面向卫星通信的6G通感算融合架构、技术与挑战

为了适应第六代移动通信(6G)技术在卫星通信领域的发展需求,亟需将感知、通信、计算(通感算)融合一体化,兼顾6G通信对于极低时延、极高带宽、极低功耗和极高算力的要求,发展通感算融合理论和关键技术。卫星通信凭借其覆盖面积大、成本低和算力强的特点,成为6G通感算融合的关键场景,其通过边缘计算、联邦学习等技术构建6G通感算融合网络,设计实现一体化终端功能结构,从而有效提升系统性能。目前面向卫星通信中的通感算融合研究还处于起步阶段,架构与关键技术的相关研究都在加速进行中。基于此,首先对6G通感算背景和研究现状进行介绍;然后提出面向卫星通信的6G通感算架构,概述其系统功能模块构成和关键技术;接着提出基于联邦学习的卫星通感算融合架构,并详细阐述架构组成和性能指标;最后探讨了面向卫星通信的6G通感算融合面临的挑战和未来的发展趋势。     

基于深度强化学习的边缘视频任务卸载策略

随着物联网的发展以及智能设备的普及,视频处理技术已广泛应用于生活中。自动驾驶、产品质检等应用场景对视频处理技术的实时性需求逐步提高,移动边缘计算为计算能力不足和能源受限的设备提供计算资源以执行时延敏感性任务,为实时视频处理提供了新的计算架构。本文搭建了一个视频计算卸载场景,并以视频检测为任务,以系统时延为优化目标,建立了计算卸载模型和马尔可夫决策模型;考虑到计算卸载场景的复杂动态因素,如带宽波动、设备数量、任务大小等,以最小化系统时延为目标,提出了一种基于深度强化学习的计算卸载策略进行求解。实验表明,与其他基线方案相比,该卸载策略能够适应较复杂卸载场景,有效降低系统时延。     

大规模无线通信网络移动边缘计算和缓存研究

面向未来6G移动通信的大规模网络移动边缘计算与缓存技术,首先,介绍了大规模无线网络下移动边缘计算和缓存的架构与原理,并阐释了移动边缘计算和缓存技术在大规模无线网络中的必要性和普适性。接着,从计算卸载、边缘缓存、多维资源分配、用户关联和隐私保护这5个关键问题出发,综述和分析了移动边缘计算和缓存赋能大规模无线网络时会引入的新型关键问题以及对应的解决方案研究,并进一步指出了未来的发展趋势和研究方向。最后,针对隐私保护问题,提出了一种基于联邦学习的隐私保护方案,并通过仿真结果表明所提方案能够同时保护用户数据隐私且改善系统服务质量。     

智慧城轨交通通信技术的分析与展望

为解决由城轨环境特殊性导致的通信可靠性、时延性能降低以及运营效率瓶颈等问题,首先从整体运营控制的角度分析了城轨通信需求。结合新型智能运行控制技术及T2T(train to train,列车到列车)通信、5G、人工智能、移动边缘计算等新兴信息技术对城轨通信关键技术进行梳理、总结与展望;然后提出了一种新型城轨通信网络架构,最后探讨了智慧城轨通信技术的研究方向与面临的挑战,为智慧城市的发展提供研究基础。     

移动边缘计算中的智能任务迁移技术:现状与展望

与传统边缘计算相比,移动边缘计算(Mobile Edge Computing,MEC)技术能更好地解决移动设备资源受限问题,因而备受关注。而移动场景的高动态性,又对MEC的任务调度能力提出了挑战。为了应对这种挑战,将人工智能技术与MEC技术相结合已经成为一种新的发展趋势。首先,介绍了MEC技术的发展背景,然后详细说明MEC中的任务迁移技术和研究现状,最后展望了MEC和人工智能技术融合的发展方向,并对结合了深度强化学习技术的MEC技术进行仿真。结果表明,结合了深度强化学习的MEC系统在服务时延以及系统容量性能上都有着较大提升。     

可信区块链联邦学习技术在无线通信网中的研究与应用

随着无线通信技术的快速发展，越来越多的数据产生并在网络中传输，包括个人隐私信息和商业机密等敏感数据。数据安全和隐私保护引发通信行业的广泛关注，并已成为人工智能大规模落地的关键挑战点。可信区块链联邦学习从保障信息安全的角度发展人工智能，被誉为最有前景解决这一挑战的新方向。归纳了当前行业面临的关键问题和挑战，分析了可信区块链联邦学习的国内外主要研究状况。讨论了无线通信网中典型的区块链联邦学习的系统架构，并阐述了其系统功能和实现的关键技术。在此基础上，给出该系统下的典型应用场景，为可信人工智能从理论走向实用提供了可能性。     

