Multi-Period Upgrade Scheduling for Ultra-Low Loss Optical Fiber
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Solution Overview
Problem
Current backbone optical networks using conventional single-mode fibers struggle to meet the demands of ultra-high speed and ultra-long distance transmission due to decreased repeaterless transmission distance as transmission speed increases, necessitating the use of ultra-low loss optical fibers, but lack effective multi-period upgrade and scheduling methods.
Innovation Solution
A multi-period upgrade scheduling method for ultra-low loss optical fibers involves setting periods for upgrade, generating and evaluating upgrade sequences, and iteratively replacing sequences based on gain calculations and probability formulas to maximize spectrum utilization, ensuring efficient deployment and replacement of ULL optical fibers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If conventional single-mode fibers are used for backbone optical networks, then device complexity is reduced and ease of manufacture is improved, but transmission distance decreases and bandwidth demand cannot be met
Solution Approach 1:
The optical fiber upgrade process is divided into multiple periods or stages, where each period involves upgrading a subset of fiber links. This segmentation allows the complex replacement task to be managed in manageable increments, reducing overall complexity while achieving the goal of extending transmission distance through ultra-low loss fiber deployment.
Solution Approach 2:
The system performs preliminary analysis and planning to identify which fiber links should be upgraded in each period. By pre-calculating the optimal upgrade sequence based on traffic demand, transmission distance requirements, and cost considerations, the system prepares the upgrade path before actual implementation, reducing on-site complexity.
2Length of moving object
If ultra-low loss optical fibers are deployed to increase transmission distance, then transmission distance is improved, but spectrum resource utilization becomes suboptimal without proper scheduling
Solution Approach 1:
The upgrade scheduling system dynamically adjusts the deployment plan based on changing traffic demands, network conditions, and resource availability. Rather than following a static upgrade schedule, the system continuously optimizes which links to upgrade next, ensuring that spectrum resources are utilized efficiently while achieving transmission distance goals.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor spectrum resource utilization and transmission performance after each upgrade period. This feedback is used to refine subsequent upgrade decisions, ensuring that future deployments maximize spectrum efficiency while continuing to extend transmission distance where needed.
3Length of moving object
If multiple periods of fiber upgrade are implemented, then transmission distance and bandwidth are improved, but the complexity of scheduling and coordination increases
Solution Approach 1:
The multi-period upgrade process is segmented into discrete, manageable periods with clear objectives for each. Each period focuses on upgrading specific fiber links that provide the most value, breaking down the complex multi-year planning into simpler annual or quarterly targets that are easier to coordinate and execute.
Solution Approach 2:
Rather than attempting to upgrade the entire network at once or following a perfectly optimized but complex global schedule, the system implements partial upgrades in each period that provide sufficient improvement. This approach accepts that each individual period may not achieve absolute optimality, but the cumulative effect across multiple periods achieves the desired transmission distance and bandwidth improvements with manageable coordination complexity.
Data Source
AI summary
The invention provides a multi-period upgrade scheduling method, including: setting a quantity of periods for optical fiber upgrade, and determining a maximum optical fiber length of upgrade in each period; randomly generating an upgrade scheduling sequence, and calculating a total upgrade gain of the sequence; randomly generating a new upgrade scheduling sequence, and calculating a total upgrade gain of the current sequence; calculating a difference obtained by subtracting a gain of the previous sequence from a gain of the current sequence, if the difference is greater than or equal to 0, replacing the previous upgrade scheduling sequence with the current upgrade scheduling sequence, and if the difference is less than 0, accepting the current upgrade scheduling sequence according to a probability value formula; and performing multiple iterations, and if a new upgrade scheduling sequence has not been updated when a set quantity of iterations is reached, terminating iteration.