Optical filtering penalty estimation using artificial neural network in elastic optical networks with cascaded reconfigurable optical add-drop multiplexers
Abstract
For future elastic optical networks, the narrow filtering effect induced by cascaded reconfigurable optical add-drop multiplexers (ROADMs) is one of the major impairments. It is essential to accurately estimate the filtering penalty to minimize network margins and optimize resource utilization. We present a method for estimating filtering penalty using machine learning (ML). First, we investigate the impact of ROADM location distribution and bandwidth allocation on the narrow filtering effect. Afterward, an ML-aided approach is proposed to estimate the filtering penalty under various link conditions. Extensive simulations with 9600 links are implemented to demonstrate the superior performance of the proposed scheme.
- Publication:
-
Optical Engineering
- Pub Date:
- July 2019
- DOI:
- 10.1117/1.OE.58.7.076105
- Bibcode:
- 2019OptEn..58g6105Z