面向高效通信边缘学习网络的通信计算一体化设计

面向边缘学习网络,探讨了一种新型的基于空中计算的模型聚合方案,并对其中的关键使能技术展开论述。该方案利用无线多址信道的波形叠加特性将通信与计算在空中无缝融合,能够突破现有的通信-计算分离设计框架的局限性,从而大大提高频谱利用率,缓解了制约联邦式边缘学习大规模扩展的通信时延问题。     

移动边缘计算的计算卸载技术分析研究

计算卸载作为移动边缘计算的关键技术之一,对于提升移动边缘计算实现节能、降低时延和改善用户体验等方面,起到关键的作用.本文围绕移动边缘计算的计算卸载技术进行分析研究,首先介绍了计算卸载概念和主要特征;并就移动边缘计算的计算卸载实施步骤和计算卸载系统分类进行阐述;然后针对计算卸载关键技术中的3个重点研究问题进行了详细分析;最后,对研究工作进行总结.     

移动边缘计算的计算卸载技术分析研究

计算卸载作为移动边缘计算的关键技术之一,对于提升移动边缘计算实现节能、降低时延和改善用户体验等方面,起到关键的作用.本文围绕移动边缘计算的计算卸载技术进行分析研究,首先介绍了计算卸载概念和主要特征;并就移动边缘计算的计算卸载实施步骤和计算卸载系统分类进行阐述;然后针对计算卸载关键技术中的3个重点研究问题进行了详细分析;最后,对研究工作进行总结.     

基于联盟链的可靠边缘计算任务卸载方法

随着移动终端尤其是工业互联网技术的快速发展,终端设备密集分布,无线带宽有限,经常导致业务过程中的集中式云资源调度,难以满足远程终端应用对低时延和低成本计算的需求.着眼于本地服务器联动云数据中心,边缘计算为这类移动应用提供了一种敏捷的计算服务模式.虽然边缘计算的敏捷服务模式能够有效缩短移动应用的时延并降低对应的通信成本,但在边缘计算环境下,异构资源之间的任务卸载经常会导致一些潜在的数据安全隐患和服务质量受损.针对上述应用挑战和技术发展趋势,本文提出了一种基于联盟链的可靠边缘计算任务卸载方法 .该方法利用联盟链进行身份校验和卸载结果反馈,以任务的完成时间、卸载成本与资源可靠度作为评价标准,设计了一种基于遗传算法的卸载策略,支持卸载决策时任务卸载的可靠性评估.实验结果表明,本文方法能在满足时延约束的前提下提高任务卸载的可靠性,为移动智能应用提供了一种有效的数据安全保障方法.     

Overview on intelligent wireless communication technology

In recent years,artificial intelligence (AI) has been applied to wireless communications,in order to address the challenges introduced by data explosion and Internet of everything.Firstly,three core technologies of AI were introduced,including deep learning,deep reinforcement learning,and federated learning.Then,an overview of their applications on wireless communications was provided,from the aspects of wireless transmission,spectrum management,resource allocation,network access,network and system optimization.Based on the overview,the principle,applicability,design methodology,pros and cons on applying AI technologies to solve wireless communication problems were analyzed and summarized.Regarding the existed limitations,the future development trends and research directions on intelligent wireless communication technologies were pointed out,to hopefully provide useful help and reference for the future research in this field.     

Artificial Intelligence-Empowered Resource Management for Future Wireless Communications: A Survey

How to explore and exploit the full potential of artificial intelligence(AI)technologies in future wireless communications such as beyond 5G(B5G)and 6G is an extremely hot inter-disciplinary research topic around the world.On the one hand,AI empowers intelligent resource management for wireless communications through powerful learning and automatic adaptation capabilities.On the other hand,embracing AI in wireless communication resource management calls for new network architecture and system models as well as standardized interfaces/protocols/data formats to facilitate the large-scale deployment of AI in future B5G/6G networks.This paper reviews the state-of-art AI-empowered resource management from the framework perspective down to the methodology perspective,not only considering the radio resource(e.g.,spectrum)management but also other types of resources such as computing and caching.We also discuss the challenges and opportunities for AI-based resource management to widely deploy AI in future wireless communication networks.     

AI技术在卫星通信/互联网领域的应用综述

卫星互联网的诞生带来新的发展机遇，同时也具备诸多挑战。主要调研了在空天地一体化的背景下，人工智能（AI）技术作为重要的辅助工具在卫星通信/卫星互联网领域的应用，涉及通信抗干扰、通信路由、星地网络系统架构、星座运营与管理等场景，其中涉及的AI算法包括传统机器学习、深度学习、强化学习等；最后，针对AI技术在卫星通信/互联网领域的发展趋势，提出了未来的研究方向，为我国卫星领域的智能化发展提供了思路和技术方案。     

Topics in automotive networking

Significant research efforts in industry and academia have been underway to accelerate the development of intelligent transportation systems (ITS) for safe, efficient, and convenient driving. Much of the research effort has been devoted to meet the challenges of effectively integrating wireless communications, computing, and advanced sensor technologies into vehicular and transportation systems. To date, the Automotive Networking Series has published articles that address such challenges, including characterization of vehicular communication channels and development of wireless system technologies; design of protocols for vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) networking that adapt to changes of roadway conditions to provide fast, reliable communications; simulation tools to validate designs in realistic roadway scenarios; technologies to achieve security and privacy; and standardization, among others.     

