Thiago Diniz - Academia.edu (original) (raw)
Related Authors
Jadavpur University, Kolkata, India
Uploads
Papers by Thiago Diniz
Journal of Lightwave Technology, 2017
We investigate the computational cost of the nonlinear Fourier transform (NFT) based on the Zakha... more We investigate the computational cost of the nonlinear Fourier transform (NFT) based on the Zakharov-Shabat scattering problem as a nonlinear compensation technique for quadrature-phase-shift keyed (QPSK) signals with raised cosine frequency characteristic in optical fiber transmission systems with normal dispersion fibers. We show that the primary sources of computational errors that arise from the use of the NFT is the finite eigenvalue resolution of the left and the right reflection spectra. We show that this effect and, consequently, the computational cost of the NFT as a nonlinear mitigation technique in the normal dispersion regime increases exponentially or faster with both the channel power and the number of symbols per data frame even using the most efficient NFT algorithms that are currently known. We find that the computational cost of this approach becomes unacceptably large at data frame lengths and powers that are too small for this approach to be competitive with standard transmission methods. We explain the physical reasons for these limits.
Journal of Lightwave Technology, 2017
We investigate the computational cost of the nonlinear Fourier transform (NFT) based on the Zakha... more We investigate the computational cost of the nonlinear Fourier transform (NFT) based on the Zakharov-Shabat scattering problem as a nonlinear compensation technique for quadrature-phase-shift keyed (QPSK) signals with raised cosine frequency characteristic in optical fiber transmission systems with normal dispersion fibers. We show that the primary sources of computational errors that arise from the use of the NFT is the finite eigenvalue resolution of the left and the right reflection spectra. We show that this effect and, consequently, the computational cost of the NFT as a nonlinear mitigation technique in the normal dispersion regime increases exponentially or faster with both the channel power and the number of symbols per data frame even using the most efficient NFT algorithms that are currently known. We find that the computational cost of this approach becomes unacceptably large at data frame lengths and powers that are too small for this approach to be competitive with standard transmission methods. We explain the physical reasons for these limits.