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Space division multiplexed elastic optical networks (SDM-EONs) enhance service provisioning by offering increased fiber capacity through the use of flexible spectrum allocation, multiple spatial modes, and efficient modulations. In these networks, the problem of allocating resources for connections involves assigning routes, modulations, cores, and spectrum (RMCSA). However, the presence of intercore crosstalk (XT) between ongoing connections on adjacent cores can degrade signal transmission, necessitating proper handling during resource assignment. The use of multiple modulations in translucent optical networks presents a challenge in balancing spectrum utilization and XT accumulation. In this paper, we propose a dual-optimized RMCSA algorithm called the Capacity Loss Aware Resource Assignment Algorithm (CLARA+), which optimizes network capacity utilization to improve resource availability and network performance. A two-step machine-learning-enabled optimization is used to improve the resource allocations by balancing the tradeoff between spectrum utilization and XT accumulation with the help of feature extraction from the network. Extensive simulations demonstrate that CLARA+ significantly reduces bandwidth blocking probability and enhances resource utilization across various scenarios. We show that our strategy applied to a few algorithms from the literature improves the bandwidth blocking probability by up to three orders of magnitude. The algorithm effectively balances spectrum utilization and XT accumulation more efficiently compared to existing algorithms in the literature.
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Exploration of Optical Signal QoT Margin with Intelligent WSS Filtering Penalty Estimator Using Neural Network
Deployment of 5G requires increased data trans-mission capacity in the metro fiber network. Besides deploying new dark fiber operators are also looking into solutions that improve fiber spectrum utilization by means of high-order modulation formats, flexible grid, and subcarrier multiplexing (SCM) technologies. An important factor that limits fiber spectrum utilization in metro network is the penalty inflicted on the optical signals that are routed by wavelength selective switches (WSS). In this paper, an intelligent WSS filtering penalty estimator is proposed based on neural network. With the achieved accuracy of 0.34 dB of mean absolute error in estimating the optical signal-to-noise ratio penalties caused by WSS filtering, the trained neural network is applied to estimate the fiber throughput gains that can be obtained by optimally selecting the signal symbol rate in a number of use cases.
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- Award ID(s):
- 1956357
- PAR ID:
- 10292080
- Date Published:
- Journal Name:
- 2021 International Conference on Optical Network Design and Modeling (ONDM)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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