**1. Introduction**

The topic on HPA linearization and the use of machine learning and artificial intelligence (ML-AI) for the high-power amplifier (HPA) linearizer has been investigated in the recent past [1–12]. These works were focused on the linearizer that are usually placed before the HPA and applicable to either a satellite or a ground system with the HPA operating at saturation. This chapter addresses the HPA linearization of an end-to-end satellite system with an uplink (U/L) signal to a satellite transponder

(TXDER) and a downlink signal (D/L) from the TXDER. The proposed ground-based ML-AI HPA pre-distorter concept is intended to place on the ground tracking station and before the ground transmitter's HPA. The novelty of our proposed ground-based ML-AI is to linearize the combined amplitude and phase distortions caused by both the ground and satellite TXDER HPAs.

In practice, a typical SATCOM system includes a U/L and a D/L signals. The U/L signal transmits from a transmit (TX) terminal to a satellite TXDER. The D/L signal transmits from the satellite TXDER to a received (RX) terminal. The TX and RX terminals can be on the ground or an airborne or a navy ship. For the sake of discussion, this chapter assumes it is a fixed ground terminal. For this scenario, the U/ L signal is corrupted by the U/L propagation environment including weather, propagation path loss, and U/L radio frequency interferences (RFI). The signal passing through the satellite TXDER is corrupted by transponder processing noise and distortions caused HPA nonlinearity. The D/L signal is also corrupted by the D/L propagation environment including weather, propagation path loss, and D/L RFI. The combined distortion can cause serious Bit Error rate (BER) performance degradation to the received signal on the ground. The use of ML-AI technology to combine data science and decision science (a.k.a. data and decision sciences) can address these challenges. ML-AI can be used to observe the D/L signal amplitude and phase distortions behavior from the ground. It can also be used to predict the amount of distortions for signal compensation before the uplink transmission. In this context, observing the received signal and collecting the received data for predicting the signal distortion behavior is an application of data science. And deciding how much distortion for uplink signal compensation is an application of decision science. Therefore, this is a combined data and decision science technologies.

Through the Industrial Project for Graduate Program in Applied Mathematics (IPGPAM), a collaboration project between CSUF and Intelligent Fusion Technology (IFT) was initiated in 2019 to address this combined data and decision science technologies. During the 2019**–**2020 academic year, a CSUF team consisting of five graduate students and two faculty members at CSUF was formed to collaborate with the IFT team through this joint industry and university project. This project focused on the advanced mathematical modeling and simulation aspect of the ground-based ML-AI framework for future SATCOM applications. The IFT team provided the end-toend SATCOM System Model (E2E-SSM) emulator as a platform for demonstrating the newly proposed ground-based ML-AI framework. The CSUF team was responsible for the development of a ground-based HPA pre-distorter (PD) to compensate for the amplitude-to-amplitude and amplitude-to-phase modulation (AM-AM/AM-PM) distortions caused by the HPA nonlinearity and imperfect satellite onboard processing. The problem became more complicated due to unknown operating conditions associated with the satellite system operations, along with the U/L and D/L RFI environment. The RFI can be friendly and unfriendly. Friendly RFI sources are from neighboring satellites using the same RF or the RF near the victim's RF. Unfriendly sources are from adversary jammers. As discussed in [1], for unknown operating conditions, the existing ground-based ML-AI frameworks [2–4] using MORL-ANN require a very large amount of environmental data for all practical operating conditions for training purposes. Thus, with limited training data, trial and error learningbased processes such as MORL-ANN may not be practical in real satellite communication systems where actual operational conditions are varied and at times can be unpredictable. In addition, the use of MORL-ANN described in [2] can potentially run into "bottle-necks" without having proper training data under an unknown

## *Ground-Based HPA Pre-Distorter Using Machine Learning and Artificial Intelligent… DOI: http://dx.doi.org/10.5772/intechopen.110735*

Operational Environment (OE). Also, based on our past simulation results, we have observed that MORL-ANN usually performs very well under a controlled operational environment, where OE conditions, such as system temperatures and propagation loss, are fluctuating predictably and well within the norms. However, when the OE conditions change extensively and rapidly, such as unpredictable RFI power and Total Electron Content (TEC) changing abruptly causing RX signal scintillation, the MORL-ANN might not perform well. As discussed in [1], to address the unknown and uncertain operational environment, our proposed ML-AI framework monitors the received Signal of Interest (SOI) in real time and uses OEP to estimate the operating conditions for reducing uncertainties associated with the observed data before applying MORL-ANN. This proposed technique helps to reduce the amount of data required for training the pre-distorter and avoid the above-mentioned bottleneck.

The CSUF-IFT team has successfully implemented and demonstrated the newly proposed ground-based ML-AI framework addressing satellite TXDER's distortion under unknown HPA operating temperatures and operating Input Back-Off (IPBO). The implemented framework uses the IFT's E2E-SSM emulator as an end-to-end platform [1]. The E2E-SSM emulator includes a sophisticated frequency hopping (FH) satellite modem (modulator-demodulator) and a satellite TXDER model that was verified and validated with existing global broadcasting satellite transponder and global wideband satellite transponder models. This chapter provides a summary of the work performed by the joint CSUF-IFT team. The chapter is organized as follows:

