用输入幅度调节LSTM门控，提升宽带功放建模精度
Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM
- 通过幅度条件门控增强LSTM对记忆效应的捕捉能力
- 在5G NR信号下NMSE达-41.25 dB，优于基线模型1.15~7.45 dB
- 适合通信系统中高精度功放建模与射频设计人员
宽带功率放大器表现出复杂的非线性与记忆效应，挑战传统行为建模方法。本文提出一种新型幅度条件长短期记忆网络（AC-LSTM），通过引入显式的幅度依赖门控机制，增强对宽带功放动态特性的建模能力。该架构结合特征自适应线性调制（FiLM）层，将LSTM的遗忘门条件化于瞬时输入幅度，赋予物理感知的归纳偏置，以捕捉幅度相关的记忆效应。基于100 MHz 5G NR信号与氮化镓（GaN）功率放大器的实验验证表明，所提AC-LSTM实现-41.25 dB的归一化均方误差（NMSE），相较标准LSTM提升1.15 dB，相较增强型实值时延神经网络（ARVTDNN）提升7.45 dB。模型还准确复现了实测功放的频谱特性，邻道功率比（ACPR）为-28.58 dB。结果表明，幅度条件化显著提升了宽带功放行为建模的时间域精度与频谱保真度。
原文摘要 · Abstract (English)
Wideband power amplifiers exhibit complex nonlinear and memory effects that challenge traditional behavioral modeling approaches. This paper proposes a novel amplitude conditioned long short-term memory (AC-LSTM) network that introduces explicit amplitude-dependent gating to enhance the modeling of wideband PA dynamics. The architecture incorporates a Feature-wise Linear Modulation (FiLM) layer that conditions the LSTM's forget gate on the instantaneous input amplitude, providing a physics-aware inductive bias for capturing amplitude-dependent memory effects. Experimental validation using a 100 MHz 5G NR signal and a GaN PA demonstrates that the proposed AC-LSTM achieves a normalized mean square error (NMSE) of -41.25 dB, representing a 1.15 dB improvement over standard LSTM and 7.45 dB improvement over augmented real-valued time-delay neural network (ARVTDNN) baselines. The model also closely matches the measured PA's spectral characteristics with an adjacent channel power ratio (ACPR) of -28.58 dB. These results shows the effectiveness of amplitude conditioning for improving both time-domain accuracy and spectral fidelity in wide-band PA behavioral modeling.
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