96 citations · 268 across the 14 of their papers we have counts for
39 papers
Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR
Reinhard Wiesmayr, Sebastian Cammerer, Fayçal Aït Aoudia +3
We detail the steps required to deploy a multi-user multiple-input multiple-output (MU-MIMO) neural receiver (NRX) in an actual cellular communication system. This raises several e…
Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier
Dani Korpi, Mikko Honkala, Janne M. J. Huttunen +2
Machine learning (ML) has shown great promise in optimizing various aspects of the physical layer processing in wireless communication systems. In this paper, we use ML to learn jo…
Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding
Qiyu Hu, Yunlong Cai, Kai Kang +3
In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, w…
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer
Brian Rappaport, Emre Gönültaş, Jakob Hoydis +3
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state informa…
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…
End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
Fayçal Ait Aoudia, Jakob Hoydis
As communication systems are foreseen to enable new services such as joint communication and sensing and utilize parts of the sub-THz spectrum, the design of novel waveforms that c…