Pervasive Wireless Intelligence Beyond the Generations

超越世代的普遍无线智能

• 批准号: EP/Y026721/1
• 负责人: Chao Xu
• 金额: $ 31.34万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY026721%2F1
• 关键词: Pervasive; Wireless; Intelligence; Beyond; Generations

The International Telecommunication Union estimates that only 63% of the world population have Internet access, despite the fact that almost 88% have already been covered by 4G. This clearly indicates the urgency in a paradigm shift from pursuing data rate or coverage to the global optimization of the entire wireless network.Motivated by this, the objective of this project is to optimize the wireless efficiencies by finding the Pareto-optimal solutions in the context of next-generation space-air-ground integrated networks (SAGIN), which constitutes a promising architecture that extends wireless broadband dividend to the hitherto unconnected 3 Billion. This project places emphasis on the following questions: 1) In the face of their inevitable coexistence, how can radar and communication both benefit from their integration in SAGIN? 2) How to extend their coverage without building new base stations? 3) How to achieve the near-capacity performance for SAGIN that is predicted by the Shannon-Hartley's channel capacity theorem?Against this background, this project breaks new grounds in the following aspects: 1) New waveforms are proposed for integrated sensing and communication (ISAC), which will be the first solutions that can improve both functionalities. 2) New materials of reconfigurable intelligent surfaces (RIS) are optimized in the face of doubly selective fading in order to extend the coverage of ISAC in SAGIN. This project proposes to dispense with RIS channel estimation, which facilitates a major breakthrough in conceiving coherent/non-coherent adaptivity for RIS assisted high-mobility SAGIN. 3) New deep learning (DL) tools are conceived for solving the multiobjective optimization problems of SAGIN. Specifically, our bespoke DL architecture will be intelligently tuned for any given error correction code, so that the near-capacity performance can be achieved for SAGIN, paving the way for pervasive wireless intelligence beyond the generations.

国际电信联盟估计，尽管近88%的人口已经被4G覆盖，但只有63%的世界人口能够接入互联网。这清楚地表明了从追求数据速率或覆盖到整个无线网络的全局优化的范式转变的紧迫性。受此启发，该项目的目标是在下一代空间-空地综合网络(SAGIN)的背景下通过寻找帕累托最优解决方案来优化无线效率，这构成了一个有前途的架构，将无线宽带红利扩展到迄今未连接的30亿人。本课题重点研究了以下几个问题：1)面对雷达和通信不可避免的共存，如何从两者的融合中获益？2)如何在不建设新基站的情况下扩大覆盖范围？3)如何达到香农-哈特利信道容量定理所预测的Sagin的近容量性能？在此背景下，本项目在以下几个方面取得了新的突破：1)提出了集成侦听和通信(ISAC)的新波形，这将是第一个能够同时改善两者功能的解决方案。2)针对双选择性衰落，对可重构智能表面(RIS)的新材料进行了优化，以扩大ISAC在萨金的覆盖范围。该项目建议省去RIS信道估计，这有助于在为RIS辅助的高移动性自适应构思相干/非相干自适应方面取得重大突破。3)提出了解决Sagin多目标优化问题的深度学习工具。具体地说，我们定制的DL架构将针对任何给定的纠错码进行智能调整，以便为Sagin实现接近容量的性能，为超越几代人的普适无线智能铺平道路。