Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks
| Autor: | Alexandre Graell i Amat, Yibo Wu, Ulf Gustavsson, Henk Wymeersch |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
Artificial neural network Computational complexity theory Computer Networks and Communications Computer science Transmitter Residual Quadrature (mathematics) FOS: Electrical engineering electronic engineering information engineering Pruning (decision trees) Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering Layer (object-oriented design) Joint (audio engineering) Algorithm |
| Zdroj: | IEEE Journal on Selected Areas in Communications. 40:54-64 |
| ISSN: | 1558-0008 0733-8716 |
| Popis: | Neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity. We propose two methods to reduce complexity without significant performance penalty. We first propose a novel attention residual learning NN, referred to as attention residual real-valued time-delay neural network (ARDEN), where trainable neuron-wise shortcut connections between the input and output layers allow to keep the attention always active. Furthermore, we implement a NN pruning algorithm that gradually removes connections corresponding to minimal weight magnitudes in each layer. Simulation and experimental results show that ARDEN with pruning achieves better performance for compensating frequency-dependent quadrature imbalance and power amplifier nonlinearity than other NN-based and Volterra-based models, while requiring less or similar complexity. 9 pages, 8 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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