**2. Proposed ground-based ML-AI framework for unknown operating environment**

The newly proposed ground-based ML-AI framework for the unknown operating environment was developed by the CSUF team and discussed in [1]. **Figure 1**

## **Figure 1.**

*Proposed ML-AI framework for unknown operating environment.*

illustrates a simplified version of the framework presented in [1]. The ground-based ML-AI framework consists of the following key components:


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

## **3. E2E-SSM model using FH MODEM**

Section 3 provides an overview of the MATLAB models for FH-TX terminal, satellite TXDER model, and FH-RX terminal of the E2E-SSM provided by the IFT team. These MATLAB models serve as the backbone of the proposed ML-AI framework providing an accurate E2E-SSM emulator for generating and collecting SATCOM data at the FH-RX terminal under various operating conditions of interest. The data collection part of this project is thought of as the data science aspect of this problem. For example, what type of data needs to be collected, what actual operating conditions we need to set the E2ESSM emulator, how to arrange the data for the decision-making process, etc.

### **3.1 IFT E2E-SSM using FH MODEM and satellite TXDER**

This section presents an overview of the FH modulator–demodulator (MODEM) employed by the emulator and discusses the optimization of processing time allowing real-time simulation. In addition, the training data used for the demonstration of the ground-based ML-AI PD will also be addressed.

**Figure 2(a)–(c)** provides high-level block diagrams of the IFT MATLAB models for the ground FH-TX terminal, satellite TXDER model, and ground FH-RX terminal, respectively. The details of the QPSK modulator and demodulator can be found in [1]. The QPSK modulator can (i) generate a slow frequency hopping or high-frequency hopping rate by controlling the chip rate and (ii) produce a hopped signal with and without SRRC pulse shaping filter. In addition, the MATLAB ground FH-TX terminal

#### **Figure 2.**

*Block diagrams of IFT MATLAB models: (a) ground FH-TX terminal, (b) satellite transponder, and (c) ground FH-RX terminal.*

also incorporates an HPA model that can accurately generate AM-AM/AM-PM distortions. The MATLAB satellite TXDER model can accurately generate signal distortions caused by imperfect satellite TXDER components. This includes (i) RF-to-IF (intermediate frequency) down-converter, (ii) analog-to-digital converter (ADC), (iii) digital channelizer, (iv) digital-to-analog converter (DAC), (v) IF-to-RF upconverter, and (vi) onboard HPA operation causing AM-AM/AM-PM distortions. The satellite TXDER model is capable of setting practical satellite operating temperatures, IPBO settings, and the amount of distortions caused by imperfect satellite TXDER components. Note that the IPBO setting controls the amount of HPA AM-AM/AM-PM distortions. The ground frequency FH-RX terminal is capable of de-hopping the signal and demodulating the QPSK signal to recover the transmitted data bits and calculate the bit error rate (BER).

In **Figure 2**, let *SNRNU* be the U/L (i.e., from ground FH-TX terminal to satellite TXDER) signal-to-noise power ratio (SNR), *SNRDU* be the D/L (i.e., satellite TXDER to FH-RX terminal) SNR, the intermodulation noise (a.k.a. IM noise) caused by the HPA nonlinearity at the TXDER is characterized by *C=IM* (a.k.a. carrier-to-IM power ratio), and the overall SNR, *SNR***0**, received at the FH-RX terminal, can be shown to have the following form:

$$\frac{1}{\text{SNR}\_O} = \frac{1}{\left(\frac{1}{\text{SNR}\_{\text{MU}}}\right) + \left(\frac{1}{\text{SNR}\_{\text{MD}}}\right) + \left(\frac{1}{\text{C/IM}}\right)}\tag{1}$$

Let us assume that there in a U/L RFI with unknown signal-to-interference power, *SIRU*, and a D/L RFI with unknown *SIRD*, and the overall received *SNR***<sup>0</sup>** at the FH-RX terminal becomes:

$$\frac{1}{\text{SNR}\_{O}} = \frac{1}{\left(\frac{1}{\text{SNR}\_{\text{ND}}}\right) + \left(\frac{1}{\text{SNR}\_{\text{ND}}}\right) + \left(\frac{1}{C/\text{IM}}\right) + \left(\frac{1}{\text{SIR}\_{U}}\right) + \left(\frac{1}{\text{SIR}\_{D}}\right)}\tag{2}$$

Using ML-AI, the FH-RX (see **Figure 2(c)**) observes the overall received *SNR***<sup>0</sup>** and predicts the amount of AM-AM and AM-PM distortions caused by the HPA located in the FH-TX terminal (see **Figure 2(a)**) and HPA located in the satellite TXDER (see **Figure 2(b)**). The ground-based ML-AI pre-distorter uses the predicted distortions and pre-distorts the transmitted signal to compensate for the combined AM-AM and AM-PM distortions. As shown in Eq. (2), the distortions depend on the IM at the TXDER. The IM level depends on the HPA operating point, TXDER HPA characteristics, and operating TXDER temperature. The HPA operating point is characterized by the IPBO. In addition, the unknown U/L RFI can change the HPA operating point (i.e., IPBO) causing unknown AM-AM/AM-PM distortions. This chapter investigates the performance of the proposed ground-based ML-AI predistorter framework shown in **Figure 1** in the presence of unknown IPBO, HPA AM-AM/AM-PM characteristics, and TXDER operating temperature.

#### **3.2 Reducing processing time of existing IFT E2E-SSM**

As pointed out in Section 3.1, the signal distortion models caused by imperfect satellite TXDER components with the satellite TXDER include amplitude ripple caused by input/output RF filters, phase noise caused by RF up/down-converters, and quantization noise caused by ADC-DAC, AM-AM/AM-PM distortion effects. The

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

current IFT E2E-SSM model requires excessive processing time, rendering it an inability to support real-time simulation or generate large training data for various operating temperatures and HPA IPBO's. The CSUF graduate students worked on the optimization of the filtering and HPA functions of the IFT E2E-SSM model in MATLAB to (i) reduce processing time which allowed for real-time simulation and (ii) provide for varying HPA operating temperature and IPBO.

#### **3.3 Training data for demonstrating proposed ML-AI framework**

The CSUF graduate students fine-tuned the E2E-SSM emulator to allow for realtime simulation using the satellite system parameters as shown in **Figure 3(a)**. Using the selected setup, the team generated E2E BER as captured in **Figure 3(b)**. The E2E BER simulation results represent the observed BER performance of a practical FH-QPSK signal under unknown HPA's operating conditions. The unknown HPA's operating conditions considered in the simulation are characterized by the HPA operating temperatures and IPBO as parameters. As specified in **Figure 3(a)**, the HPA operating temperatures and IPBO used in the simulation shown in **Figure 3(b)** are (i) 25°C, 27° C, and 30°C and (ii) IPBO = 0, 5, 7, 10, 13, and 15 dB, respectively.
