Learning to Compensate: A Deep Neural Network Framework for 5G Power Amplifier Compensation

• Main Authors: Chen, Po-Yu, Chen, Hao, Tsai, Yi-Min, Kuo, Hsien-Kai, Huang, Hantao, Chen, Hsin-Hung, Yan, Sheng-Hong, Ou, Wei-Lun, Cheng, Chia-Ming
• Format: Conference Proceeding
• Language: English
• Subjects: 5G Communication; 5G mobile communication; Deep learning; Deep Neural Network; Digital Pre-Distortion; Frequency-domain analysis; Mean square error methods; Nonlinear distortion; Power Amplifier; Power amplifiers; Radio frequency
• ISSN: 1938-1883
• DOI: 10.1109/ICC42927.2021.9500277

Owing to the complicated characteristics of 5G communication system, designing RF components through math-ematical modeling becomes a challenging obstacle. Moreover, such mathematical models need numerous manual adjustments for various specification requirements. In this paper, we present a learning-based framework to model and compensate Power Amplifiers (PAs) in 5G communication. In the proposed frame-work, Deep Neural Networks (DNNs) are used to learn the characteristics of the PAs, while, correspondent Digital Pre-Distortions (DPDs) are also learned to compensate for the nonlinear and memory effects of PAs. On top of the framework, we further propose two frequency domain losses to guide the learning process to better optimize the target, compared to naive time domain Mean Square Error (MSE). The proposed framework serves as a drop-in replacement for the conventional approach. The proposed approach achieves an average of 56.7% reduction of nonlinear and memory effects, which converts to an average of 16.3% improvement over a carefully-designed mathematical model, and even reaches 34% enhancement in severe distortion scenarios.