Unlike previous generations of mobile networks, the beyond 5G (B5G) network is envisioned to support edge intelligence, which is to provide both communication and computing capabilities to the proximity of end users. Wireless edge intelligence is particularly important to those crucial use cases of B5G, including smart cities, autonomous driving, wireless healthcare, virtual reality (VR) and augmented reality (AR) gaming, where mobile networks are expected to be equipped with intelligent capabilities for prediction and shaping experiences to individuals. Federated learning (FL) is a key enabling technology for wireless edge intelligence, by performing the model training in a decentralized manner and keeping the data where it is generated. However, a straightforward adaption of FL from computer networks to wireless systems can suffer performance degradation in spectral and implementation efficiency, because of the complex wireless environment with heterogeneous resources and a massive number of devices. The aim of this project is to develop a novel scalable hybrid architecture for wireless FL by efficiently utilising the physical layer dynamics of the mobile communication environments and exploiting sophisticated service-aware and resource-aware collaborative edge learning. The novelty of the project is the development of this novel edge learning architecture, where the fundamental limits of the learning architecture is characterised by advanced mathematical tools, such as graph theory and stochastic learning. In addition, an algorithmic framework for quantifying challenging design trade-offs in the presence of practical constraints by applying sophisticated tools such as compressed sensing and machine learning.

The 5G-and-Beyond cellular networks promise UAVs with ultra-reliable low-latency control, ubiquitous coverage, and seamless swarm connectivity under complex and highly flexible multi-UAV behaviours in three-dimension (3D), which will unlock the full potential of UAVs. This so-called cellular-connected UAVs (C-UAVs) system creates a radically different and rapidly evolving networking and control environment compared to conventional terrestrial networks: 1) The UAV-ground BS/user channels enjoy fewer channel variations due to their dominant line-of-sight (LOS) characteristics, which imposes severe air-ground interference to the coexisting BSs/users in the uplink/downlink. 2) Operating in existing cellular networks designed mainly for dominate downlink traffic (e.g., video), the UAVs with high data rate requirement in uplink payload uploading, and ultra-reliable low-latency communication (URLLC) requirement in downlink command and control communication can hardly be satisfied. 3) Maintaining seamless connectivity for mission-centric UAV swarms with 3D high mobility is essential for UAV cooperation but extremely challenging. 4) Controlling a swarm of UAVs to accomplish complex tasks with limited human supervision under the connectivity constraints is of capital importance but challenging. The above challenges can hardly be solved via conventional model-driven approaches, which are limited to performance evaluation or optimisation at one time instant in an offline or semi-offline manner, relying on given ideal probabilistic channel models without time correlation. Meanwhile, the future cellular networks in 5G-and-Beyond moves towards an open, programmable, and virtualised architecture with unprecedented data availability. Both facts mandate a fundamental change in the way we model, design, control, and optimise the C-UAVs system, from reactive/incident driven decoupled networking and control operation to proactive/ data-driven joint network and control design. This project has the ambitious vision to develop artificial intelligence (AI)-powered C-UAVs system with full network automation and conditional control automation, that allow for joint design and optimization of the network operation and the UAVs control in real-time with minimum human supervision and the target of mission completion under the long-term quality of service (QoS) guarantees. The project will engage with the end-users to exploit the C-UAVs applications in surveillance and emergency services in urban areas. Our results on network automation and control automation will directly benefit the telecom manufacturers (e.g., Ericsson AB, Toshiba Europe, AccelerComm), and broader UAV industries (e.g., Airborne Robotics, Thales, Northrop Grumman) internationally with foreseeable industrial impact. The NGMN and CommNet will facilitate the dissemination of the research outcomes nationally and internationally. The development, implementation, and testing of our proposed solutions serve as a platform towards the commercialisation of our research outcomes, putting the UK at the forefront of the "connected aerial vehicles" revolution.

