Ai-based Physical Layer Enhancement for 6g

il y a 4 jours

Rouen, France Université de Rouen Normandie - LABORATOIRE de recherche LITIS Temps plein

**AI-based physical layer enhancement for 6G Communications**:

- Réf **ABG-127340**
- Stage master 2 / Ingénieur- Durée 6 mois- Salaire net mensuel 600€- 03/12/2024- Université de Rouen Normandie - LABORATOIRE de recherche LITIS- Lieu de travail- Rouen Normandie France- Champs scientifiques- Télécommunications
- Informatique- 31/12/2024**Établissement recruteur**:
Le LITIS, Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes, est une unité de recherche (UR 4108) en sciences et technologies de l’information rattachée à l’Université de Rouen Normandie (URN), l’Université Le Havre Normandie (ULHN) et l’Institut National des Sciences Appliquées de Rouen Normandie (INSARN). Le LITIS est né en janvier 2006 de la volonté des membres des laboratoires STIC de Haute-Normandie, et des trois établissements de tutelle, d’unir leurs forces pour valoriser les synergies existantes et renforcer leur visibilité.

Depuis sa création, le LITIS est structuré en sept équipes.

Le LITIS est membre de la Fédération de Recherche CNRS NormaSTIC (FR CNRS 3638) avec le GREYC (UMR CNRS 6072), regroupant ainsi les deux unités de recherche normandes dans le domaine des STIC.

**Description**:
AI has emerged as a major player in the development of the next generation of wireless communication systems, where machine learning (ML) algorithms are widely explored for various tasks to improve the performance of the PHY layer. The research trend in wireless communication systems is mainly focused on the sixth generation of cellular networks (6G).

These expectations are probably motivated by the advantages promised by 6G, which can ensure high data throughput and reliability, with low latency and also, a high level of security against cyber-attacks. However, in dynamic scenarios such as in the case of a connected autonomous vehicle (CAV), which uses systems that include multiple MIMO antennas, wider bandwidths, and a higher degree of temporal variations, the performance losses are significant. Furthermore, ML machine learning algorithms are very well suited to overcome these limitations. ML approaches require a large dataset to train the models, however, in wireless communication systems, labelled data can be easily generated.

The goal of this internship is to design and implement an AI-based architecture for 6G Physical layer. Optimize the proposed AI-based architecture to enhance performance metrics such as bit error rate (BER) and reduce computational complexity.

**Profil**:

- Solid knowledge of radio access technologies such as MIMO, OFDM, physical layers

and/or MAC layers.
- Experience in artificial intelligence and machine learning (AI/ML) algorithms and

software tools (PyTorch, TensorFlow, scikit-learn).
- Excellent communication and writing skills.
- 3GPP standards knowledge is a plus.
- Hands-on Python and C++ coding skills.

**Prise de fonction**:

- 03/03/2025

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