Digital Predistortion of D-Band Power Amplifiers Using AI-Based Nonlinearity Compensation
Published in IEEE Middle East Conference on Communications and Networking (MECOM),, 2025
This paper presents the first demonstration of a neural network-based digital predistortion (DPD) architecture for compensating nonlinearities in real D-band power amplifiers (PAs). The proposed method employs two separate models to independently correct amplitude (AM/AM) and phase (AM/PM) distortions. Training data is generated through simulation using a Rapp model under varying nonlinearity levels, and further validated using measurements from real D-band hardware. The AM/AM network predicts the ideal input magnitude required to produce a linearized output, while the AM/PM network compensates for the associated phase shifts. Evaluation results demonstrate improvements in linearity, with the DPD-corrected output closely matching the behavior of an ideal limiter. Model compression techniques are applied to reduce memory and computational requirements with minimal impact on performance
Recommended citation: Alqasir Hiba, Justine Mauro, Ihsen Alouani and Iyad Dayoub. "Digital Predistortion of D-Band Power Amplifiers Using AI-Based Nonlinearity Compensation." 2025 IEEE Middle East Conference on Communications and Networking (MECOM), 2025.
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