Cost-Efficient Baseband DPD for Hybrid MIMO Systems with Shallow Learning Artificial Neural Networks
Jueschke P, Stedile-Ribeiro T, Fischer G (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2022-June
Pages Range: 788-790
Conference Proceedings Title: IEEE MTT-S International Microwave Symposium Digest
Event location: Denver, CO, USA
ISBN: 9781665496131
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).
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Germany (DE)How to cite
APA:
Jueschke, P., Stedile-Ribeiro, T., & Fischer, G. (2022). Cost-Efficient Baseband DPD for Hybrid MIMO Systems with Shallow Learning Artificial Neural Networks. In IEEE MTT-S International Microwave Symposium Digest (pp. 788-790). Denver, CO, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Jueschke, Patrick, Thales Stedile-Ribeiro, and Georg Fischer. "Cost-Efficient Baseband DPD for Hybrid MIMO Systems with Shallow Learning Artificial Neural Networks." Proceedings of the 2022 IEEE/MTT-S International Microwave Symposium, IMS 2022, Denver, CO, USA Institute of Electrical and Electronics Engineers Inc., 2022. 788-790.
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