AI-aided FEC code design and decoding – AI4CODE
The AI4CODE project brings together 6 research team with strong expertise in the design, decoding and standardization of forward-error-correction codes. The aim is to develop skills in artificial intelligence and machine learning, and to explore how learning techniques can contribute to the improvement of code design methods (by using less parameters, more relevant heuristics, producing stronger codes) and decoders (better performance, reduced complexity or energy consumption), on selected scenarios of practical interest for which a full theoretical understanding is still lacking. The proposed methodology is to augment legacy design methods and decoders with learning capabilities or decision support systems wherever relevant, rather than replacing them by a generic, black-box neural network, so that we can inspect the trained solutions and try to infer why they work better. Our ultimate goal is to obtain new theoretical hindsight that could translate into better codes and decoders.
Project coordination
Raphaël Le Bidan (Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
Partner
LAB-STICC Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance
IRIT Institut de Recherche en Informatique de Toulouse
LETI Laboratoire d'Electronique et de Technologie de l'Information
ETIS Equipes Traitement de l'Information et Systèmes
IMS LABORATOIRE D'INTEGRATION DU MATERIAU AU SYSTEME
LAB-STICC Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance
Help of the ANR 636,667 euros
Beginning and duration of the scientific project:
October 2021
- 48 Months
