Bandwidth, Power and Carrier Configuration Resilient Neural Networks Digital Predistorter
This paper proposes a neural networks predistorter based on the bidirectional long-short-term memory (BiLSTM) structure. The proposed predistorter was trained while ensuring that it captures the full intrinsic behavior of the device under test including its memory effects and nonlinear distortions....
Main Authors: | Asma Ali, Oualid Hammi |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10155407/ |
Similar Items
-
Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China
by: Ishan Ayus, et al.
Published: (2023-05-01) -
An investigation of multivariate data-driven deep learning models for predicting COVID-19 variants
by: Akhmad Dimitri Baihaqi, et al.
Published: (2024-06-01) -
Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks
by: Adan Antonio Alonso-Ramirez, et al.
Published: (2022-01-01) -
Hybrid Digital/Analog Predistorter Architecture With Enhanced Robustness to Hardware Impairments
by: Majid Ahmed, et al.
Published: (2024-01-01) -
CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> Concentration
by: Prasanjit Dey, et al.
Published: (2024-01-01)