MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers

1 code implementation • 18 Apr 2024 • Yizhuo Wu, Ang Li, Mohammadreza Beikmirza, Gagan Deep Singh, Qinyu Chen, Leo C. N. de Vreede, Morteza Alavi, Chang Gao

Applied to a 160MHz-BW 1024-QAM OFDM signal from a digital RF PA, MP-DPD gives no performance loss against 32-bit floating-point precision DPDs, while achieving -43. 75 (L)/-45. 27 (R) dBc in Adjacent Channel Power Ratio (ACPR) and -38. 72 dB in Error Vector Magnitude (EVM).

OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion

1 code implementation • 16 Jan 2024 • Yizhuo Wu, Gagan Deep Singh, Mohammadreza Beikmirza, Leo C. N. de Vreede, Morteza Alavi, Chang Gao

With the rise in communication capacity, deep neural networks (DNN) for digital pre-distortion (DPD) to correct non-linearity in wideband power amplifiers (PAs) have become prominent.

Benchmarking