2 citations · 2 across the 1 of their papers we have counts for
6 papers
Learning OFDM Waveforms with PAPR and ACLR Constraints
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characterist…
Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
Machine learning (ML) can be used in various ways to improve multi-user multiple-input multiple-output (MU-MIMO) receive processing. Typical approaches either augment a single proc…
End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment. However, it suffers from a p…
Machine Learning for MU-MIMO Receive Processing in OFDM Systems
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
Machine learning (ML) starts to be widely used to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) receivers. However, it is still unclear if such met…
Deep HyperNetwork-Based MIMO Detection
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Conventional heuristic algorithms are either too complex to be practic…
Deep Reinforcement Learning Autoencoder with Noisy Feedback
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis
End-to-end learning of communication systems enables joint optimization of transmitter and receiver, implemented as deep neural network-based autoencoders, over any type of channel…