OwnDPDLab: A Flexible Open-Source Testbed for Wideband DPD Algorithm Benchmarking
arXiv:2607.11612
The paper introduces OwnDPDLab, an open‑source, low‑cost testbed built on an RFSoC platform for benchmarking wideband digital predistortion (DPD) algorithms up to 1 GHz, and demonstrates its use to linearize a power amplifier with OFDM 256‑QAM signals using both memory‑polynomial and neural‑network DPD models.
Abstract
5G and Beyond-5G standards require digital predistortion (DPD) algorithms to operate on increased signal bandwidths. Wideband laboratory test hardware is cost-intensive, and openly available solutions lack flexibility. The OwnDPDLab provides a highly flexible, affordable, open-source, and openly accessible system. It is based on the RFSoC 4x2 and supports full control of center frequency, sampling mode, output power, and input attenuation at a signal bandwidth of up to 1 GHz. The system's capability is demonstrated by linearizing a laboratory power amplifier using a 196.608 MHz orthogonal frequency division multiplexing (OFDM) signal with 256-QAM modulation using both a memory polynomial and an augmented real-valued time-delay neural network in the first and second Nyquist zone. The system achieves a normalized mean squared error improvement of up to 23 dB and an adjacent channel leakage ratio improvement of up to 11 dB, using DPD.