|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | 面向自动驾驶的边缘计算技术研究综述显示文摘边缘计算在自动驾驶的环境感知和数据处理方面有着极其重要的应用。自动驾驶汽车可以通过从边缘节点获得环境信息来扩大自身的感知范围,也可以向边缘节点卸载计算任务以解决计算资源不足的问题。相比于云计算,边缘计算避免了长距离数据传输所导致的高时延,能给自动驾驶车辆提供更快速的响应,并且降低了主干网络的负载。基于此,首先介绍了基于边缘计算的自动驾驶汽车协同感知和任务卸载技术及相关挑战性问题,然后对协同感知和任务卸载技术的研究现状进行了分析总结,最后讨论了该领域有待进一步研究的问题。 | 吕品 许嘉 李陶深 徐文彪 | 2021 | 通信学报2021，42，3： | 16 |
| 2 | 人工智能在5G系统中的应用综述显示文摘随着5G的不断发展,万物互联时代即将来临。海量设备连接、海量业务请求、超高网络负载、复杂动态的网络环境等对5G系统优化提出了巨大的挑战。面对这些技术难点,人工智能(AI)算法表现了其独特的优势。首先对5G系统中基于深度学习的AI算法相比于传统算法的优势进行介绍;随后,针对多接入边缘计算和毫米波大规模多输入多输出(mmWave massive MIMO)系统中的AI算法应用进行详细的阐述,并对比分析了各种方法的优劣;最后,根据已有研究,总结了AI算法在5G实际场景中存在的不足,并对未来研究方向提出了展望。 | 章坚武 王路鑫 孙玲芬 章谦骅 单杭冠 | 2021 | 电信科学2021，37，5： | 13 |
| 3 | Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT显示文摘In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments.Federated learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy.However,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still under-explored.In this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular IoT.The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with environments.The clients also work as forwarder nodes in information sharing among network entities.The client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking architecture.We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning. | Wugedele Bao Celimuge Wu Siri Guleng Jiefang Zhang Kok-Lim Alvin Yau Yusheng Ji | 2021 | China Communications2021，18，6： | 3 |
| 4 | AI算法在车联网通信与计算中的应用综述显示文摘在5G时代,车联网的通信和计算发展受到信息量急速增加的限制。将AI算法应用在车联网,可以实现车联网通信和计算方面的新突破。调研了AI算法在通信安全、通信资源分配、计算资源分配、任务卸载决策、服务器部署、通算融合等方面的应用,分析了目前AI算法在不同场景下所取得的成果和存在的不足,结合车联网发展趋势,讨论了AI算法在车联网应用中的未来研究方向。 | 康宇 刘雅琼 赵彤雨 寿国础 | 2023 | 电信科学2023，39，1： | 3 |
| 5 | 深度强化学习与移动通信资源管理:算法、进展与展望显示文摘深度强化学习(deep reinforcement learning,DRL)将深度学习从高维数据提取低维特征的能力与强化学习的决策能力相结合,是移动通信资源管理与优化的高效算法之一.在引入DRL相关算法概念与原理的基础上,重点对DRL在网络切片、云计算、雾计算、移动边缘计算等通信技术与场景中的资源管理与优化效果进行综述与分析,结合DRL在移动通信资源管理的算法原理与研究进展,论述了DRL面临的问题与挑战,并提出相应解决思路.最后,展望了DRL在移动通信资源管理领域的发展趋势和主要研究方向. | 孙恩昌 袁永仪 吴兵 屈晗星 张延华 | 2023 | 北京工业大学学报2023，49，1： | 2 |
| 6 | Performance Analysis for Large Intelligent Surface Assisted Vehicular Networks显示文摘Large intelligent surface(LIS)is considered as a new solution to enhance the performance of wireless networks[1].LIS comprises low-cost passive elements which can be well controlled.In this paper,a LIS is invoked in the vehicular networks.We analyze the system performance under Weibull fading.We derive a novel exact analytical expression for outage probability in closed form.Based on the analytical result,we discuss three special scenarios including high SNR case,low SNR case,as well as weak interference case.The corresponding approximations for three cases are provided,respectively.In order to gain more insights,we obtain the diversity order of outage probability and it is proved that the outage probability at high SNR depends on the interference,threshold and fading parameters which leads to 0 diversity order.Furthermore,we investigate the ergodic achievable rate of LIS-assisted vehicular networks and present the closed-form tight bounds.Similar to the outage performance,three special cases are studied and the asymptotic expressions are provided in simple forms.A rate ceiling is shown for high SNRs due to the existence of interference which results 0 high SNR slope.Finally,we give the energy efficiency of LIS-assisted vehicular network.Numerical results are presented to verify the accuracy of our analysis.It is evident that the performance of LIS-assisted vehicular networks with optimal phase shift scheme exceeds that of traditional vehicular networks and random phase Received:Aug.6,2020 Revised:Nov.17,2020 Editor:Caijun Zhong shift scheme significantly. | Yiyang Ni Yaxuan Liu Jin Zhou Qin Wang Haitao Zhao Hongbo Zhu | 2021 | China Communications2021，18，3： | 1 |
| 7 | 车联网边缘智能:概念、架构、问题、实施和展望显示文摘作为一项新兴交叉学科领域,边缘智能通过将人工智能推送至靠近交通数据源侧,并利用边缘算力、存储资源及感知能力,在提供实时响应、智能化决策、网络自治的同时,赋能更加智能、高效的资源调配与处理机制,从而实现车联网从接入“管道化”向信息“智能化”使能平台的跨越。然而,当前边缘智能于车联网领域的成功实施仍处于起步阶段,迫切需要以更为广阔的视角对这一新兴领域进行全面综述。为此,面向车联网应用场景,首先介绍边缘智能的背景、概念及关键技术;然后,对车联网应用场景中基于边缘智能的服务类型进行整体概述,同时详细阐述边缘智能模型的部署和实施过程;最后,分析边缘智能于车联网中的关键开放性挑战,并探讨应对策略,以推动其潜在研究方向。 | 江恺 曹越 周欢 任学锋 朱永东 林海 | 2023 | 物联网学报2023，7，1： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员