Details of Grant 

EPSRC Reference:	EP/Y037243/1
Title:	TITAN Extension
Principal Investigator:	Haas, Professor H
Other Investigators:	Musavian, Professor L Cotton, Professor S Nallanathan, Professor A Ling, Dr C Safari, Professor M Nejabati, Professor R Hanzo, Professor L Dawson, Professor M Simeonidou, Professor D Thompson, Professor JS Tang, Professor J Petropoulos, Professor P Matthaiou, Professor M Thomos, Professor N Derakhshani, Dr M Liu, Dr Y Ngo, Dr H Jin, Dr W Tavakkolnia, Dr I Vasilakos, Dr X Clerckx, Professor B Bottrill, Dr KRH Zheng, Professor G Chen, Professor Y Sellathurai, Professor M Leung, Professor KK Penty, Professor R Elmirghani, Professor J O'Brien, Professor D Lambotharan, Professor S El-Hajjar, Dr M Nakhai, Dr MR Savory, Professor SJ Giddings, Dr R P Popoola, Professor WO Yang, Dr Y Herrnsdorf, Dr JHL Yan, Dr S Vlaski, Dr S Hussain, Dr R Elgorashi, Dr T
Researcher Co-Investigators:
Project Partners:
Department:	Engineering
Organisation:	University of Cambridge
Scheme:	Standard Research - NR1
Starts:	01 January 2024	Ends:	31 March 2025	Value (£):	10,612,161
EPSRC Research Topic Classifications:	Networks & Distributed Systems RF & Microwave Technology
EPSRC Industrial Sector Classifications:	Communications
Related Grants:
Panel History:	Panel Date Panel Name Outcome 07 Nov 2023 Technology Missions Fund-Value for Money Announced
Summary on Grant Application Form
The research of the TITAN platform is geared towards the ultimate network of networks and is structured in six strongly interconnected lighthouse projects which reflect all network elements - 1) the core, 2) optical fibre, 3) radio frequency (RF) including cellular and wireless networks, 4) emerging optical wireless networks for access and backhaul, 5) non-terrestrial networks involving satellites, aerial and underwater networks, and finally 6) quantum communication networks. The research on the core network focuses on a new architecture and artificial intelligence (AI) techniques that enable the integration of multi-access technologies for a seamless end-to-end service delivery by considering advanced requirements in terms of data rate, latency, security and energy efficiency. TITAN will conduct novel research that aims at orchestrating the different existing and emerging RF networks (3G, 4G, 5G, 6G, WiFi, Bluetooth, etc.) towards a single network by developing techniques that would optimally select the respective RF network, or networks, and develop the respective protocols to enable a seamless end-to-end connection. Because of the undisputed need for new spectrum in future networks, TITAN will crucially include new networks that are built around the terahertz and optical spectrum. Since these networks will benefit from new intelligent reflecting surfaces as part of a new network element, TITAN will include research on the networking aspects and the integration of reconfigurable intelligent surfaces (RIS) by building on the work on AI and machine learning (ML) developed for other parts of the network, such as edge and core. Optical fibre networks are an important element of a network of networks. Therefore, TITAN will address research questions on the optimum integration of advanced optical fibre technologies such as hollowcore fibres and new agile transceiver technologies to support key network requirements such as latency. Universal service availability and what is described as the 'digital divide' represent an increasing societal challenge. Therefore, TITAN will conduct critical research on the integration of non-terrestrial networks which include aerial, satellite and underwater networks all geared towards a seamless end-to-end service provision which is achieved by the holistic approach of TITAN. Lastly, TITAN will meaningfully integrate new quantum network technologies alongside conventional networks and will provide important guidance on the optimum use of both fundamental networks. An important consideration of TITAN is the extraction of sensing information from networks. All network elements have particular features and, in conjunction with ML techniques, important side information can be extracted. TITAN will investigate this capability for each network segment, but crucially brings the independent sensing information together to achieve an ultra-cognitive network which exhibits the highest level of self-x (configuration, healing, automation, optimisation).
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.cam.ac.uk