Mu, Xun; (2023) Multi-core Fibre Design and Analysis using Artificial Intelligence. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

The emerging cloud services and communication/computation applications exponentially increase data traffic and demand, exceeding the standard single-mode fibre’s capacity. It renders the development of high-capacity fibre necessary. Space Division Multiplexing (SDM), as one of the auspicious solutions, has been intensively researched in the past decade. Among the members in SDM, the single-mode Multi-Core Fibre (MCF) in standard cladding diameter has the highest potential to be applied in the existing fibre transmission systems. It can prevent complex multi-input multi-output digital signal processing while having negligible inter-core Crosstalk (XT) and being compatible with the standard single-mode fibre and the relative components. Regarding the single-mode MCF design, there are multiple parameters to optimise due to several variants of the core index profile per type of core and heterogeneous structure which can lead to lower XT. Meanwhile, multiple properties need to be addressed to meet the application requirements and maintain feasibility. These make it time- and computation-consuming to optimise MCF comprehensively. Therefore, an automatic and efficient artificial intelligence-based toolkit has been proposed in this thesis for MCF design and optimisation. In the toolkit, for the sake of computation efficiency, machine learning methods were used to do the regression of optical properties and then replace the numerical methods, which results in up to 106× speed up. Particle Swarm Optimisation (PSO) algorithm optimises the particles which each represent one MCF structure according to the fitness value given by the objective function. Each objective function is designed to include the objective and the constraints of properties based on the application requirements. 6-core and 8-core fibres in standard cladding diameter were designed for three objectives respectively: minimising XT, maximising effective mode area, or maximising fabrication yield. Moreover, based on the optimised MCFs, the relationships between the properties and capacity also were investigated thoroughly. Further suggestions were given for future MCF design.

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