TITAN is building a single, intelligent network that weaves together everything from satellites and undersea cables to 6G mobile and quantum communications, orchestrating them as one seamless system. This matters because today’s networks are a patchwork of separate technologies—mobile, Wi-Fi, fibre, satellite—each with its own rules. Users experience dropped connections, lag, and coverage gaps, especially in rural or remote areas. TITAN aims to solve this by using artificial intelligence to automatically select the best network for each task, whether that means low-latency fibre for surgery or resilient satellite links for a ship at sea. If successful, the platform could eliminate the “digital divide” by delivering universal, uninterrupted connectivity. It would also extract sensing data from network signals—using machine learning to detect environmental changes or infrastructure faults—creating an “ultra-cognitive” network that self-configures and self-heals. The research spans hollow-core fibres, terahertz spectrum, reconfigurable surfaces, and quantum links, integrating them into a single architecture. While the work is applied, it also advances fundamental understanding of how to orchestrate radically different physical-layer technologies under a unified AI-driven control plane.
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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).
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