Post-Doctoral Research Visit F / M Post-Doctoral Research Visit F / M Modern Grant-Free Access Techniques for Cellular Networks (AI and Experimentation)

Contexte et atouts du poste

This post-doctoral position is part of the

Mission confiée

The proposed position will focus on the development of grant-free access techniques for IoT applications.

One common communication scenario in IoT applications is massive machine-type communications (mMTC), where a large number of devices transmit sporadic, small packets. In traditional cellular systems, each device is allocated orthogonal resources prior to uplink transmission via a grant mechanism. However, this allocation requires signaling on control channels, which can exceed the data payload size and lead to inefficient resource use. Consequently, grant-free methods [1] that eliminate or reduce control traffic are well suited for these scenarios. Removing coordination introduces non-orthogonality, resulting in the superposition of signals from some or all devices.

A recent family of random access protocols—sometimes called "modern random access"—aims to address and even exploit this phenomenon. The IRSA protocols (Irregular Repetition Slotted ALOHA) [2,3,4] use Successive Interference Cancellation (SIC) and represent one form of grant-free technique, but they can also operate with any packet transmission scheme. They are related to "Unsourced Random Access" [7]. Adapting these methods for grant-free mMTC in cellular networks is therefore of prime interest and the main objective of this position.

Principales activités

The initial research direction in this post-doctoral position is to study variants of modern random access, incorporating realistic physical-layer features and methods (e.g., [5,6]), including cell-free systems.

Our main objectives are to enhance performance and practicality in cellular networks. IRSA-based protocols can operate with any modulation scheme, including existing transmission techniques, and do not necessarily require NOMA features such as advanced multi-user detection. However, they can benefit from NOMA methods that improve SIC, as well as ML methods to perform SIC itself (e.g. [9]).

We are particularly interested in improving the selection of transmission opportunities (e.g., using precomputed sequences), possibly constructed with machine learning techniques (as in [8]). We could also consider lightweight node synchronization to reduce signal superposition.

An important aspect is that we plan to do actual experiments on the .

References

[1] Muhammad Basit Shahab, Rana Abbas, Mahyar Shirvanimoghaddam, and Sarah J. Johnson. "Grant-free non-orthogonal multiple access for iot : A survey.IEEE Communications Surveys & Tutorials", 2020.

[2] Gianluigi Liva. "Graph-Based Analysis and Optimization of Contention Resolution Diversity Slotted ALOHA." IEEE Transactions on Communications, 59 : 477–487, 2011.

[3] Clazzer, Federico, Andrea Munari, Gianluigi Liva, Francisco Lazaro, Cedomir Stefanovic, and Petar Popovski. "From 5G to 6G : Has the time for modern random access come?." arXiv preprint arXiv : 1903.03063 .

[4] "Modern Random Access for Grant-Free Cellular Networks," C. Adjih, Tutorial, [6] Saeed Alsabbagh, Cédric Adjih, Amine Adouane, and Nadjib Aitsaadi "IRSA Under Capture Effect and Imperfect SIC : a de Analysis for Future Cellular IoT", PIMRC 2025, Sep. 2025

[7] G. Liva and Y. Polyanskiy, “Unsourced Multiple Access : A Coding Paradigm for Massive Random Access,” Proceedings of the IEEE, vol. 112, no. 9, pp. 1214–1229, Sep. 2024

[8] Iman Hmedoush, Pengwenlong Gu, Cedric Adjih, Paul Muhlethaler, Ahmed Serhrouchni, "DS-IRSA : A Deep Reinforcement Learning and Sensing Based IRSA" in IEEE Global Communications Conference - GLOBECOM 2023, Kuala Lumpour, Malaysia, December 04–08, 2023.

[9] N. Shlezinger, R. Fu, Y. C. Eldar, "DeepSIC : Deep Soft Interference Cancellation for Multiuser MIMO Detection,"" IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1349–1362, Feb. 2021.

Compétences

• Ph. D. in Computer Science, Telecommunications, Electrical Engineering, or a related field.
• Excellent programming skills, specifically in Python and good knowledge of machine learning frameworks.
• Strong background in telecommunication, including communication theory, random access, (machine learning, or artificial intelligence applied to telecommunications).
• Experience in programming SDR (Software Defined Radio) is a plus.
• Some experience in Sionna is a plus.

Avantages

Rémunération

2788€ gross per month