Optical network routing and spectrum allocation algorithm based on GCN-LSTM

By using a hybrid GCN-LSTM model to predict optical network traffic and dynamically select modulation formats, the problems of low spectral efficiency and high computational complexity in traditional optical network resource scheduling are solved, achieving efficient utilization and accurate prediction of spectrum resources in optical networks.

CN122027918APending Publication Date: 2026-05-12SHAOXING RES INST OF ZHEJIANG UNIV
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Patent Information

Application Number
CN202512026582.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-30
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

Existing optical network resource scheduling methods struggle to dynamically and collaboratively handle changes in network traffic, leading to bottlenecks in improving spectrum efficiency. Traditional methods suffer from resource waste and high computational complexity in spectrum allocation strategies.

Method used

A hybrid model based on GCN-LSTM is adopted, which uses graph convolutional networks to capture the spatial characteristics of network topology and long short-term memory networks to learn the temporal characteristics of traffic data, predicts future traffic and dynamically selects the optimal modulation format, and combines the K-shortest path algorithm for routing and spectrum allocation.

Benefits of technology

It improves the utilization rate and prediction accuracy of spectrum resources in optical networks, realizes intelligent resource allocation in dynamic network environments, and reduces spectrum channel occupancy.

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Abstract

The invention discloses an optical network routing and spectrum allocation algorithm based on a GCN-LSTM, and the algorithm comprises the steps: S1, defining an optical network environment, initializing the algorithm, and constructing a basic framework of a traffic prediction GCN-LSTM model; s2, according to historical traffic data in the network, network traffic data prediction is carried out in combination with a machine learning method; s3, predetermining an optimal modulation format based on the predicted flow result; and S4, performing efficient RSA decision according to the determined modulation format. According to the optical network routing and spectrum allocation algorithm based on the GCN-LSTM, optimization and improvement are carried out in the aspects of flow data prediction, flow dynamic threshold setting, modulation format determination and the like, and the problems that in a traditional heuristic algorithm, the spectrum resource utilization rate is low and the like can be solved.
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