6 months internship leading to a PhD (funded) on deep learning applied to massive MIMO systems.
Contact : email@example.com
The internship will be part of the MoBAIWL project (Model-Based frugal AI for efficient WireLess communication systems), which aims to design efficient data processing methods for future wireless communication systems (6G and beyond), using physical models to structure, initialize and train frugal artificial intelligence methods. In particular, multi-antenna systems (hybrid massive MIMO) will be considered. They may greatly enhance the spectral and energy efficiency of wireless network by focusing precisely radiated waves. However, in order to unleash their full potential, such systems require complex data processing that can be tackled using either signal processing or machine learning methods. In order to achieve a satisfying trade-off between these two approaches, model-based learning has been introduced recently  and led to promising results in various fields of wireless systems [2–6].
The main objectives of the internship are the following: (O1) Designing a model-based learning strategy in order to jointly estimate the channel and choose an appropriate precoder in an hybrid MIMO system. (O2) Comparing the developed method to existing purely data-driven approaches . (O3) (Optional) Adapting the method to exhibit robustness to the most common hardware impairments [8, 9]. One interesting lead for (O1) is to combine the channel estimation method of  with the precoding approach of  to obtain an end-to-end learning method. Regarding (O3), it is possible to include impairments in a sparse model of the channel with a specific parameterization of the used dictionary, similarly to what is proposed in [2, 11]. If everything go as planned, the results of the internship should lead to the submission of an article to an international conference.
The internship will be hosted in the SIGNAL team of the IETR (on the campus of INSA Rennes), for a duration of six months starting between January and March of 2024. Students in their final year (M2/PFE) with a background/interest in signal processing, machine learning and applied mathematics are encouraged to apply by sending an email to firstname.lastname@example.org. The internship is thought of as a preparation for a PhD on a related topic (whose funding will also come from the MoBAIWL project).
 Nir Shlezinger, Jay Whang, Yonina C Eldar, and Alexandros G Dimakis. Model-based deep learning. Proceedings of the IEEE, 2023.
 Taha Yassine and Luc Le Magoarou. mpnet: variable depth unfolded neural network for massive mimo channel estimation. IEEE Transactions on Wireless Communications, 21(7):5703–5714, 2022.
 Nhan Thanh Nguyen, Mengyuan Ma, Nir Shlezinger, Yonina C Eldar, AL Swindlehurst, and Markku Juntti. Deep unfolding hybrid beamforming designs for thz massive mimo systems. arXiv preprint arXiv:2302.12041, 2023.
 Jérôme Sol, Hugo Prod’Homme, Luc Le Magoarou, and Philipp del Hougne. Experimentally realized physical-model-based wave control in metasurface-programmable complex media. arXiv preprint arXiv:2308.02349, 2023.
 José Miguel Mateos-Ramos, Christian H¨ager, Musa Furkan Keskin, Luc Le Magoarou, and Henk Wymeersch. Model-based end-to-end learning for multi-target integrated sensing and communication. arXiv preprint arXiv:2307.04111, 2023.
 Baptiste Chatelier, Luc Le Magoarou, Vincent Corlay, and Matthieu Crussière. Model-based learning for location-to-channel mapping. arXiv preprint arXiv:2308.14370, 2023.
 Qiyu Hu, Yunlong Cai, Kai Kang, Guanding Yu, Jakob Hoydis, and Yonina C Eldar. Two-timescale end-to-end learning for channel acquisition and hybrid precoding. IEEE Journal on Selected Areas in Communications, 40(1):163–181, 2021.
 Tim Schenk. RF imperfections in high-rate wireless systems: impact and digital compensation. Springer Science & Business Media, 2008.
 Hui Chen, Musa Furkan Keskin, Sina Rezaei Aghdam, Hyowon Kim, Simon Lindberg, Andreas Wolfgang, Traian E Abrudan, Thomas Eriksson, and Henk Wymeersch. Modeling and analysis of 6g joint localization and communication under hardware impairments. arXiv preprint arXiv:2301.01042, 2023.
 Ortal Agiv and Nir Shlezinger. Learn to rapidly optimize hybrid precoding. In 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), pages 1–5, 2022.
 Baptiste Chatelier, Luc Le Magoarou, and Getachew Redieteab. Efficient deep unfolding for siso-ofdm channel estimation. arXiv preprint arXiv:2210.06588, 2022.
(c) GdR 720 ISIS - CNRS - 2011-2022.