Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM

• URL: http://arxiv.org/abs/2603.00101v1
• Date: Tue, 17 Feb 2026 20:16:11 GMT
• Title: Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM
• Abstract summary: This paper proposes a novel amplitude conditioned long short-term memory (AC-LSTM) network to enhance the modeling of wideband PA dynamics.<n>The architecture incorporates a Feature-wise Linear Modulation layer that conditions the LSTM's forget gate on the instantaneous input amplitude.<n> 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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