Year: 2025
Type: conference-paper
Abstract: Digital Predistortion (DPD) is essential for enhancing the efficiency of power amplifiers (PAs) in modern wireless communication systems by mitigating nonlinear distortions and memory effects. Neural network-based DPD has emerged as a powerful approach due to its ability to model complex PA behaviors. In this work, we propose TinyMLDPD, a lightweight neural network-based DPD that maintains high accuracy while reducing hardware complexity. Our approach employs a fully connected feedforward neural network inference which utilizes 821 coefficients. In addition, we present a symmetric bipartite table method (SBTM) for efficient activation function computation. Implemented on an AMD Zynq UltraScale+ ZCU106 field-programmable gate array (FPGA) Device, the proposed architecture is flexible enough to support online training with reasonable enhancements, making it a practical solution for realworld deployment.
Institution: Oklahoma State University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411640502