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

IEEE MTT-S International Microwave Symposium digest, pp. 1103–1106

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_200 MHz dataset, TCN-DPD achieves simulated ACPRs of$-51.58 /-49.26 \text{dBc}$(L/R), EVM of -47.52 dB, and NMSE of -44.61 dB with 500 parameters and maintain superior linearization than prior models down to 200 parameters, making it promising for efficient wideband PA linearization.

Authors 3

1. Qinyu Chen Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS), Leiden University,The Netherlands

2. Leo C. N. de Vreede

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology,Department of Microelectronics,The Netherlands

3. Chang Gao

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology,Department of Microelectronics,The Netherlands