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Found 8 papers, 0 papers with code
Time vs. Frequency Domain DPD for Massive MIMO: Methods and Performance Analysis
The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs).
Impact of Phase Noise on Uplink Cell-Free Massive MIMO OFDM
Cell-Free massive MIMO networks provide huge power gains and resolve inter-cell interference by coherent processing over a massive number of distributed instead of co-located antennas in access points (APs).
Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink
In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas.
MCRB-based Performance Analysis of 6G Localization under Hardware Impairments
Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems.
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning.
Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks
Neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity.
Distortion-Aware Linear Precoding for Massive MIMO Downlink Systems with Nonlinear Power Amplifiers
By studying the spatial characteristics of the distortion, we demonstrate that conventional linear precoding techniques steer nonlinear distortions towards the users.
Information Theory Signal Processing Information Theory
Residual Neural Networks for Digital Predistortion
Tracking the nonlinear behavior of an RF power amplifier (PA) is challenging.
