Ensuring maximum impact, by partnering with key players throughout the community
Academic leaders across both optical networks and machine learning
Find out about current opportunities to join the TRANSNET team.
TRANSNET is a new £6.1m, 6- year research programme, funded by EPSRC transforming optical networks for future society needs. The programme is in collaboration with 28 industrial and academic partners, contributing an additional £5.6M in various ways.
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.
Our vision and ambition are 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.
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.