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03/20/2023
Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling
Sionna is a GPU-accelerated open-source library for link-level simulatio...
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07/29/2022
Graph Neural Networks for Channel Decoding
In this work, we propose a fully differentiable graph neural network (GN...
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05/22/2022
Deep Learning-Based Synchronization for Uplink NB-IoT
We propose a neural network (NN)-based algorithm for device detection an...
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03/22/2022
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Sionna is a GPU-accelerated open-source library for link-level simulatio...
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01/27/2022
Convolutional Self-Attention-Based Multi-User MIMO Demapper
In orthogonal frequency division multiplexing (OFDM)-based wireless comm...
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01/14/2022
Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier
Machine learning (ML) has shown great promise in optimizing various aspe...
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10/21/2021
Learning OFDM Waveforms with PAPR and ACLR Constraints
An attractive research direction for future communication systems is the...
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09/02/2021
Waveform Learning for Next-Generation Wireless Communication Systems
We propose a learning-based method for the joint design of a transmit an...
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06/30/2021
Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems
Machine learning (ML) can be used in various ways to improve multi-user ...
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06/30/2021
End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints
Orthogonal frequency-division multiplexing (OFDM) is widely used in mode...
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06/29/2021
End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
As communication systems are foreseen to enable new services such as joi...
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01/20/2021
Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning
Orthogonal frequency division multiplexing (OFDM) is one of the dominant...
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12/15/2020
Towards a 6G AI-Native Air Interface
Each generation of cellular communication systems is marked by a definin...
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12/15/2020
Machine Learning for MU-MIMO Receive Processing in OFDM Systems
Machine learning (ML) starts to be widely used to enhance the performanc...
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09/11/2020
End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication
Previous studies have demonstrated that end-to-end learning enables sign...
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04/10/2020
Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems
We introduce a trainable coded modulation scheme that enables joint opti...
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02/07/2020
Deep HyperNetwork-Based MIMO Detection
Optimal symbol detection for multiple-input multiple-output (MIMO) syste...
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11/29/2019
Trainable Communication Systems: Concepts and Prototype
We consider a trainable point-to-point communication system, where both ...
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06/18/2019
Joint Learning of Geometric and Probabilistic Constellation Shaping
The choice of constellations largely affects the performance of communic...
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02/19/2019
Towards Hardware Implementation of Neural Network-based Communication Algorithms
There is a recent interest in neural network (NN)-based communication al...
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12/14/2018
Model-free Training of End-to-end Communication Systems
The idea of end-to-end learning of communication systems through neural ...
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10/12/2018
Deep Reinforcement Learning Autoencoder with Noisy Feedback
End-to-end learning of communication systems enables joint optimization ...
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04/06/2018