The research is focused on one of our society's greatest technical challenges and economic drivers with impact on knowledge, economy, society and people as well as business and government activities. It aims to transform the development of the information and communication infrastructure. A high-capacity, flexible, cost-effective and efficient telecommunications and data infrastructure is of great national and international importance. The ability to communicate seamlessly, without delay, requires intelligent communications networks with high capacity, available when and where it is needed. To achieve this requires research advances in ultrawideband wireless and optical networks, as well as intelligent transceivers, new ultrawideband optical devices and algorithms. This is a fast-moving and internationally fiercely competitive field and to maintain international leadership requires the capability of not only making theoretical advances, but the also the ability of demonstrating these experimentally. Our vision is to create an advanced, world leading signal generation and detection test-bed for advanced communications systems research. The key feature of the proposed system are the ultra-low noise, high-resolution capture and analysis of complex broadband signals, more than quadrupling the achievable network capacity. This unique facility will allow the investigation of optical and wireless networks over a wide range of time- and length scales, including long-haul networks, data centres and enable the research into the ultra-wideband signal manipulation for the next-generation optical & wireless access networks. It will enable UCL and UK to consolidate and enhance its internationally leading position in communications systems research supporting a wide range of other areas.

The exponential growth in the use of bandwidth-hungry internet services such as high-definition video streaming, cloud computing, artificial intelligence, Big Data and the Internet of Things requires new advances in optical data transmission technologies to achieve ultra-high throughputs and minimal latencies. To go beyond current channel limits is arguably the greatest challenge faced by digital optical communications. To target it, the proposed research programme will develop new approaches to significantly increase the capacity of future communication systems focusing on the ultrawideband optical transmission and amplification in combination with adaptative coded modulation and digital signal processing, to ensure a robust communications infrastructure beyond tomorrow. Systems capacity is bounded by three dimensions: bandwidth, information spectral density and space. Whilst much research has focused on maximising the information spectral density and investigating space division multiplexing, little attention has been paid to the bandwidth domain. We propose to significantly extend the channel bandwidth with transceivers, broadband optical amplifiers, beyond the well-established erbium doped fibre amplifier (EDFA), focusing on bismuth and thulium doped fibre amplifiers with the assistance of Raman-amplification. Together with space division multiplexing, based on multiple fibres or new multi-core fibres, will ensure system capacities of tens of Petabit/s will be possible in the future. In EWOC research, we will gain a deeper understanding of the fundamental nonlinear effects that govern the upper limit on capacity in such ultra-wide systems, never previously investigated. Three main challenges are: (i) to fully utilise the bandwidth of the ubiquitous silica fibres low-loss window, overcoming the single mode fibre constraints, to reach bit rates of up to 250 Tb/s per core; (ii) to operate beyond the Raman gain shift - means that the associated nonlinear signal-to-signal interference in the widely diverse dispersion and nonlinearity regimes must be understood, quantified and effectively mitigated and (iii) experimentally demonstrate the combination of the significantly increased bandwidth with novel coded modulation, advanced DSP and nonlinearity mitigation in a wide variety of distance and bitrate transmission scenarios and applications in core, access and data centre networks. The EWOC proposal is a collaboration between UCL's Optical Networks Group and the University of Southampton Optoelectronics Research Centre and 6 world-leading industrial partners spanning network and service providers (BT and KDDI), equipment systems (Xtera and Nokia) and optical fibre/amplifier (Corning/OFS) manufacturers, a testament to the strategic importance of this research. The importance of ubiquitous, broadband, high-capacity, low delay and secure telecommunications infrastructure is critical to the UK's future and economic success. The recently published report of the National Taskforce on Telecoms Equipment Diversification Task Force (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/975007/April_2021_Telecoms_Diversification_Taskforce_Findings_and_Report_v2.pdf) has highlighted the need for R&D to ensure this: 'Research, development and innovation are central to the development of new telecoms solutions and technologies and a major competitive advantage for incumbent vendors. Therefore, R&D activity and investment is vital in driving diversification' recommending 'The Government should invest in projects aimed at early development and growth of systems integration skills in the UK. Such projects will ensure it builds a competitive advantage in this domain, and as an early element of its ambition to build UK capability'. The EWOC proposal is focused on both of these goals.

Optical networks underpin the global digital communications infrastructure, and their development has simultaneously stimulated the growth in demand for data, and responded to this demand by unlocking the capacity of fibre-optic channels. The work within the UNLOC programme grant proved successful in understanding the fundamental limits in point-to-point nonlinear fibre channel capacity. However, the next-generation digital infrastructure needs more than raw capacity - it requires channel and flexible resource and capacity provision in combination with low latency, simplified and modular network architectures with maximum data throughput, and network resilience combined with overall network security. How to build such an intelligent and flexible network is a major problem of global importance. To cope with increasingly dynamic variations of delay-sensitive demands within the network and to enable the Internet of Skills, current optical networks overprovision capacity, resulting in both over- engineering and unutilised capacity. A key challenge is, therefore, to understand how to intelligently utilise the finite optical network resources to dynamically maximise performance, while also increasing robustness to future unknown requirements. The aim of TRANSNET is to address this challenge by creating an adaptive intelligent optical network that is able to dynamically provide capacity where and when it is needed - the backbone of the next-generation digital infrastructure. Our vision and ambition is to introduce intelligence into all levels of optical communication, cloud and data centre infrastructure and to develop optical transceivers that are optimally able to dynamically respond to varying application requirements of capacity, reach and delay. We envisage that machine learning (ML) will become ubiquitous in future optical networks, at all levels of design and operation, from digital coding, equalisation and impairment mitigation, through to monitoring, fault prediction and identification, and signal restoration, traffic pattern prediction and resource planning. TRANSNET will focus on the application of machine techniques to develop a new family of optical transceiver technologies, tailored to the needs of a new generation of self-x (x = configuring, monitoring, planning, learning, repairing and optimising) network architectures, capable of taking account of physical channel properties and high-level applications while optimising the use of resources. We will apply ML techniques to bring together the physical layer and the network; the nonlinearity of the fibres brings about a particularly complex challenge in the network context as it creates an interdependence between the signal quality of all transmitted wavelength channels. When optimising over tens of possible modulation formats, for hundreds of independent channels, over thousands of kilometres, a brute force optimisation becomes unfeasible. Particular challenges are the heterogeneity of large scale networks and the computational complexity of optimising network topology and resource allocation, as well as dynamical and data-driven management, monitoring and control of future networks, which requires a new way of thinking and tailored methodology. We propose to reduce the complexity of network design to allow self-learned network intelligence and adaptation through a combination of machine learning and probabilistic techniques. This will lead to the creation of computationally efficient approaches to deal with the complexity of the emerging nonlinear systems with memory and noise, for networks that operate dynamically on different time- and length-scales. This is a fundamentally new approach to optical network design and optimisation, requiring a cross-disciplinary approach to advance machine learning and heuristic algorithm design based on the understanding of nonlinear physics, signal processing and optical networking.