ESA Open Invitation to Tender: 1-10937
Open Date: 22/12/2021 16:33 CEST
Closing Date: 05/04/2022 05:00 CEST

Objective: To develop applications for machine learning algorithms in optical communication systems. Targeted Improvements: Ten fold speed increase in traffic routing of optical communication signals in a satellites, air-borne and optical ground stations network. Optimisation of optical communication-link acquisition time. Description: Machine learning is a subset of artificial intelligence (AI) and is addressing computer algorithms that improve automatically through experience without being explicitly programmed. Machine learning is closely related to computational statistics aiming making predictions using computer programmes accessing data and use it learn for themselves. In the field of optical communication machine learning techniques allow for different optimisation tasks. Simulation experiments show that machine learning could be an appropriate solution for developing a full self-managing future network. Machine learning algorithms are also useful to optimise acquisition times in optical communication systems and in the field of adaptive optics to improve their performance. This activity will investigate the potential improvementsachievable by machine learning techniques in the following fields:- Spatial acquisition optimisation of Optical Communication Terminals- Frequency acquisition optimisation in coherent homodyne communication systems- Optimisation of traffic routing in satellite and ground based network- Adaptive Optics (AO) wave-front estimation for terabit feeder links.

Estabilishment: ESTEC
ECOS Required: No
Classified: No
Price Range: 200-500 KEURO
Authorised Contact Person: Audrey Ferreol
Initiating Service: TIA-TIS
IP Measure: N/A
Prog. Reference: E/0534-05A – OP-Other Act. Prod.
Tender Type: Open Competition
Open To Tenderers From: AT+BE+CA+CH+CZ+DE+DK+EL+ES+FR+GB+HU+IE+IT+LU+NL+NO+PL+PT+RO+SE
Technology Keywords: 16-C-VI-Optical Communications
Products Keywords: 2-G-1-a-Optical terminals