Cost-Efficient Baseband DPD for Hybrid MIMO Systems with Shallow. Learning Artificial Neural Networks - Jüschke, Patrick; Stedile Ribeiro, Thales; Fischer, Georg (IEEE MTT-S International Microwave Symposium Digest, 2022)
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).<br
Keywords: Hybrid mMIMO; Artificial Neural Networks (ANN); Digital Predistortion (DPD); Shared Feedback Path

Feed-forward neural networks for analog impairment mitigation in high power RF transceivers - Jüschke, Patrick; Brendel, Johannes; Fischer, Georg; Pascht, Andreas (Asia-Pacific Microwave Conference (APMC), 2011)
Constantly rising capacity and increasing complexity of mobile communication systems as well as the general demand to reduce the power consumption to get the systems greener, are a big challenge especially for radio frontends. Complex modulated signals of mobile communication standards like LTE have high demands on radio frontends regarding signal requirements. the numerous variety of different standards in different frequency bands have further demands on radio transceivers and its architecture. Flexible radios, suitable for different standards, signals and frequencies with highest efficiency and dynamic are required. This paper shows possibilities to enhance the flexibility of RF transceivers and how to enable future standards and handle high requirements while relaxing configuration using neural networks for signal processing in RF transceivers.
elib2cris::1512001600,jueschke2011

Machine Learning using Neural Networks in Digital Signal Processing for RF Transceivers - Jüschke, Patrick; Fischer, Georg (IEEE Africon, 2017)

Neural Network based efficiency optimization method for RF Power Amplifiers with controllable Power Supply - Jüschke, Patrick; Pascht, Andreas; Fischer, Georg (AFRICON, 2013)
elib2cris::1512001600,jueschke2013

Neural Network Based Linearization of RF Power Amplifiers Using In-Situ Device Temperature Measurement - Jüschke, Patrick; Fischer, Georg (IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), 2015)

Thermal Memory Effect Characterization of GaN based Class ABJ Power Amplifier using Intrinsic Temperature Measurement - Jüschke, Patrick; Fischer, Georg (IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR), 2018)