Abstract
In this Letter, we propose a real-time machine learning scheme of a tracking optical intensity-modulation and direct-detection (IMDD) system's conditional distribution using linear optical sampling and inline Gaussian mixer modeling (GMM) programming. End-to-end conditional distribution tracking enables an adaptive decoding of optical IMDD signals, with robustness to the bias point shift of the optical intensity modulator. Experimental demonstration is conducted over a 20-Gbits/s optical pulse amplitude modulation-4 (PAM-4) modulation system. Optical PAM- 4 signals are optically down-sampled by short pulses to 250 Msa/s. Then, statistical characters of signal distribution can be estimated using inline GMM processing. Due to the real-time learned distribution, intelligent decoding of received signals exhibits a perfect adaptation to the changing bias point of aMach-Zendner intensity modulator, enhancing the communication reliability with bit error rate (BER) below 3.8 . 10-3. In addition, the proposed scheme also provides the possibility of practical implementation to other machine learning signal decoding methods.
Original language | English |
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Pages (from-to) | 4426-4429 |
Number of pages | 4 |
Journal | Optics Letters |
Volume | 46 |
Issue number | 17 |
DOIs | |
Publication status | Published - 1 Sept 2021 |
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics