Presentation + Paper
29 July 2019 An effective satellite transponder linearization method using a physics-based predistorter
Author Affiliations +
Abstract
In satellite communication (SATCOM) system, a simple “bent-pipe” transponder is widely adopted to convert uplink carrier frequencies to downlink carrier frequencies for transmission of information without having on-board processing capability. The transponders are equipped with high power amplifiers (HPAs), which like other amplifier modules in communication systems, cause nonlinear distortions to transmitted signals, when HPAs are operated at or close to their saturation points to maximize power efficiency. These nonlinearities can be characterized as amplitude modulation-toamplitude modulation (AM-AM), and amplitude modulation-to-phase modulation (AM-PM) effects, which degrade the transmission performance of the system. Therefore, additional processing techniques such as predistortion (PD) has applied to maximize the transponder throughput along with the HPA power efficiency. In this paper, we first propose an accurate HPA modelling method, which leads to an outstanding agreement with the measured HPA AM-AM and AM-PM characteristics data. Then, a close-form PD is derived with respect to the power and phase compensation for the corresponding output signals of HPA. Finally, simulation results are provided to evaluate and verify the bit error rate (BER) improvement for the considered SATCOM system by applying our proposed PD technique.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lun Li, John Nguyen, Jingyang Lu, Dan Shen, Xin Tian, Genshe Chen, and Khanh Pham "An effective satellite transponder linearization method using a physics-based predistorter", Proc. SPIE 11017, Sensors and Systems for Space Applications XII, 110170G (29 July 2019); https://doi.org/10.1117/12.2520482
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KEYWORDS
Transponders

Satellites

Data modeling

Modulation

Performance modeling

Amplifiers

Machine learning

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