Pervasive Wireless Intelligence Beyond the Generations (PerCom)

从历史上讲，新一代的移动的通信系统大约每十年被构想一次。随着5G在全球范围内的推广进展，以及经济受到疫情限制的抑制，全球经济复苏需要强大的下一代解决方案，以支持全球各地的智能制造、采矿、运输和农业，即使是在偏远或恶劣的地理位置。未来几代人的成功将取决于三个主要方面：强大的市场需求、坚实的商业模式和新技术。因此，这项前沿研究设想了激进的空间-空中-地面综合网络（SAGIN）解决方案，原因如下。至于市场需求，只有大约一半的世界人口可以上网。无线通信作为关键基础设施的一部分支持宽带市场的时机已经到来。第二，SAGIN体系结构包括地面上的卫星、飞机和车辆。这种模式在扩大无线宽带红利方面具有强大的潜力，作为关键基础设施的一部分。第三，为了克服SAGIN的各种关键部署问题，本项目旨在提供以下三个方面的整体解决方案：（1）在SAGIN下首次提出了联合雷达和通信（RadCom）的新波形，以便它们都受益于集成，重用相同的硬件和相同的有限频谱，以支持引人注目的新应用。(2)开发一种新的超材料，以可重构智能表面（RIS）的形式，以提高SAGIN的覆盖率。RISs由大量低成本的无源元件组成，这些元件反射信号而不使用有源RF链，从而消除了信号处理延迟和功耗。我们通过优化RIS模式来创新RIS配置。这种激进的解决方案消除了对每个RIS元件的高复杂度信道估计的需要，这促进了在构思用于RIS辅助SAGIN的相干/非相干自适应性方面的重大突破。(3)新的深度学习（DL）工具被设想用于SAGIN的多目标优化问题，其中在任何给定的SAGIN场景中，可以借助于信道编码来实现接近容量的性能。解释一下，类似于将高复杂度信道编码设计分解为两个级联卷积码的turbo编码的成功，该项目提出了两个DNN的串行/并行级联，其中一个对另一个进行校正。这有助于先进的DL设计，在任何信道编码方案的背景下指导其过程，从而使近容量无线智能成为现实。该项目将是英国有史以来第一个通过寻找最优解的整个帕累托前沿来超越惊人的权衡的项目，最优解被定义为所有最优解的集合，其中没有一个优化的性能度量可以在不损害另一个的情况下被进一步改进。例如，这比纯粹的功率最小化或吞吐量最大化更具挑战性。因此，该项目将有利于和激励学术界和工业界。

Historically speaking, a new generation of mobile communication system has been conceived roughly every decade. As the global roll-out of 5G is progressing and the economy is stifled by the restrictions imposed by the pandemic, the global economic recovery requires powerful next-generation solutions right across the planet in support of smart manufacturing, mining, transport and agriculture even in remote or hostile geographic locations.The success of future generations will depend on three major aspects: strong market demand, solid business model and new technologies. Hence this frontier research conceives radical space-air-ground integrated networking (SAGIN) solutions for the following reasons. As for the market demand, only about half of the world population has Internet access. The time has come for wireless communication to support the broadband market as part of the critical infrastructure. Secondly, the SAGIN architecture incorporates satellites, planes and vehicles on the ground. This model has strong potential in extending the wireless broadband dividend as part of the critical infrastructure. Thirdly, to overcome a variety of critical deployment issues of SAGIN, this project aims to provide holistic solutions in the following three aspects: (1) New waveforms are proposed for joint radar and communication (RadCom) for the first time under SAGIN, so that they both benefit from the integration in terms reusing the same hardware and the same limited spectrum in support of compelling new applications. (2) Exploiting a new breed of meta-materials in form of reconfigurable intelligent surfaces (RIS) for improving the SAGIN coverage. Explicitly, RISs consist of a large number of low-cost passive elements, which reflect signals without using active RF chains, hence eliminating the signal processing delay and power-consumption. We innovate in RIS configuration by optimizing the RIS patterns. This radical solution eliminates the need for high-complexity channel estimation for each RIS element, which facilitates a major breakthrough in conceiving coherent/non-coherent adaptivity for RIS-assisted SAGIN. (3) New deep learning (DL) tools are conceived for the multi-objective optimization problems of SAGIN, where near-capacity performance can be achieved with the aid of channel coding in any given SAGIN scenario. Explicitly, in analogy to the success of turbo coding that breaks up a high-complexity channel coding design into two concatenated convolutional codes, this project proposes the serial/parallel concatenation of two DNNs, where one makes corrections for the other. This facilitates an advanced DL design that guides its process in the context of any channel coding scheme, which makes near-capacity wireless intelligence a reality.This project will be the first ever in the UK that goes way beyond striking trade-offs by finding the entire Pareto-front of optimal solutions, which is defined as the set of all optimal solutions, where none of the optimized performance metrics can be further improved without compromising another. This is vastly more challenging than pure power-minimization or throughput maximijzation, for example. Hence the project will benefit and inspire both the academic and industrial community.