no code implementations • 26 Feb 2024 • Shanglin Yang, Yohann Benedic, Dinh-Thuy Phan-Huy, Jean-Marie Gorce, Guillaume Villemaud
We accurately simulate the ambient waves from a BS of Orange 4G commercial network, inside an existing large building covered with ZED beacons, thanks to a ray-tracing-based propagation simulation tool.
no code implementations • 8 Jun 2022 • Mateus P. Mota, Alvaro Valcarce, Jean-Marie Gorce
In this paper, we apply an multi-agent reinforcement learning (MARL) framework allowing the base station (BS) and the user equipments (UEs) to jointly learn a channel access policy and its signaling in a wireless multiple access scenario.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 21 Oct 2021 • Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis, Jean-Marie Gorce
An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characteristics.
no code implementations • 16 Aug 2021 • Mateus P. Mota, Alvaro Valcarce, Jean-Marie Gorce, Jakob Hoydis
In this paper, we propose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) to come up with a medium access control (MAC) protocol in a multiple access scenario.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 30 Jun 2021 • Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis, Jean-Marie Gorce
Machine learning (ML) can be used in various ways to improve multi-user multiple-input multiple-output (MU-MIMO) receive processing.
no code implementations • 30 Jun 2021 • Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis, Jean-Marie Gorce
Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment.
no code implementations • 15 Dec 2020 • Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis, Jean-Marie Gorce
Machine learning (ML) starts to be widely used to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) receivers.
no code implementations • 20 May 2019 • Cyrille Morin, Leonardo Cardoso, Jakob Hoydis, Jean-Marie Gorce, Thibaud Vial
Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others.