Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model
arXiv:1912.05146v242 citations
Originality Incremental advance
AI Analysis
This work addresses a bottleneck in optical communication system design for researchers and engineers, though it appears incremental as it builds on existing deep learning methods.
The authors tackled the problem of optimizing optical intensity-modulation/direct-detection (IM/DD) systems by using a generative adversarial network to approximate the experimental channel, eliminating the need for simplified channel models.
We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel. Previously, optimization was only possible through a prior assumption of an explicit simplified channel model.