人工智能在安全领域的应用

人工智能的发展和应用给信息安全领域带来了希望和挑战。一方面人工智能在信息安全领域的应用能够显著提升安全防护能力,另一方面人工智能自身也存在着数据安全、对抗欺骗、隐私保护、动态环境适应等安全问题。本文分析了信息安全领域对人工智能的需求以及相关应用场景,分析了人工智能面临的安全挑战,提出了人工智能安全应用及安全防护建议,旨在推动信息通信及网络信息安全行业对人工智能带来的安全挑战达成充分认识,促进人工智能在行业的安全、可靠、可控发展。     

Energy Efficient Multi-hop Cooperative Transmission Protocol for Large Scale Mobile Ad hoc Networks

A great advancement has been made in intelligent transportation system and communication technologies in order to exchange secure information between automobiles, facilities provider have led an frame over road network. The intelligent transportation system provides an efficient traffic system for drivers, so that there must be less risk to users. In order to design a secure communication protocol among V & V and V & I is a challenging problem. In order to reduce the chance of attacks and increase privacy level, crypto graphic tools provides the feasible solution. In this paper, we proposed pseudonym changing strategy with mix zones (1) Anonymous authentication: the message should be authenticated by issuer due to mix zone and cryptographic tools secure message without any attack. (2) Privacy: Communication contents are confidential due to encrypted messages during communications also improving the scalability through address configuration scheme to reduce computational cost. (3) Efficiency: low storage requirements, The velocity and distance factors may also consider secure measurement, message delivery, overhead and coverage,packet delivery rate, reduce latency and overhead not only by computation cost and time but also compared our scheme, fast delivery rate, low latency and maximum coverage in order to enhance privacy against malicious attacks.

     

BDN-GWMNN: Internet of Things (IoT) Enabled Secure Smart City Applications

Nowadays, next-generation networks such as the Internet of Things (IoT) and 6G are played a vital role in providing an intelligent environment. The development of technologies helps to create smart city applications like the healthcare system, smart industry, and smart water plan, etc. Any user accesses the developed applications; at the time, security, privacy, and confidentiality arechallenging to manage. So, this paper introduces the blockchain-defined networks with a grey wolf optimized modular neural network approach for managing the smart environment security. During this process, construction, translation, and application layers are created, in which user authenticated based blocks are designed to handle the security and privacy property. Then the optimized neural network is applied to maintain the latency and computational resource utilization in IoT enabled smart applications. Then the efficiency of the system is evaluated using simulation results, in which system ensures low latency, high security (99.12%) compared to the multi-layer perceptron, and deep learning networks.

     

基于深度学习的无线通信接收方法研究进展与趋势

随着无线通信应用边界的不断扩展,无线通信应用环境也日趋复杂多样,面临射频损伤、信道衰落、干扰和噪声等负面影响,给接收端恢复原始信息带来挑战。借鉴深度学习方法在计算机视觉、模式识别、自然语言处理等领域取得的研究成果,基于深度学习的无线通信接收技术受到学术界和产业界的广泛关注。首先阐述了国内外基于深度学习无线通信接收技术的研究现状;接着概述了信号大数据背景下无线通信接收所面临的技术挑战,并提出基于深度神经网络的无线通信智能接收参考架构;最后探讨了信号大数据背景下无线通信智能接收方法的发展趋势。为基于深度学习无线通信技术的研究和发展提供借鉴。     

构建智能实时网络,使能5G视频业务繁荣

5G将促进视频业务的大繁荣,包括极大地改善现有的视频业务体验和催生新型的视频服务形态。为应对5G视频业务所面临的超低时延、高可靠及高体验质量等方面的挑战,中兴通讯提出构建智能实时视频网络(SmartRTN)的理念,并围绕这一理念,创新性地研发出一系列技术和方案,包括基于内容智能分析的低码高清视频编码技术、超低时延的网络传输、基于深度学习的内容处理与增强、结合边缘计算以及网络切片的组网方案和智能调度策略等。这些技术和方案被应用于视频业务端到端各个环节,有效地解决了困扰5G视频业务发展的技术瓶颈问题。     

面向5G Advanced的智能边缘网络演进

5G Advanced演进愿景中实时宽带交互(Real Time Broadband Communication,RTBC)、通信感知一体(Harmo?nized Communication and Sensing,HCS)、上行超宽带(Uplink Centric Broadband Communication,UC...