Optimization of spectral band utilization in gridless WDM optical network

Martins, Indayara Bertoldi; Aldaya, Ivan; Pérez-Sánchez, G. G.; Gallion, P.

doi:10.1117/12.2041229

Cited by 1 publication

(2 citation statements)

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“…However, traditional optical networks are facing significant challenges as network traffic grows rapidly, and new applications continue to emerge. Conventional wavelength-division-multiplexing (WDM) networks slice the spectrum into fixed sizes with fixed bandwidths and center frequencies [1]. In particular, fixed spectrum grid patterns limit the adaptability and flexibility of existing optical networks in dynamic and evolving network environments [2].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers first focused on the spectral performance of gridless networks. Martins, et al investigated gridless spectrum allocation in WDM optical networks and showed that gridless transmission systems reduce spectrum consumption while providing acceptable QoT [1]. Martí nez et al evaluated the impact of interchannel interference in a Nyquist-WDM-based gridless scheme and showed that backpropagation reduces power penalties, but overlap effects lead to higher BER penalties [3].…”

Section: Introductionmentioning

confidence: 99%

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Deep reinforcement learning-based routing and spectrum allocation for variable-bandwidth gridless optical networks

Huaizhao,

Ju,

Yang

et al. 2024

Advanced Fiber Laser Conference (AFL2023)

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With the diversified development for network service in terms of quality of service, bandwidth, and noise tolerance, variable-bandwidth gridless optical networks (V-GON) have become a promising technology for future networking. Unlike traditional elastic optical networks, which use orthogonal frequency division multiplexing for flexible allocation of subcarriers, V-GON adopts wave division multiplexing with variable modulation format and adjustable bandwidth, which can provide a more convenient way of data transmission. One of the key issues for V-GON is routing and spectrum allocation (RSA) due to the complexity of multi-channels. With complex modulation formats and different center wavelengths, an RSA algorithm needs to assign center frequencies, bandwidths, and modulation formats for each connection and also needs to consider the noise tolerance of the path. In this paper, we focus on optimizing routing, modulation format, and spectrum allocation by deep reinforcement learning to meet the demands of noise tolerance at different speeds. We proposed a reinforcement learning model to realize the availability forecast. The model can predict the feasibility of a pre-allocated optical spectrum. We conduct a numerical simulation to validate the performance. The results show that the proposed Graph Neural Network-Optical Path Availability Prediction algorithm can improve network efficiency, and utilization of spectral resources, and reduce the blocking ratio, which can support dynamic connectivity and interference reduction. In NSFNET network topology our proposed GNN-OPAP algorithm model reduces the loss by 95.88% compared to the DNN model and 64.23% compared to the LSTM model. In 4*mesh network topology, our proposed GNN-OPAP algorithm model reduces the loss by 16.14% compared to the DNN model and 76.33% to the LSTM model. This research provides new insights into the RSA problems in V-GON and provides a powerful reference for designing and optimizing future optical communication networks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%