34 citations · 36 across the 2 of their papers we have counts for
7 papers
Applications of Deep Learning to the Design of Enhanced Wireless Communication Systems
Mathieu Goutay
Innovation in the physical layer of communication systems has traditionally been achieved by breaking down the transceivers into sets of processing blocks, each optimized independe…
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…