Platform Driving The Ultimate Connectivity
Platform Driving The Ultimate Connectivity
批准号:
EP/X04047X/2
负责人:
Harald Haas
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2024
资助国家:
英国
项目状态:
未结题
起止时间:
2024 至 --
中文摘要
Titan平台的研究面向最终的网络网络,由六个互连紧密的灯塔项目构成,这些项目反映了所有网络元素-1)核心,2)光纤,3)射频(RF),包括蜂窝和无线网络,4)用于接入和回程的新兴光无线网络,5)涉及卫星、航空和水下网络的非地面网络,最后是6)量子通信网络。核心网的研究重点是一种新的体系结构和人工智能(AI)技术,通过考虑数据速率、延迟、安全和能源效率方面的高级要求,能够集成多路访问技术,以实现无缝的端到端服务交付。Titan将进行新的研究,旨在协调不同的现有和新兴的射频网络(3G、4G、5G、6G、WiFi、蓝牙等)。通过开发将最佳地选择相应的一个或多个RF网络并开发相应的协议以实现无缝的端到端连接的技术来朝向单个网络。由于未来网络对新频谱的需求是毋庸置疑的,泰坦将关键地包括围绕太赫兹和光纤频谱建立的新网络。由于这些网络将受益于作为新网络元素一部分的新智能反射表面,Titan将通过在人工智能和机器学习(ML)方面的工作基础上,通过为网络的其他部分(如边缘和核心)开发的工作,包括网络方面的研究和可重新配置智能表面(RIS)的集成。光纤网络是网络网络的重要组成部分。因此,Titan将致力于研究空芯光纤等先进光纤技术和新的灵活收发器技术的最佳集成,以支持延迟等关键网络要求。普遍服务的可获得性和所谓的“数字鸿沟”是一个日益严重的社会挑战。因此,泰坦将对包括空中、卫星和水下网络在内的非地面网络的整合进行关键研究,这些网络都旨在通过泰坦的整体方法实现无缝的端到端服务提供。最后,Titan将有意义地将新的量子网络技术与传统网络结合在一起,并将为这两种基本网络的最佳使用提供重要指导。Titan的一个重要考虑是从网络中提取传感信息。所有的网络元素都有特定的特征,结合ML技术,可以提取重要的边信息。泰坦将研究每个网络段的这种能力,但关键是将独立的感知信息整合在一起,以实现具有最高水平的自我x(配置、修复、自动化、优化)的超认知网络。
英文摘要
The research of the TITAN platform is geared towards the ultimate network of networks and is structured in six strongly interconnected lighthouse projects which reflect all network elements - 1) the core, 2) optical fibre, 3) radio frequency (RF) including cellular and wireless networks, 4) emerging optical wireless networks for access and backhaul, 5) non-terrestrial networks involving satellites, aerial and underwater networks, and finally 6) quantum communication networks. The research on the core network focuses on a new architecture and artificial intelligence (AI) techniques that enable the integration of multi-access technologies for a seamless end-to-end service delivery by considering advanced requirements in terms of data rate, latency, security and energy efficiency. TITAN will conduct novel research that aims at orchestrating the different existing and emerging RF networks (3G, 4G, 5G, 6G, WiFi, Bluetooth, etc.) towards a single network by developing techniques that would optimally select the respective RF network, or networks, and develop the respective protocols to enable a seamless end-to-end connection. Because of the undisputed need for new spectrum in future networks, TITAN will crucially include new networks that are built around the terahertz and optical spectrum. Since these networks will benefit from new intelligent reflecting surfaces as part of a new network element, TITAN will include research on the networking aspects and the integration of reconfigurable intelligent surfaces (RIS) by building on the work on AI and machine learning (ML) developed for other parts of the network, such as edge and core. Optical fibre networks are an important element of a network of networks. Therefore, TITAN will address research questions on the optimum integration of advanced optical fibre technologies such as hollowcore fibres and new agile transceiver technologies to support key network requirements such as latency. Universal service availability and what is described as the 'digital divide' represent an increasing societal challenge. Therefore, TITAN will conduct critical research on the integration of non-terrestrial networks which include aerial, satellite and underwater networks all geared towards a seamless end-to-end service provision which is achieved by the holistic approach of TITAN. Lastly, TITAN will meaningfully integrate new quantum network technologies alongside conventional networks and will provide important guidance on the optimum use of both fundamental networks. An important consideration of TITAN is the extraction of sensing information from networks. All network elements have particular features and, in conjunction with ML techniques, important side information can be extracted. TITAN will investigate this capability for each network segment, but crucially brings the independent sensing information together to achieve an ultra-cognitive network which exhibits the highest level of self-x (configuration, healing, automation, optimisation).
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会议论文
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批准号:EP/X034542/2
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项目类别:Research Grant
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资助金额:$0.0万
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负责人:Harald Haas
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依托单位:
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