End-to-end Learning for GMI Optimized Geometric Constellation Shape
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Abstract (arXiv)
Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings. Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the presence of transceiver impairments. The gains can be harvested with standard binary FEC at no cost w.r.t. conventional BICM.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jul 2019.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 1.
4 papers.
- 2022Hybrid Neural Coded Modulation: Design and Training Methodscited by 2
- 2022Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communicationcited by 2
- 2022Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learningcited by 2
- 2024Friendly Attacks to Improve Channel Coding Reliabilitycited by 1
- 2024Friendly Attacks to Improve Channel Coding Reliability25 Jan 2024
- 2022Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning8 Nov 2022
- 2022Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication5 Apr 2022
- 2022Hybrid Neural Coded Modulation: Design and Training Methods4 Feb 2022