Space division multiplexed optical networks: When, where and how
1 October 2020
Using space division multiplexing (SDM) technologies in metropolitan and core networks in order to solve the internet capacity crunch.
Funder EPSRC & BT
Amount £ 136 000
Research topics Multi core/mode fibres | SDM based optical switching | SDM network graphs and architecture | Multi-dimensional resource allocation and routing | AI and ML for programmable and open systems
Space division multiplexing enabled by various types of specialty fibres allowing for multiple spatial channels (cores, modes or their combination) within the cladding diameter is considered the most promising solution to overcome the capacity saturation imposed by Shannon-limit. However, Multi-Core Fibres experience static and dynamic inter-core crosstalk is dependent on signal format, signal-to-carrier power ratio, PRBS, baud-rate, temperature, wavelength.These dependencies are also unique to fibre structures (core pitch, core radius, trench and cladding parameters of each core that lead to various effective indices, propagation constant, coupling coefficient, etc). This leads to a multi-dimensional optimization problem both in terms of fibre structure, transmission, switching and networking.
This program will focus on network modelling in order to explore the deployment and challenges imposed by MCF fibre systems and address the following questions:
• When and where to deploy MCFs using BT’s traffic prediction models, current and future topologies
• What flavour of MCFs (single mode or few-mode MCF, homogeneous or heterogeneous).
• How to monitor, control, optimize, estimate and predict quality and performance.
All networking studies to date don’t consider the intricate static and dynamic physical layer characteristics of MCFs and how this could affect network performance, reliability, and operations.
We aim to form models that can estimate static and dynamic crosstalk dependencies at link and network-level as a first step. However, since this is a multi-dimensional problem it is critical to explore real-time monitoring methods and use of Artificial Intelligence for optimization, control, and prediction. Ultimately the project will develop methods and technologies with a practical solution in mind that can offer a step change from SMF based networks as well as identify its limits.