Ground-Based HPA Pre-Distorter Using Machine Learning and Artificial Intelligent for Satellite Communication Applications

*Tien M. Nguyen, Charles H. Lee, Sean Cantarini, Xuanyu Huang, Jennifer Gudgel, Chanel Lee, Cristal Gonzalez, Genshe Chen, Dan Shen, John D.T. Nguyen and Khanh D. Pham*

## **Abstract**

This chapter describes an innovative design and implementation approach of a ground-based pre-distorter framework using machine learning and artificial intelligence (ML-AI) technology for high power amplifier (HPA) pre-distortion. The ML-AI technology enabler proposed is a combined multi-objective reinforce learning-andadaptive neural network (MORL-ANN) and an operating environment predictor (OEP). The proposed framework addresses the signal distortions caused by a nonlinear HPA on the ground transmitter and a nonlinear HPA located at a satellite communication (SATCOM) transponder (TXDER). The TXDER's HPA is assumed to operate under unknown conditions. The objective is twofold, namely, to demonstrate (i) an advanced decision science technique using ML-AI for future SATCOM applications and (ii) the feasibility of the proposed ground-based ML-AI framework using an end-to-end SATCOM emulator. A new OEP concept using a deterministic and Bayesian approach to improve the MORL-ANN pre-distorter (PD) performance will also be presented.

**Keywords:** satellite communication, ground-based, high power amplifier predistorter, machine learning, artificial intelligent, operational environment predictor, signal distortion, high power amplifier
