Fast Nonlinear Fourier Transform Algorithms Using Higher Order Exponential Integrators
December 03, 2018 Β· Declared Dead Β· π IEEE Access
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Shrinivas Chimmalgi, Peter J. Prins, Sander Wahls
arXiv ID
1812.00703
Category
eess.SP: Signal Processing
Cross-listed
cs.IT
Citations
36
Venue
IEEE Access
Last Checked
6 months ago
Abstract
The nonlinear Fourier transform (NFT) has recently gained significant attention in fiber optic communications and other engineering fields. Although several numerical algorithms for computing the NFT have been published, the design of highly accurate low-complexity algorithms remains a challenge. In this paper, we present new fast forward NFT algorithms that achieve accuracies that are orders of magnitudes better than current methods, at comparable run times and even for moderate sampling intervals. The new algorithms are compared to existing solutions in multiple, extensive numerical examples.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Signal Processing
R.I.P.
π»
Ghosted
π
π
The Cartographer
1D Convolutional Neural Networks and Applications: A Survey
R.I.P.
π»
Ghosted
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
π
π
The Cartographer
Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond
R.I.P.
π»
Ghosted
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
R.I.P.
π»
Ghosted
A New Wireless Communication Paradigm through Software-controlled Metasurfaces
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted