2 code implementations • 22 Mar 2022 • Jakob Hoydis, Sebastian Cammerer, Fayçal Ait Aoudia, Avinash Vem, Nikolaus Binder, Guillermo Marcus, Alexander Keller
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow.
no code implementations • 14 Jan 2022 • Dani Korpi, Mikko Honkala, Janne M. J. Huttunen, Fayçal Ait Aoudia, Jakob Hoydis
In particular, we consider a scenario where the transmitter power amplifier is operating in a nonlinear manner, and ML is used to optimize the waveform to minimize the out-of-band emissions.
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 • 2 Sep 2021 • Fayçal Ait Aoudia, Jakob Hoydis
We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector.
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 • 29 Jun 2021 • 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 can support these emerging applications becomes increasingly challenging.
no code implementations • 20 Jan 2021 • Fayçal Ait Aoudia, Jakob Hoydis
Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation.
no code implementations • 15 Dec 2020 • Jakob Hoydis, Fayçal Ait Aoudia, Alvaro Valcarce, Harish Viswanathan
Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Massive multiple-input multiple-output (MIMO) for 5G.
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 • 11 Sep 2020 • Fayçal Ait Aoudia, Jakob Hoydis
The first comes from a neural network (NN)-based receiver operating over a large number of subcarriers and OFDM symbols which allows to significantly reduce the number of orthogonal pilots without loss of bit error rate (BER).
no code implementations • 10 Apr 2020 • Fayçal Ait Aoudia, Jakob Hoydis
We introduce a trainable coded modulation scheme that enables joint optimization of the bit-wise mutual information (BMI) through probabilistic shaping, geometric shaping, bit labeling, and demapping for a specific channel model and for a wide range of signal-to-noise ratios (SNRs).
no code implementations • 7 Feb 2020 • 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.
no code implementations • 29 Nov 2019 • Sebastian Cammerer, Fayçal Ait Aoudia, Sebastian Dörner, Maximilian Stark, Jakob Hoydis, Stephan ten Brink
We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-wise mutual information (BMI) allows seamless integration with practical bit-metric decoding (BMD) receivers, as well as joint optimization of constellation shaping and labeling.
Information Theory Signal Processing Information Theory
no code implementations • 18 Jun 2019 • Maximilian Stark, Fayçal Ait Aoudia, Jakob Hoydis
In this work, we show how autoencoders can be leveraged to perform probabilistic shaping of constellations.
no code implementations • 19 Feb 2019 • Fayçal Ait Aoudia, Jakob Hoydis
There is a recent interest in neural network (NN)-based communication algorithms which have shown to achieve (beyond) state-of-the-art performance for a variety of problems or lead to reduced implementation complexity.
no code implementations • 14 Dec 2018 • Fayçal Ait Aoudia, Jakob Hoydis
The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model.
1 code implementation • 12 Oct 2018 • Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis
However, this approach requires feedback of real-valued losses from the receiver to the transmitter during training.
Information Theory Information Theory
1 code implementation • 6 Apr 2018 • Fayçal Ait Aoudia, Jakob Hoydis
The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model.