Recurrent Neural Network-based Digital Predistortion for wideband Radio Frequency Power Amplifier

Abstract

With the advancement of 5G/6G radio networks, the demand for high-performance power amplifiers (PAs) with clean spectra and compact constellations has increased significantly. To address these challenges, Artificial Intelligence (AI)-based digital predistortion (DPD) has emerged as a promising approach to linearize radio-frequency (RF) PAs. However, existing state-of-the-art AI-based architectures rely on computationally expensive online feature extraction to achieve satisfying linearization performance, resulting in complicated algorithm data paths and difficulty in energy-efficient hardware implementation. This thesis proposes a new deep recurrent neural network (RNN)-based DPD architecture, called Skip Gated Recurrent Unit (SGRU), with precise offline baseband signal feature extraction to bypass the need for complex online feature extraction while still maintaining high linearization performance. The proposed RNN architecture employs the end-to-end (E2E) learning framework to implement an efficient DPD model. By combining the offline feature extraction and E2E framework, we achieved a more streamlined and faster training method for wideband RF power amplifier (PA) DPD. With a simplified neural network architecture and fewer parameters, our approach utilizes 394 parameters to achieve an adjacent channel power ratio (ACPR) (lower/upper) of -45.16/-44.31 dBc for 100 MHz orthogonal frequency division multiplexing (OFDM) signal, ACPR (lower/upper) of -38.44/-42.09 dBc for 200 MHz OFDM signal. Compared to previous state-of-art phase gated just-another-network (PG-JANET) [1] and decomposed vector rotation just-another-network (DVR-JANET) [2], our approach has better ACPR and error vector magnitude (EVM) performance with parameters around 400. Compared to vector decomposition long-short-term memory (VDLSTM) [3], our approach achieves a better lower/upper band balance.