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

Ahmed M
(2023)
Privacy-Preserving Distributed Beamformer Design Techniques for Correlated Parameter Estimation
in IEEE Sensors Journal

An J
(2024)
Two-Dimensional Direction-of-Arrival Estimation Using Stacked Intelligent Metasurfaces
in IEEE Journal on Selected Areas in Communications


An J
(2024)
Adjustable-Delay RIS Is Capable of Improving OFDM Systems
in IEEE Transactions on Vehicular Technology

An J
(2023)
A Tutorial on Holographic MIMO Communications-Part II: Performance Analysis and Holographic Beamforming
in IEEE Communications Letters

An J
(2023)
A Tutorial on Holographic MIMO Communications-Part III: Open Opportunities and Challenges
in IEEE Communications Letters

An J
(2023)
A Tutorial on Holographic MIMO Communications-Part I: Channel Modeling and Channel Estimation
in IEEE Communications Letters

An J
(2023)
Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G
in IEEE Journal on Selected Areas in Communications
Description | Platform Driving The Ultimate Connectivity |
Amount | £2,030,861 (GBP) |
Funding ID | EP/X04047X/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 04/2023 |
End | 04/2026 |
Description | TITAN |
Organisation | University of Essex |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | We formed a larger consortium called TITAN by 16 Universities and 44 academics; We are now engaging in collaborative research; |
Collaborator Contribution | My closest collaborators are Essex Uni and Loughborough, plus Imperial College; 2.2.5 LP 5: Non-Terrestrial network design and optimisation (Lead: UoS) The objectives of LP5 as stated in the original proposal are to develop: O5.1) develop techniques that will allow maintaining a uniform quality of service in a network with increasing non-terrestrial network elements, and thus effectively mitigating the digital divide; O5.2) develop techniques for prompt, low-delay on-line optimisation allowing each network element to make its decisions independently - ideally based on local constraints and on limited cooperation strictly with immediate neighbours only, while approaching the Pareto-front of centrally controlled solutions; O5.3) conceive Pareto-optimal RA solutions without relying on high-overhead central control. To address these objectives seven MPs are proposed: MP23: Network-slicing aided multi-component optimisation of NTNs/ space-air-ground integrated networks (SAGINs) As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customised services in the global SAGIN with diverse resource constraints. This is particularly critical in the face of the extremely heterogeneous characteristics of the terrestrial, aerial and satellite layers, where the propagation conditions, including the bandwidth, carrier frequency, distances, line-of-sight (LOS) vs. non-line of sight (NLOS) conditions are substantially different. The throughput, the delay and the coverage area of these three classes of RAN slices will be jointly optimised by carefully considering the distinct channel features and service advantages of the terrestrial, aerial and satellite components of SAGINs. Given the potentially excessive complexity of solving the above problems to find the Pareto optimal solutions in high-Doppler scenarios, sophisticated AI techniques will be conceived for solving the associated problem. This MP will address O5.2 and O5.3. MP24: Adaptive physical layer design for NTN/SAGIN systems In the 2G, 3G and 4G terrestrial systems different Gaussian minimum shift keying (GMSK)/ time division multiple access (TDMA), code division multiple access (CDMA) and orthogonal frequency division multiple access (OFDMA) solutions have been used, but the 5G systems opted for a similar OFDM-based physical layer (PHY) to that of the 4G systems. However, current PHY layer technologies fall short of accommodating the demands of high-mobility scenarios envisioned for 6G networks. One significant limitation is their inability to effectively handle the elevated Doppler spread commonly encountered in SAGINs. This MP will develop new PHY layer techniques for NTN/SAGIN networks and will address O5.1. MP25: Task Specific Security Solutions for Space-Air-Ground Networks Ensuring security, reliability, and resilience in NTN/SAGIN networks is an open challenge. In this mini project, we will develop an innovative task-specific security protocol leveraging physical layer security (PLS) techniques that aims to provide security measures for the semantics of the data rather than the data itself and adjust to requirements of the different parts of SAGIN. This MP will address O5.2. MP26: Semantic Aware Modulation Schemes for Space-Air-Ground Networks (Lead: UoEs) NTN/SAGIN networks suffer from long delays and cannot guarantee sufficient bandwidth resources to facilitate emerging applications based on extended reality, among other applications. These applications typically require transmission capabilities in the region of 10s -100s of Mbps at less than 10 ms delay. Semantic communication has been proposed as an efficient way to improve the effectiveness of communication by considering the semantics of the data rather than trying to reconstruct the data faithfully. In this MP, we aim to revolutionise the design of modulation schemes for SAGIN networks by considering the content/context (semantics) of the data to deal with excess delay and inefficient use of network resources. This MP addresses O5.1 MP27: Quantum Signal Processing and Optimisation Algorithms for Non-Terrestrial Communications The computational capability outpaced by demand has become the bottleneck in the development of future non-terrestrial communications that cannot be dealt with by today's supercomputers. Quantum computing has emerged as a new tool, but its current applications in communications are still very limited, and are yet to show tangible advantages over classical methods. This MP will address the computational bottleneck by exploiting quantum computing techniques through effective modelling and algorithm design for channel estimation and prediction, channel decoding and quantum optimization algorithms. This MP will address O5.1 and will leverage synergies with LP6. MP28: NTN design and optimisation Unmanned aerial vehicles (UAV) play a critical role in future NTNs. However, the flight trajectory must be optimised to maximise coverage. In this context, multi-objective (MO) optimisation plays a pivotal role in the realm of airport flight planning, and its relevance extends to the domain of UAV swarm trajectory planning in NTNs. In addition to conventional objectives, e.g., communication quality, energy consumption, and collision avoidance, UAV swarm introduces a new optimisation domain, i.e., swarm structures. This MP will develop MO soft actor and critic (SAC) algorithms to find the full potential of a sensing and communication system under bandwidth constraints. This MP will address O5.2. MP29: Low-complexity Channel Estimation and Data Detection in High Doppler Non-Terrestrial Networks The currently used orthogonal frequency-division multiplexing (OFDM) modulation technique is primarily designed for time-invariant frequency-selective scenarios. However, in the high-mobility NTNs, mainly doubly selective fading is encountered and the substantially increased Doppler frequency leads to intercarrier interference (ICI) that damages the OFDM's subcarrier orthogonality. Orthogonal time-frequency space (OTFS) has been proposed as a promising candidate for high-mobility communications. However, OTFS modulation will dramatically increase the system complexity, especially at the receiver side. Thus, designing low complexity OTFS receiver with high reliability is essential for OTFS modulation to be adopted by new-generation wireless communication systems. To address these challenges, this MP contributes to development of signal processing and ML solutions for channel estimation and signal detection for OTFS-based NTNs. This MP address O5.3 |
Impact | See under the publications |
Start Year | 2024 |