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Found 19 papers, 3 papers with code
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow.
Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier
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.
Learning OFDM Waveforms with PAPR and ACLR Constraints
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.
Waveform Learning for Next-Generation Wireless Communication Systems
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.
Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems
Machine learning (ML) can be used in various ways to improve multi-user multiple-input multiple-output (MU-MIMO) receive processing.
End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints
Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment.
End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
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.
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 waveforms in wireless communication systems due to its efficient implementation.
Toward a 6G AI-Native Air Interface
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.
Machine Learning for MU-MIMO Receive Processing in OFDM Systems
Machine learning (ML) starts to be widely used to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) receivers.
End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication
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).
Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems
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).
Deep HyperNetwork-Based MIMO Detection
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem.
Trainable Communication Systems: Concepts and Prototype
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
Joint Learning of Geometric and Probabilistic Constellation Shaping
In this work, we show how autoencoders can be leveraged to perform probabilistic shaping of constellations.
Towards Hardware Implementation of Neural Network-based Communication Algorithms
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.
Model-free Training of End-to-end Communication Systems
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.
Deep Reinforcement Learning Autoencoder with Noisy Feedback
However, this approach requires feedback of real-valued losses from the receiver to the transmitter during training.
Information Theory Information Theory
End-to-End Learning of Communications Systems Without a Channel Model
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.
