Synergizing Hyper-accelerated Power Optimization and Wavelength-Dependent QoT-Aware Cross-Layer Design in Next-Generation Multi-Band EONs
The extension of elastic optical networks (EON) to multi-band transmission (MB-EON) shows promise in enhancing spectral efficiency, throughput, and long-term cost-effectiveness for telecom operators. However, designing MB-EON networks introduces complex challenges, notably the optimization of physical parameters like optical power and quality of transmission (QoT). Frequency-dependent characteristics of fiber, such as loss, dispersion, and nonlinear effects, alongside inter-channel stimulated Raman scattering, pose significant hurdles when extending beyond the L+C (LC) band to a continuous spectrum over 100 nm. In this study, we propose a span-by-span methodology for optimal power allocation, introducing two hyper-accelerated power optimization (HPO) strategies: flat launch power (FLP) and flat received power (FRP). These approaches significantly expedite network power optimization while preserving the stability of running services. Our comparative analysis of FLP and FRP models reveals that while FRP has a minimal effect on capacity (increasing less than 10 Tbps for an L+C+S (LCS) system over 100 km), it improves flatness and GSNR/OSNR metrics in the S-band by approximately 2/0 dB and 2.5/6 dB, respectively. A network-wide analysis across various topologies shows that the FRP technique enhances minimum GSNR, contributing to a throughput increase of 12% to 75%, depending on network scale, at a 1% bandwidth blocking rate. Lastly, our application of HPO in MB-EON for both local and global power optimization demonstrates that while both approaches offer comparable performance, global optimization is simpler and more cost-effective for large-scale networks.
Code (0)
등록된 구현이 없습니다.
Tasks
Blockingglobal-optimizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Fast Hyperspectral Reconstruction for Neutron Computed Tomography Using Subspace Extraction
Hyperspectral neutron computed tomography enables 3D non-destructive imaging of the spectral characteristics of materials. In traditional hyperspectral reconstruction, the data for each neutron wavelength bin is reconstr…
Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
Our overarching goal is to develop an accurate and explainable model for plant disease identification using hyperspectral data. Charcoal rot is a soil borne fungal disease that affects the yield of soybean crops worldwid…
ClassificationGeneral ClassificationGlobal Riemannian Acceleration in Hyperbolic and Spherical Spaces
We further research on the accelerated optimization phenomenon on Riemannian manifolds by introducing accelerated global first-order methods for the optimization of $L$-smooth and geodesically convex (g-convex) or $\mu$-…
global-optimizationWavelength-aware 2D Convolutions for Hyperspectral Imaging
Deep Learning could drastically boost the classification accuracy for Hyperspectral Imaging (HSI). Still, the training on the mostly small hyperspectral data sets is not trivial. Two key challenges are the large channel …
Hyperspectral Image-Based Fruit Ripeness PredictionAn optimization problem on the performance of FSO communication system
Performance of Free Space Optical (FSO) communication system is affected by atmospheric turbulences and pointing errors. These effects can easily be mitigated by adapting natural system parameters such as wavelength. In …