Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM

TL;DR

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 is proposed.

Abstract

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.

Figures (6)

• Figure 1: The Proposed Amplitude Conditioned LSTM cell
• Figure 2: Three-stage workflow for PA behavioral modeling: (a) Data Acquisition & Pre-processing, (b) Model Training, and (c) Testing & Evaluation.
• Figure 3: General Network Architecture
• Figure 4: Laboratory measurement setup for data acquisition.
• Figure 5: Gallium Nitride (GaN) Power Amplifier Hardware used as DUT.