There is insufficient radio spectrum below 6GHz to cater for future mobile communications demand. Researchers are also now beginning to consider the needs of the 2030 intelligent information society, which will likely include a further push into sub-terahertz radio spectrum, to deliver yet more user data bandwidth. In 5G, future 6G and beyond, use of millimetre wavelength (mmWave) bands in fixed wireless access and handheld equipment will require power efficient, low cost yet high-performance RF transceivers. Such transceivers must also support extremely high data rates (e.g. Gigabit Ethernet; 5Gbit/s for USB 3.0; 10's of Gbit/s peak rates for vehicular 'infotainment' and '8k' ultra-high-definition TV for virtual reality). This challenging set of requirements has, to date, been mutually exclusive in all conventional mmWave technologies. With the release of early 5G smartphones, such as Samsung Galaxy S10 5G incorporating 28GHz / 39GHz communication radios (bands n257-n261), the era of mmWave mobile communications has begun. Although entry-level 5G is in early stage deployment (using modifications to 4G), it is unlikely to be defined or viable for deploying at high mmWave bands (circa 73GHz) before 2030. Initial analysis shows the digital signal processing (DSP) required for multi-Gbit/s data may extend to 10's of billions of 'multiply-accumulate' instructions per second. When combined with analogue radio functions, this could result in consumed battery powers of 14W by receive functions alone, with considerably more for transmit. Smartphone battery capacities are now circa 4.5Ahr, which would support just 1 hour of operation at such consumed receive powers. Thus, there is an urgent need for new research into mmWave radio hardware and software architectures, for frequencies at E band (circa 73GHz) and beyond. The Fellowship will focus on the following areas:- 1) Cost-effective and power-efficient techniques to form mmWave antenna arrays. Our recent research into Time Modulated Antenna Arrays (TMA) has shown ways of improving TMA efficiency at lower frequencies. A key attraction of the TMA is its simplicity of control interface (all digital). 2) Reinvestigation of fundamental mmWave circuit concepts, such as mixers and oscillators, using new insight and making use of the latest findings for manufacturing key components such as resonators. The research in resonators at mmWave could now benefit from the latest 3D printing techniques available at the University of Sheffield as well as updated techniques in low temperature co-fired ceramics. 3) A holistic view of the mmWave transceiver in terms of hardware and software, with partitioning to give best power efficiency for an RF performance target. Novel techniques will be valuable in saving power in massive multiple-input multiple-output systems (M-MIMO), having many hundreds of antennas and transceivers. In existing M-MIMO systems the power consumed by RF hardware could rival that of the digital signal processors. Research will include reconsidering long-forgotten circuit topologies and ideas, in this new setting. 4) Exploration of signal processing techniques for mmWave cognitive radio- allowing it to sense its operational environment and optimise its performance (via reconfigurable RF hardware). Also, the emergence and increase in capability of artificial intelligence is now becoming relevant for operation closer to the hardware itself, such as in demodulating an incoming RF signal. 5) Prototype test chips and subsystems will be created during the project. These will be used to build mmWave radio system demonstrators, including for propagation measurement research. The post-fellowship application for the trial platforms will support further research in future mass-producible mmWave systems, as well as facilitating enhanced industry engagement.

Artificial Intelligence (AI) can have dramatic effects on industrial sectors and societies (e.g., Generative AI, facial recognition, autonomous vehicles). AI UK will pioneer a reflective, inclusive approach to responsible AI development that does not ignore AI's potential harms but acknowledges, understands and mitigates them for diverse societies. AI UK adopts a strong human-centred approach to ensure societies deploy and use AI in a responsible way by providing the AI community with a toolkit of technological innovations, case studies, guidelines, policies and frameworks for all key sectors of the economy. To achieve this, AI UK will deliver and drive a collaborative ecosystem of researchers, industry, policymakers and stakeholders that will be responsive to the needs of society, led by a team of experienced, well-connected leaders from all four nations of the UK, committed to an inclusive approach to the management of the programme. AI UK grows an interdisciplinary ecosystem that adopts Equality, Diversity and Inclusivity (EDI), Trusted Research, and Responsible Research and Innovation (RRI) as fundamental principles. AI UK will champion a research culture where everyone is respected, valued and able to contribute and benefit and coordinate the UK's AI research networks and programmes, working with key Research Council (and other funding) programmes, The Alan Turing Institute, The Ada Lovelace Institute, AI Standards hub, Centres for Doctoral Training, UKRI AI Research Hubs, Public Sector Research Establishments (PSREs) as well as the wider landscape of university-based Responsible/Ethical AI research institutes. AI UK will connect UK research to internationally leading research centres and institutions around the world. Ultimately, through this ecosystem, AI UK will deliver world-leading best practices for the design, evaluation, regulation and operation of AI-systems that benefit the nation and society. AI UK will invest in the following strands: Ecosystem Creation and Management: to define the portfolio of thematic areas, translational activities, and strategic partnerships with academia, business and government and associated impact metrics. This will broaden and consolidate the network nationally and internationally and identify course corrections to national policy (e.g., industrial strategy). Research & Innovation Programmes: to deliver consortia-led research that address fundamental challenges with multi-disciplinary and industrial perspectives, integrative research projects that link connected and established research teams across the community, and early stage and industry-led research and innovation projects to expand the UK's ecosystem and develop the next generation of leaders. Skills Programme: to translate research into skills frameworks and training for users, customers, and developers of AI, and to contribute to the call for the UK AI Strategy's Online Academy. Public and Policy Engagement: working with the network of policy makers, regulators, and key stakeholders to respond to arising concerns, need for new standards, build capacity for public accountability and provide evidence-based advice to the public and policymakers.