Abstract
A machine learning-based low-cost monitoring technique for transmitter IQ phase and gain imbalances is proposed. Simulations with formats up to NRZ-64QAM (28 GBd) show 95%- confidence estimation within 1.5° for phase and 0.06 for gain imbalances.
Original language | English |
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Title of host publication | CLEO |
Subtitle of host publication | Science and Innovations, CLEO_SI 2018 |
Publisher | OSA - The Optical Society |
Volume | Part F94-CLEO_SI 2018 |
ISBN (Electronic) | 9781557528209 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
Event | CLEO: Science and Innovations, CLEO_SI 2018 - San Jose, United States Duration: 13 May 2018 → 18 May 2018 |
Conference
Conference | CLEO: Science and Innovations, CLEO_SI 2018 |
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Country/Territory | United States |
City | San Jose |
Period | 13/05/18 → 18/05/18 |
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Mechanics of Materials