Cost-Efficient Baseband DPD for Hybrid MIMO Systems with Shallow. Learning Artificial Neural Networks

Jüschke P, Stedile Ribeiro T, Fischer G (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE MTT-S International Microwave Symposium Digest Institute of Electrical and Electronics Engineers

DOI: 10.1109/ims37962.2022.9865518

Abstract

Enhancing power efficiency and bandwidth of RF
power amplifiers (PAs) is always a challenge for future mobile
basestation transceivers. Linearization, most commonly as a
digital predistortion system (DPD), is essential to enhance power
efficiency of individual transmit paths. Especially massive MIMO
systems for 5G applications and beyond with multiple transmit
paths and PAs are challenging in respect of implementation effort
and resource costs. An individual signal processing block for each
transmit path is a possible but costly solution. This work shows
different solutions for a cost efficient design of a DPD system
for hybrid multi-user mMIMO applications with artificial neural
networks (ANNs).

Authors with CRIS profile

Patrick Jüschke Professur für Technische Elektronik Thales Stedile Ribeiro Professur für Technische Elektronik Georg Fischer Professur für Technische Elektronik

How to cite

APA:

Jüschke, P., Stedile Ribeiro, T., & Fischer, G. (2022). Cost-Efficient Baseband DPD for Hybrid MIMO Systems with Shallow. Learning Artificial Neural Networks. IEEE MTT-S International Microwave Symposium Digest. https://dx.doi.org/10.1109/ims37962.2022.9865518

MLA:

Jüschke, Patrick, Thales Stedile Ribeiro, and Georg Fischer. "Cost-Efficient Baseband DPD for Hybrid MIMO Systems with Shallow. Learning Artificial Neural Networks." IEEE MTT-S International Microwave Symposium Digest (2022).

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