Partial Spectrum Reconfiguration in Optical Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional Routing and Wavelength Assignment (RWA) and Routing and Spectrum Assignment (RSA) algorithms in optical networks do not effectively address partial reconfiguration of services, leading to sub-optimal wavelength reduction and unbounded implementation costs, particularly in larger networks, as they consider all services equally and lack efficient methods to minimize the number of reconfiguration steps.
Innovation Solution
A partial optimization method that involves two stages: the first stage optimizes which services to change by utilizing Integer Linear Programming or genetic algorithms to select routes and wavelength assignments, and the second stage uses dual, parallel heuristic optimization, including genetic algorithms and simulated annealing, to determine the order of changes that minimize conflicts and step counts, thereby reducing wavelength count and implementation effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional RWA/RSA algorithms reconfigure all services to optimize wavelength assignment, then wavelength count reduction is achieved, but implementation cost and complexity become unbounded
Solution Approach 1:
The patent applies partial reconfiguration by selecting only a subset of services (p-SNCs) for reconfiguration rather than all services. The optimization algorithm determines the optimal proportion of services to reconfigure, balancing wavelength reduction benefits against implementation cost. This partial action principle resolves the contradiction by avoiding the unbounded complexity of reconfiguring all services while still achieving meaningful wavelength count reduction.
Solution Approach 2:
The patent introduces a proportion parameter (ratio of services to reconfigure) as a controllable variable. By adjusting this parameter, the system can optimize the trade-off between wavelength reduction and implementation cost. The optimization algorithm varies this parameter to find the optimal point where marginal benefits of reconfiguration diminish relative to increasing complexity.
2Device complexity
If conventional algorithms consider all services equally for reconfiguration, then optimization is simplified, but wavelength reduction is sub-optimal
Solution Approach 1:
The patent applies local quality by differentiating the treatment of services based on their characteristics. Not all services are treated equally; instead, the optimization algorithm identifies and prioritizes specific services (p-SNCs) for reconfiguration based on their route, wavelength assignment, and impact on overall optimization. This selective approach achieves superior wavelength reduction compared to uniform treatment.
Solution Approach 2:
The patent segments the set of all services into two groups: services to be reconfigured and services to remain unchanged. The optimization algorithm determines which segment should be reconfigured to achieve optimal wavelength reduction. This segmentation allows the system to focus computational resources on the most impactful services rather than treating all services uniformly.
3Ease of operation
If sequential reconfiguration of services is performed without bound on steps, then implementation is simplified, but reconfiguration cost increases
Solution Approach 1:
The patent applies preliminary action by performing optimization calculations before actual reconfiguration implementation. The optimization algorithm pre-determines the optimal set of services and the optimal sequence of reconfiguration steps. This preliminary planning phase identifies the most efficient reconfiguration path, minimizing the number of steps required during actual implementation while maintaining operational simplicity.
Solution Approach 2:
The patent introduces dynamic sequencing where the reconfiguration order is optimized based on current network state. The algorithm determines the optimal sequence of reconfiguration steps adaptively, considering temporary wavelength allocations and conflicts. This dynamic approach minimizes the total number of steps compared to fixed sequential reconfiguration, reducing implementation time and cost.
Data Source
AI summary
Partial optimization systems and methods of wavelengths or spectrum in an optical network include, based on current services in the optical network each having a route and wavelength assignment and based on a ratio of services that can be changed in the partial optimization, preforming a first stage optimization to determine which of the current services are changed for one or more of the route and the wavelength assignment to attain a reduction of a number of wavelengths; performing a second stage optimization to determine an order of implementing changes from the first stage optimization that with the order minimizing one or more of conflicts and step counts; and causing implementation of the changes in the optical network.