基于问题定义,任务卸载过程可以视为一个在平行链路上进行的、具有玩家特定延迟函数的Wardrop路由博弈,目的是得到均衡状态和最优状态下的卸载策略,并量化分析两者间的差距。由于均衡解难以计算,因此构造了一个新的势函数,将均衡问题转换成最小化势函数问题。同时使用Frank-Wolfe算法最终获得均衡和最优卸载策略。算法在每次迭代中将目标函数线性化,通过求解线性规划得到可行方向,进而沿此方向在可行域内作一维搜索。仿真实验表明,相比其他基准测试方法,基于平行链路Wardrop路由博弈的均衡卸载策略能够有效降低模型总成本,且与最优卸载策略下总成本的比值约为1。

Analysis of Multi-Channel and Slotted Random Multi-Access Protocol with Two-Dimensional Probability for Ad Hoc Network

A higher quality of service （QoS） is provided for ad hoc networks through a multi-channel and slotted random multi-access （MSRM） protocol with two-dimensional probability. For this protocol, the system time is slotted into a time slot with high channel utilization realized by the choice of two parameters p1 and p2, and the channel load equilibrium. The protocol analyzes the throughput of the MSRM protocol for a load equilibrium state and the throughput based on priority. Simulations agree with the theoretical analysis. The simulations also show that the slotted-time system is better than the continuous-time system.

多接入边缘计算赋能的AI质检系统任务实时调度策略

AI质检是智能制造的重要环节,其设备在进行产品质量检测时会产生大量计算密集型和时延敏感型任务。由于设备计算能力不足,执行检测任务时延较大,极大影响生产效率。多接入边缘计算(MEC)通过将任务卸载至边缘服务器为设备提供就近算力,提升任务执行效率。然而,系统中存在信道变化和任务随机到达等动态因素,极大影响卸载效率,给任务调度带来了挑战。该文面向多接入边缘计算赋能的AI质检任务调度系统,研究了联合任务调度与资源分配的长期时延最小化问题。由于该问题状态空间大、动作空间包含连续变量,该文提出运用深度确定性策略梯度(DDPG)进行实时任务调度算法设计。所设计算法可基于系统实时状态信息给出最优决策。仿真结果表明,与基准算法相比,该文所提算法具有更好的性能表现和更小的任务执行时延。