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Today, Ïã¸ÛÁùºÏ²Ê researchers, along with collaboratorsÌýat Aston and Cambridge Universities, have launched a 6-year research programme to developÌýan adaptive and intelligent optical network, transforming the way networks are designed today to cope with the yet unknown needs of tomorrow's society.

The goal of the new research programme,Ìýentitled 'Transforming networks - building an intelligent optical infrastructure (TRANSNET)',Ìýis to create inteligent optical fibre networks, automatically assessing need and dynamically providing capacity.

Optical networks underpin the entire digital communications and data infrastructure, yet are a finiteÌýresource, that is to say, there is a maximum amountÌýof data that can travel down each fibre at any one time. TRANSNET will enable theÌýuse of this finite resource with maximum efficiency.

The research will investigate the use of state-of-the-art artificial intelligence (AI) and machine learning(ML) techniques to controlÌýelements of the network in responseÌýto different requirements and priorities of the users.ÌýÌý

TRANSNET is funded by a £Ìý6.1m researchÌýgrant from the Engineering and Physical Science Research Council (EPSRC). In addition, the research is undertaken in collaboration with 28 industrial and academic partners contributing a further £5.6m to the work.Ìý

Professor Polina Bayvel CBE, Co-Director of the Institute of Communications and Connected Systems and head of the Ïã¸ÛÁùºÏ²Ê Optical Networks research group, will lead theÌýprogramme. Explaining the work Polina said:

“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.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.ÌýWe envisage that machine learningÌýwill be integrated throughoutÌýfuture 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.

Expressing the importance of the work Professor Bayvel went on to say:

With the vast demands placed on data networks we are soon going to meet a crunch point where the yet un-known needs of our future society will not be met by current infrastructure. The next-generation of digital infrastructure needs more than raw capacity - it requiresÌýflexible resource and capacity provision in combination with low latency, simplified and modular network architectures toÌýmaximise data throughput when and where it is needed. This needs to be achieved with network resilience combined with overallÌýsecurity.