Pervasive Wireless Intelligence Beyond the Generations
Lead Research Organisation:
University of Southampton
Department Name: Sch of Electronics and Computer Sci
Abstract
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.
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.
