Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks
Artikel i vetenskaplig tidskrift, 2022

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 the complexity without significant performance penalty. First, propose a novel NN with shortcut connections, referred to as shortcut real-valued time-delay neural network (SVDEN), where trainable neuron-wise shortcut connections are added between the input and output layers. Second, we implement a NN pruning algorithm that gradually removes connections corresponding to minimal weight magnitudes in each layer. Simulation and experimental results show that SVDEN 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.

hardware impairment mitigation

in-phase (I) and quadrature (Q) imbalance

digital predistortion (DPD)

Neural networks (NNs)

power amplifier


Yibo Wu

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Ericsson AB

Ulf Gustavsson

Ericsson AB

Alexandre Graell I Amat

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

IEEE Journal on Selected Areas in Communications

0733-8716 (ISSN) 15580008 (eISSN)

Vol. 40 1 54-64

Djup RF

Ericsson AB, 2020-01-01 -- 2024-12-31.

Stiftelsen för Strategisk forskning (SSF) (DnrID19-0021), 2020-01-01 -- 2024-12-31.


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