Res-GAN for Behavioral Modeling and Pre-Distortion of Power Amplifiers in OFDM-Based Satellite Communication System

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.

I. Introduction

Satellite communication stands as a fundamental pillar in the development of the future space-air-ground integrated network [1], [2], [3], [4]. However, the long distance between satellites and ground stations necessitates sufficient power in downlink transmissions to overcome path loss. Considering the inherent limitations of satellite power, PAs become indispensable in satellite communications to enhance the power of received signals at ground stations [5].