The Quantum Technology Hub in Sensors and Timing, a collaboration between 7 universities, NPL, BGS and industry, will bring disruptive new capability to real world applications with high economic and societal impact to the UK. The unique properties of QT sensors will enable radical innovations in Geophysics, Health Care, Timing Applications and Navigation. Our established industry partnerships bring a focus to our research work that enable sensors to be customised to the needs of each application. The total long term economic impact could amount to ~10% of GDP. Gravity sensors can see beneath the surface of the ground to identify buried structures that result in enormous cost to construction projects ranging from rail infrastructure, or sink holes, to brownfield site developments. Similarly they can identify oil resources and magma flows. To be of practical value, gravity sensors must be able to make rapid measurements in challenging environments. Operation from airborne platforms, such as drones, will greatly reduce the cost of deployment and bring inaccessible locations within reach. Mapping brain activity in patients with dementia or schizophrenia, particularly when they are able to move around and perform tasks which stimulate brain function, will help early diagnosis and speed the development of new treatments. Existing brain imaging systems are large and unwieldy; it is particularly difficult to use them with children where a better understanding of epilepsy or brain injury would be of enormous benefit. The systems we will develop will be used initially for patients moving freely in shielded rooms but will eventually be capable of operation in less specialised environments. A new generation of QT based magnetometers, manufactured in the UK, will enable these advances. Precision timing is essential to many systems that we take for granted, including communications and radar. Ultra-precise oscillators, in a field deployable package, will enable radar systems to identify small slow-moving targets such as drones which are currently difficult to detect, bringing greater safety to airports and other sensitive locations. Our world is highly dependent on precise navigation. Although originally developed for defence, our civil infrastructure is critically reliant on GNSS. The ability to fix one's location underground, underwater, inside buildings or when satellite signals are deliberately disrupted can be greatly enhanced using QT sensing. Making Inertial Navigation Systems more robust and using novel techniques such as gravity map matching will alleviate many of these problems. In order to achieve all this, we will drive advanced physics research aimed at small, low power operation and translate it into engineered packages to bring systems of unparalleled capability within the reach of practical applications. Applied research will bring out their ability to deliver huge societal and economic benefit. By continuing to work with a cohort of industry partners, we will help establish a complete ecosystem for QT exploitation, with global reach but firmly rooted in the UK. These goals can only be met by combining the expertise of scientists and engineers across a broad spectrum of capability. The ability to engineer devices that can be deployed in challenging environments requires contributions from physics electronic engineering and materials science. The design of systems that possess the necessary characteristics for specific applications requires understanding from civil and electronic engineering, neuroscience and a wide range of stakeholders in the supply chain. The outputs from a sensor is of little value without the ability to translate raw data into actionable information: data analysis and AI skills are needed here. The research activities of the hub are designed to connect and develop these skills in a coordinated fashion such that the impact on our economy is accelerated.

Public opinion on complex scientific topics can have dramatic effects on industrial sectors (e.g. GM crops, fracking, global warming). In order to realise the industrial and societal benefits of Autonomous Systems, they must be trustworthy by design and default, judged both through objective processes of systematic assurance and certification, and via the more subjective lens of users, industry, and the public. To address this and deliver it across the Trustworthy Autonomous Systems (TAS) programme, the UK Research Hub for TAS (TAS-UK) assembles a team that is world renowned for research in understanding the socially embedded nature of technologies. TASK-UK will establish a collaborative platform for the UK to deliver world-leading best practices for the design, regulation and operation of 'socially beneficial' autonomous systems which are both trustworthy in principle, and trusted in practice by individuals, society and government. TAS-UK will work to bring together those within a broader landscape of TAS research, including the TAS nodes, to deliver the fundamental scientific principles that underpin TAS; it will provide a focal point for market and society-led research into TAS; and provide a visible and open door to engage a broad range of end-users, international collaborators and investors. TAS-UK will do this by delivering three key programmes to deliver the overall TAS programme, including the Research Programme, the Advocacy & Engagement Programme, and the Skills Programme. The core of the Research Programme is to amplify and shape TAS research and innovation in the UK, building on existing programmes and linking with the seven TAS nodes to deliver a coherent programme to ensure coverage of the fundamental research issues. The Advocacy & Engagement Programme will create a set of mechanisms for engagement and co-creation with the public, public sector actors, government, the third sector, and industry to help define best practices, assurance processes, and formulate policy. It will engage in cross-sector industry and partner connection and brokering across nodes. The Skills Programme will create a structured pipeline for future leaders in TAS research and innovation with new training programmes and openly available resources for broader upskilling and reskilling in TAS industry.