تقرير
Walsh-domain Neural Network for Power Amplifier Behavioral Modelling and Digital Predistortion
| العنوان: | Walsh-domain Neural Network for Power Amplifier Behavioral Modelling and Digital Predistortion |
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| المؤلفون: | Thys, Cel, Alonso, Rodney Martinez, Lhomel, Antoine, Fellmann, Maxandre, Deltimple, Nathalie, Rivet, Francois, Pollin, Sofie |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | This paper investigates the use of Neural Network (NN) nonlinear modelling for Power Amplifier (PA) linearization in the Walsh-Hadamard transceiver architecture. This novel architecture has recently been proposed for ultra-high bandwidth systems to reduce the transceiver power consumption by extensive parallelization of the digital baseband hardware. The parallelization is achieved by replacing two-dimensional quadrature modulation with multi-dimensional Walsh-Hadamard modulation. The open research question for this architecture is whether conventional baseband signal processing algorithms can be similarly parallelized while retaining their performance. A key baseband algorithm, digital predistortion using NN models for PA linearization, will be adapted to the parallel Walsh architecture. A straighforward parallelization of the state-of-the-art NN architecture is extended with a cross-domain Knowledge Distillation pre-training method to achieve linearization performance on par with the quadrature implementation. This result paves the way for the entire baseband processing chain to be adapted into ultra-high bandwidth, low-power Walsh transceivers. Comment: Accepted for presentation at the 2024 IEEE International Symposium on Circuits and Systems (ISCAS) |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/ISCAS58744.2024.10557970 |
| URL الوصول: | http://arxiv.org/abs/2402.09964 |
| رقم الأكسشن: | edsarx.2402.09964 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/ISCAS58744.2024.10557970 |
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