TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion

TL;DR

This paper introduces TCN-DPD, a parameter-efficient temporal convolutional network architecture for wideband digital predistortion, achieving high linearization performance with significantly fewer parameters than previous models.

Contribution

The paper proposes a novel TCN-based architecture for digital predistortion that is highly parameter-efficient and effective for wideband RF power amplifier linearization.

Findings

Achieves ACPRs of -51.58/-49.26 dBc with 500 parameters

Maintains superior linearization down to 200 parameters

Outperforms prior models in wideband PA linearization

Abstract

Digital predistortion (DPD) is essential for mitigating nonlinearity in RF power amplifiers, particularly for wideband applications. This paper presents TCN-DPD, a parameter-efficient architecture based on temporal convolutional networks, integrating noncausal dilated convolutions with optimized activation functions. Evaluated on the OpenDPD framework with the DPA_200MHz dataset, TCN-DPD achieves simulated ACPRs of -51.58/-49.26 dBc (L/R), EVM of -47.52 dB, and NMSE of -44.61 dB with 500 parameters and maintains superior linearization than prior models down to 200 parameters, making it promising for efficient wideband PA linearization.