Physics Informed Spiking Neural Networks: Application to Digital Predistortion for Power Amplifier Linearization
نویسندگان
چکیده
Recently, new emerging techniques of neuromorphic hardware render spiking neuron networks (SNN) promising as an energy-efficient solution for artificial intelligence (AI). With the idea physics informed neural network, structure can be simple while training data light. However its application in RF telecommunication system is still challenging. This paper, first time literatures, proposes a SNN-based digital predistortion (SNN-DPD) linearization transmitters, such power amplifiers (PA). A two-layer SNN deployed frequency domain to process spectrum stimulus predistorted signal. The proposed technique experimentally validated on test bench with real PA different bias voltages. We also SNN-DPD multi-band linearization. method reaches best performance traditional DPD methods owing advantages SNN, low consumption and good biomimicry AI.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3275434