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Found 10 papers, 2 papers with code
Deep Learning-Based Pilotless Spatial Multiplexing
This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO) communication systems.
DeepTx: Deep Learning Beamforming with Channel Prediction
The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase.
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.
HybridDeepRx: Deep Learning Receiver for High-EVM Signals
In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion.
DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations
Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers.
DeepRx: Fully Convolutional Deep Learning Receiver
To this end, we propose a deep fully convolutional neural network, DeepRx, which executes the whole receiver pipeline from frequency domain signal stream to uncoded bits in a 5G-compliant fashion.
Video Ladder Networks
Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder.
Bidirectional Recurrent Neural Networks as Generative Models
Although unidirectional RNNs have recently been trained successfully to model such time series, inference in the negative time direction is non-trivial.
Semi-Supervised Learning with Ladder Networks
We combine supervised learning with unsupervised learning in deep neural networks.
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series
Although unidirectional RNNs have recently been trained successfully to model such time series, inference in the negative time direction is non-trivial.
