TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion
This work addresses efficient wideband power amplifier linearization for RF systems, presenting an incremental improvement with optimized parameter efficiency.
The paper tackles the problem of mitigating nonlinearity in RF power amplifiers for wideband applications by proposing TCN-DPD, a parameter-efficient temporal convolutional network architecture, achieving simulated ACPRs of -51.58/-49.26 dBc, EVM of -47.52 dB, and NMSE of -44.61 dB with 500 parameters.
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.