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Found 8 papers, 0 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.
Downlink Coverage and Rate Analysis of Low Earth Orbit Satellite Constellations Using Stochastic Geometry
As low Earth orbit (LEO) satellite communication systems are gaining increasing popularity, new theoretical methodologies are required to investigate such networks' performance at large.
Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity Digital Predistortion
The results show that the linearization capabilities of the proposed methods are very close to that of the ordinary MP DPD, particularly with the proposed SMP approach, while having substantially lower processing complexity.
