Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM
arXiv:2603.00101v1 Announce Type: new 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 character
arXiv:2603.00101v1 Announce Type: new 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.
Executive Summary
This article proposes a novel amplitude conditioned long short-term memory (AC-LSTM) network for wideband power amplifier behavioral modeling. The AC-LSTM incorporates a Feature-wise Linear Modulation (FiLM) layer that conditions the LSTM's forget gate on the instantaneous input amplitude, allowing for enhanced modeling of wideband PA dynamics. Experimental validation demonstrates improved time-domain accuracy and spectral fidelity compared to standard LSTM and ARVTDNN baselines. The AC-LSTM achieves a normalized mean square error (NMSE) of -41.25 dB and an adjacent channel power ratio (ACPR) of -28.58 dB, showcasing its effectiveness in wideband PA behavioral modeling.
Key Points
- ▸ AC-LSTM proposes a novel architecture for wideband power amplifier behavioral modeling
- ▸ Feature-wise Linear Modulation (FiLM) layer conditions the LSTM's forget gate on input amplitude
- ▸ Experimental validation demonstrates improved performance compared to standard LSTM and ARVTDNN baselines
Merits
Physics-aware inductive bias for capturing amplitude-dependent memory effects
The incorporation of FiLM layer provides a physics-aware inductive bias, enabling the model to capture amplitude-dependent memory effects in wideband PA dynamics.
Demerits
Dependence on specific hardware and signal characteristics
The proposed AC-LSTM may require extensive retraining or modification to accommodate different hardware configurations or signal characteristics.
Expert Commentary
The article makes a significant contribution to the field of wideband power amplifier modeling, leveraging the capabilities of deep learning to improve time-domain accuracy and spectral fidelity. The proposed AC-LSTM architecture demonstrates a clear understanding of the physics underlying wideband PA dynamics and showcases the potential of physics-aware inductive bias in enhancing model performance. However, the dependence of the AC-LSTM on specific hardware and signal characteristics may limit its broader applicability. Further research is needed to explore the generalizability of the proposed architecture and its potential applications in various contexts.
Recommendations
- ✓ Future research should focus on developing more robust and adaptable architectures that can accommodate different hardware configurations and signal characteristics.
- ✓ The AC-LSTM architecture should be further explored in the context of other wireless communication systems and applications, such as satellite communications and cognitive radio.
