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

• URL: http://arxiv.org/abs/2506.12165v1
• Date: Fri, 13 Jun 2025 18:30:32 GMT
• Title: TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion
• Authors: Huanqiang Duan, Manno Versluis, Qinyu Chen, Leo C. N. de Vreede, Chang Gao,
• Abstract summary: TCN-DPD is a parameter-efficient architecture based on temporal convolutional networks.<n>It 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.<n>It maintains superior linearization than prior models down to 200 parameters, making it promising for efficient wideband PA linearization.
• Score: 3.6966254731864727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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