Res-GAN for Behavioral Modeling and Pre-Distortion of Power Amplifiers in OFDM-Based Satellite Communication System
Menghan Wang, Haoge Jia, Sheng Wu, Xianghui Hu, Chunxiao Jiang, Wei Zhang
Abstract
In satellite communications, the power amplifier (PA) plays a vital role in enhancing the transmission performance of signals. However, the non-linearity of PAs often distorts the transmitted signals, degrading communication performance. Digital pre-distortion (DPD) is one of the most effective linearization technologies to address the abovementioned issue and improve PA performance. Neural networks (NNs) outperform traditional models in PA behavioral modeling and linearization due to their strong nonlinear fitting capability, but the lack of learning data distributions limits the performance. Hence, this article proposes a novel behavioral and linearization model for PA, utilizing the residual generative adversarial network (Res-GAN). The proposed model can extract deep features of the PA with enhanced accuracy, thereby improving PA linearization performance. Simulation results show that the proposed Res-GAN behavioral model achieves the normalized mean squared error (NMSE) of −55.737 dB. Additionally, the Res-GAN DPD outperforms traditional DPD models by a reduction in the NMSE of about 2 dB and the adjacent channel power ratio (ACPR) of about 2-4 dBc.