Physics Informed Spiking Neural Networks: Application to Digital Predistortion for Power Amplifier Linearization
Author:
Affiliation:
1. CNRS, Lab. de Génie Electrique et Electronique de Paris, Sorbonne Université, Paris, France
2. CentraleSupélec, CNRS, Lab. de Génie Electrique et Electronique de Paris, Université Paris-Saclay, Gif-sur-Yvette, France
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10122961.pdf?arnumber=10122961
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3. A million spiking-neuron integrated circuit with a scalable communication network and interface
4. Subthreshold neuromorphic devices for Spiking Neural Networks applied to embedded A.I
5. Decorrelation-Based Concurrent Digital Predistortion With a Single Feedback Path
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1. The Influence of Temporal Dependency on Training Algorithms for Spiking Neural Networks;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27
2. Study of Spiking Neural Network-Based Regressor on Applications in Digital Predistortion for Power Amplifiers;2023 6th International Conference on Advanced Communication Technologies and Networking (CommNet);2023-12-11
3. A Study on Real-Valued Models for Wideband RF Power Amplifier Modeling;IEEE Microwave and Wireless Technology Letters;2023-12
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