A Modular 1D-CNN Architecture for Real-time Digital Pre-distortion

• URL: http://arxiv.org/abs/2111.09637v1
• Date: Thu, 18 Nov 2021 11:30:23 GMT
• Title: A Modular 1D-CNN Architecture for Real-time Digital Pre-distortion
• Authors: Udara De Silva (1), Toshiaki Koike-Akino (1), Rui Ma (1), Ao Yamashita (2), Hideyuki Nakamizo (2) ((1) Mitsubishi Electric Research Labs, Cambridge, MA, USA, (2) Mitsubishi Electric Corporation, Information Tech. R&D Center, Kanagawa, Japan)
• Abstract summary: This study reports a novel hardware-friendly modular architecture for implementing one dimensional convolutional neural network (1D-CNN) digital predistortion (DPD) technique to linearize RF power amplifier (PA) real-time. The experimental results with 100 MHz signals show that the proposed 1D-CNN obtains superior performance compared with other neural network architectures for real-time DPD application.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study reports a novel hardware-friendly modular architecture for implementing one dimensional convolutional neural network (1D-CNN) digital predistortion (DPD) technique to linearize RF power amplifier (PA) real-time.The modular nature of our design enables DPD system adaptation for variable resource and timing constraints.Our work also presents a co-simulation architecture to verify the DPD performance with an actual power amplifier hardware-in-the-loop.The experimental results with 100 MHz signals show that the proposed 1D-CNN obtains superior performance compared with other neural network architectures for real-time DPD application.

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