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Maximizing Fiber Cable Capacity under a Supply Power Constraint using Deep Neural Networks., , , , , , , , , and 2 other author(s). OFC, page 1-3. IEEE, (2020)Optimizing Gain Shaping Filters with Neural Networks for Maximum Cable Capacity under Electrical Power Constraints., , , , , , , , , and 2 other author(s). ECOC, page 1-4. IEEE, (2020)Field Trial of 100nm Ultra-Wideband Optical Transport with 42GBd 16QAM Real-Time and 64GBd PCS64QAM Channels., , , , , , , , , and 12 other author(s). ECOC, page 1-3. IEEE, (2018)Submarine Cables: Deployment, Evolution, and Perspectives.. OFC, page 1-3. IEEE, (2018)Trans-Atlantic field trial using probabilistically shaped 64-QAM at high spectral efficiencies and single-carrier real-time 250-Gb/s 16-QAM., , , , , , , , , and 5 other author(s). OFC, page 1-3. IEEE, (2017)Submarine Cable Cost Reduction Through Massive SDM., , , , , , and . ECOC, page 1-3. IEEE, (2017)Supply-Power-Constrained Cable Capacity Maximization Using Deep Neural Networks., , , , , , , , , and 2 other author(s). CoRR, (2019)Bandwidth virtualization enables long-haul WDM transport of 40 Gb/s and 100 Gb/s services., , , , , and . IEEE Commun. Mag., (2008)Recent advancements in fiber optic transmission., , , , , , , , , and 5 other author(s). WOCC, page 14-19. IEEE, (2012)