Pervasive Wireless Intelligence Beyond the Generations
Lead Research Organisation:
University of Southampton
Department Name: School of Electronics and Computer Science
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.
Organisations
Publications
Chandra D
(2023)
EXIT-Chart Aided Design of Irregular Multiple-Rate Quantum Turbo Block Codes
in IEEE Access
Li S
(2026)
Faster-Than-Nyquist Signaling for Next-Generation Wireless: Principles, Applications, and Challenges
in IEEE Communications Standards Magazine
Xuan Tung N
(2026)
Graph Neural Networks for Next-Generation-IoT: Recent Advances and Open Challenges
in IEEE Communications Surveys & Tutorials
Pan D
(2024)
The Evolution of Quantum Secure Direct Communication: On the Road to the Qinternet
in IEEE Communications Surveys & Tutorials
Hoang T
(2024)
Physical Layer Authentication and Security Design in the Machine Learning Era
in IEEE Communications Surveys & Tutorials
Zhou G
(2024)
Multiobjective Optimization of Space-Air-Ground-Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms
in IEEE Internet of Things Journal
Liu M
(2024)
A Nonorthogonal Uplink/Downlink IoT Solution for Next-Generation ISAC Systems
in IEEE Internet of Things Journal
Guo B
(2024)
Pareto-Optimal Multiagent Cooperative Caching Relying on Multipolicy Reinforcement Learning
in IEEE Internet of Things Journal
Mobini Z
(2024)
Cell-Free Massive MIMO Surveillance of Multiple Untrusted Communication Links
in IEEE Internet of Things Journal
Ghadi F
(2025)
Performance Analysis of FAS-Aided NOMA-ISAC: A Backscattering Scenario
in IEEE Internet of Things Journal
Lu S
(2024)
Integrated Sensing and Communications: Recent Advances and Ten Open Challenges
in IEEE Internet of Things Journal
An J
(2024)
Two-Dimensional Direction-of-Arrival Estimation Using Stacked Intelligent Metasurfaces
in IEEE Journal on Selected Areas in Communications
Trinh P
(2025)
Optical RISs Improve the Secret Key Rate of Free-Space QKD in HAP-to-UAV Scenarios
in IEEE Journal on Selected Areas in Communications
Chen J
(2025)
OTFS-MDMA: An Elastic Multi-Domain Resource Utilization Mechanism for High Mobility Scenarios
in IEEE Journal on Selected Areas in Communications
He H
(2023)
Towards Reliable Space-Ground Integrated Networks: From System-Level Design to Implementation
in IEEE Network
Meng K
(2025)
Integrated Sensing and Communication Meets Smart Propagation Engineering: Opportunities and Challenges
in IEEE Network
Li Q
(2025)
Holographic MIMO Aided Integrated User-Centric Cell-Free Terrestrial and Non-Terrestrial Networks
in IEEE Network
Nasir A
(2025)
Widely Linear Processing Improves the Throughput of Nonorthogonal User Access
in IEEE Open Journal of the Communications Society
Saxena S
(2023)
Sparse Channel Estimation for Visible Light Optical OFDM Systems Relying on Bayesian Learning
in IEEE Open Journal of the Communications Society
Kumar P
(2024)
Decision Fusion in Centralized and Distributed Multiuser Millimeter-Wave Massive MIMO-OFDM Sensor Networks
in IEEE Open Journal of the Communications Society
Singh J
(2023)
Joint Transceiver and Reconfigurable Intelligent Surface Design for Multiuser mmWave MIMO Systems Relying on Non-Diagonal Phase Shift Matrices
in IEEE Open Journal of the Communications Society
Liu X
(2024)
The Road to Near-Capacity CV-QKD Reconciliation: An FEC-Agnostic Design
in IEEE Open Journal of the Communications Society
Gupta A
(2024)
An Affine Precoded Superimposed Pilot-Based mmWave MIMO-OFDM ISAC System
in IEEE Open Journal of the Communications Society
Maity P
(2025)
Variational Bayesian Learning for 3-D Localization of Extended Targets in mmWave MIMO OFDM ISAC Systems
in IEEE Open Journal of the Communications Society
| Description | Perfect Doppler compensation and synchronization is nontrivial due to multi-path Doppler effects and Einstein's theory of relativity in the space-air-ground-integrated networks (SAGINs). Hence, by considering the residual Doppler and the synchronization delay, this paper investigates the bit-error-rate (BER) performance attained under time-varying correlated Shadowed-Rician SAGIN channels. First, a practical SAGIN model is harnessed, encompassing correlated Shadowed-Rician channels, the Snell's law-based path loss, atmospheric absorption, the line-of-sight Doppler compensation, elliptical satellite orbits, and Einstein's theory of relativity. Then, a specific correlation coefficient between the pilot and data symbols is derived in the context of correlated Shadowed-Rician Channels. By exploiting this correlation coefficient, the channel distribution is mimicked by a bi-variate Gamma distribution. Then, a closed-form BER formula is derived under employing least-square channel estimation and equalization for 16-QAM. Our analytical results indicate for a 300-km-altitude LEO that 1) the period of realistic elliptical orbits is around 0.8 seconds longer than that of the idealized circular orbits; and 2) the relativistic delay is lower than 1 $\mu s$ over a full LEO pass (from rise to set). Our numerical results for the L bands quantify the effects of: 1) the residual Doppler; 2) atmospheric shadowing; 3) synchronization errors; and 4) pilot overhead. |
| Exploitation Route | The mathematical expressions derived can be used for both designing new systems, as well as for benchmarking other systems. |
| Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Electronics |
| Description | We have made our industrial partners aware of the original equations derived, such as Accellercom, Interdigital and Viavi; |
| First Year Of Impact | 2025 |
| Sector | Digital/Communication/Information Technologies (including Software),Education,Electronics |
| Impact Types | Economic |