Pervasive Wireless Intelligence Beyond the Generations (PerCom)

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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.