用轻量TCN模型实现宽带功放高效非线性补偿
TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion
- 基于因果扩张卷积与优化激活函数的轻量化网络结构
- 仅500参数即达ACPR -51.58 dBc、EVM -47.52 dB
- 参数可压缩至200仍优于已有模型，适合资源受限场景
数字预失真（DPD）对抑制射频功率放大器的非线性至关重要，尤其在宽带应用中。本文提出TCN-DPD，一种基于时序卷积网络的参数高效架构，结合非因果扩张卷积与优化激活函数。在OpenDPD框架和DPA_200MHz数据集上评估，该模型仅用500个参数便实现模拟ACPR -51.58/-49.26 dBc（L/R）、EVM -47.52 dB、NMSE -44.61 dB，且在参数减少至200时仍保持优于先前模型的线性化性能，展现出在高效宽带功放线性化中的潜力。
原文摘要 · Abstract (English)
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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