Multi-core Fiber and Power-limited Optical Network Topology Optimization with MILP

TL;DR

This paper presents a MILP-based method for optimizing multi-core fiber optical network topologies, considering power levels, component placement, and routing, with applications in safety-critical systems like aircraft networks.

Contribution

It introduces a novel MILP formulation that accounts for optical switch types, attenuation, and resource restrictions, enabling globally optimal topology solutions.

Findings

Successfully optimized an aircraft cabin network topology in under 30 minutes.

Validated the approach on five scenarios, each yielding optimal solutions.

Demonstrated the method's ability to handle complex constraints and component variations.

Abstract

Optical networks with multi-core fibers can replace several electronics networks with a single topology. Each electronic link is replaced by a single fiber, which can save space, weight, and cost, while having better segregation and EMI resistance. This is, for instance, of high interest in safety-critical cyber-physical systems, such as aircraft avionics networks. Finding the optimal topology requires finding the optimal number of components, component locations, inter-meshing, and signal routing, while assuring the appropriate optical power level at each participating device. A Mixed-integer Linear Programming (MILP) representation is presented for the optimization of the topology of optical multi-core fiber networks. The optimization approach retrieves a globally optimal topology with respect to weight or cost, i.e. it builds the optimal network topology from a set of switch and cable types, which differ in the number of fibers, attenuation, connectors, and other properties. The novelty of our approach is the consideration of translucent and opaque optical switches as well as the representation of cable and device attenuation directly in the MILP constraints. Moreover, arbitrary installation and routing resource restrictions are considered. The application of the approach to five dedicated scenarios yields in each case an optimal solution and validates our method. The application to an excerpt of an aircraft cabin network shows the retrieval of the global optimum in less 30 min for a topology with 48 signals and 23 components.