Search Results for author: David S. Millar

Found 5 papers, 0 papers with code

DNN-assisted optical geometric constellation shaped PSK modulation for PAM4-to-QPSK format conversion gateway node

no code implementations23 Feb 2021 Takahiro Kodama, Toshiaki Koike-Akino, David S. Millar, Keisuke Kojima, Kieran Parsons

An optical gateway to convert four-level pulse amplitude modulation to quadrature phase shift keying modulation format having shaping gain was proposed for flexible intensity to phase mapping which exploits non-uniform phase noise.

Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links

no code implementations5 Aug 2020 Pavel Skvortcov, Ian Phillips, Wladek Forysiak, Toshiaki Koike-Akino, Keisuke Kojima, Kieran Parsons, David S. Millar

Huffman-coded sphere shaping (HCSS) is an algorithm for finite-length probabilistic constellation shaping, which provides nearly optimal energy efficiency at low implementation complexity.

Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables

no code implementations12 Jun 2020 Tobias Fehenberger, David S. Millar, Toshiaki Koike-Akino, Keisuke Kojima, Kieran Parsons, Helmut Griesser

In this paper, we study amplitude shaping schemes for the probabilistic amplitude shaping (PAS) framework as well as algorithms for constant-composition distribution matching (CCDM).

Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation

no code implementations22 Nov 2019 Toshiaki Koike-Akino, Ye Wang, David S. Millar, Keisuke Kojima, Kieran Parsons

Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts.

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