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Found 11 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).
Blind Channel Equalization Using Vector-Quantized Variational Autoencoders
Furthermore, we show that for the linear channel, the proposed scheme exhibits better convergence properties than the \ac{MMSE}-based, the \ac{CMA}-based, and the \ac{VAE}-based equalizers in terms of both convergence speed and robustness to variations in training batch size and learning rate.
Spatial Signal Design for Positioning via End-to-End Learning
This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning.
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.
Iterated Posterior Linearization PMB Filter for 5G SLAM
In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter.
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning.
End-to-End Learning for Integrated Sensing and Communication
The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure.
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.
Residual Neural Networks for Digital Predistortion
Tracking the nonlinear behavior of an RF power amplifier (PA) is challenging.
