(6G-ICARUS) - 6G Intelligent Connectivity And inteRaction for Users and infraStructures
(6G-ICARUS) - 6G Intelligent Connectivity And inteRaction for Users and infraStructures
批准号:
EP/Z000122/1
负责人:
Pavlos Lazaridis
金额:
$17.37万
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
在现代技术的背景下,过渡到6G网络面临许多挑战;因此,必须发现、组合和测试新的解决方案,以实现分布式智能连接、难以察觉的延迟、几乎无限的容量、改进的安全/隐私、智能/自适应网络和能源效率。6G-ICARUS项目将调查、合并和改进当前的技术,以解决6G网络将面临的众多障碍,以定义未来的无线网络(FWN)。6G-ICARUS专注于能够满足各种目标的FWN,从而实现更好的地理覆盖,特别是在城市地区,更高的传输速率,更低的延迟,提供多样化的服务,处理从海量异质来源产生的信息,固有的弹性来应对潜在的安全威胁,并做出智能决策。6GICARUS建立在三个支柱上:(I)有效利用可重新配置的智能表面,适当地由人工智能软件控制;(Ii)毫米波/亚太赫兹通信的信道建模;(Iii)6G移动通信的多连接解决方案。该项目将产生新的研究成果,以及主要基于机器学习和深度学习的创新软件(技术、算法)支持的创新原创硬件。硬件和软件将在现实环境中进行组合和检查,以满足某些情况,所有这些情况都与现实世界的应用程序相匹配。将有单独的概念研究证明,以验证正在考虑的技术和方法。
英文摘要
In the context of modern technologies, the transition to 6G networks faces numerous challenges; therefore, novel solutions must bediscovered, combined, and tested in order to achieve distributed smart connectivity, imperceptible latency, virtually infinite capacity,improved security/privacy, smart/adaptive networking, and energy efficiency. The 6G-ICARUS project will investigate, combine, andimprove on current technologies in order to address numerous obstacles that 6G networks will face in order to define the futurewireless networks (FWNs).6G-ICARUS is focused on FWNs capable of meeting a variety of targets, resulting in better geographic coverage, particularly in urbanareas, higher transmission rates, lower latency, provision of diverse services, processing of information generated from a massivevolume of heterogeneous sources, inherent resilience to counter potential security threats, and making intelligent decisions. 6GICARUSis founded on three pillars: (i) Effective utilisation of reconfigurable intelligent surfaces suitably controlled by artificialintelligence software; (ii) Channel modelling for mmWave/subTHz communications; (iii) Multi-connectivity solutions for 6G mobilecommunications.The project will generate novel research results as well as novel original hardware supported by innovative software (techniques,algorithms) based mostly on machine learning and deep learning. Hardware and software will be combined and examined in realworldsettings to fulfill certain situations, all of which are matched with real-world applications. There will be separate Proof ofConcept studies to validate the technologies and methodologies under consideration.
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