## 3 Proposed Methodology
### Data Acquisition Set-Up  
As fully explained in [5, 6], the desired signal is a video stream, which is modulated and processed by GNU radio and transmitted using a Universal Software Radio Peripheral (USRP-N210) [22]. For modeling a real-time Satcom, the channel simulator (RTLogic T400) [23] is used. Further, the generated jamming signals are combined to SoI by a combiner. Finally, the combined signal is received by a MegaBee modem [5]. Notably, AWGN power can be manually adjusted in the range of \(-168\) to \(-125\) dBm, which is approximately equal to SNR 5 to 12 dB. Figure 2 shows the Real-time RFI data acquisition set-up.  
Table 1 presents a summary of the dataset specification generated in [5].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Value** \\ \hline Total number of observations & 4800 \\ \hline Length of each generated signal & 32,448 (8 ms) \\ \hline Sampling frequency & \(40\times 10^{6}\) Hz \\ \hline Modulation types & QPSK, 8 APSK, 16 APSK, and 32 APSK \\ \hline AWGN power & 140 dBm (\(SNR\cong 9\) dB) \\ \hline No. of each class of signals per modulation type & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Real-time dataset specification.  
Figure 1: Proposed Radio Frequency Interference (RFI) classification and Automatic Modulation Classification (AMC) based on transfer learning.  
Figure 2: Real-time RFI data acquisition set-up.  
This study analyzes the efficiency of the proposed classification technique in the presence of three jamming signals, such as continuous-wave interference (CWI), multi-CWI (MCWI), and chirp interference (CI) [5].  
1. Continuous Wave Interference (CWI): \[CW=exp(j2\pi f_{cw}t)\] (1) where \(f_{cw}\) and \(t\) represent the center frequency and the duration of interference respectively.
2. Multi Continuous Wave Interference (MCWI): In this study, we have considered two-tone CW, which is defined as: \[MCW=exp(j2\pi f_{c_{1}}t)+exp(j2\pi f_{c_{2}}t)\] (2) where \(f_{c_{1}}\) and \(f_{c_{2}}\) are the center frequencies of each wave.
3. Chirp Interference (CI): The CI has been generated according to [24] as follows: \[Chirp=exp(2\pi\frac{k}{2}t^{2}+2\pi f_{0}t)\] (3) where \(k=\frac{f_{1}-f_{0}}{T}\) so that the signal sweeps from \(f_{0}\) to \(f_{1}\) and \(T\) is the sweeping duration.  
Note: the center frequencies have been considered to be changed randomly.  
Dataset Generation  
This study has considered a visual representation of the received signals in the time-frequency domain using a scalogram as the input data. The scalogram is the squared magnitude of Continuous Wavelet Transform (CWT) and mathematically is defined as [25]:  
\[z_{x}(\alpha,\tau)=|\frac{1}{\sqrt{\alpha}}\int_{-\infty}^{+\infty}x(t)\Phi^{ *}(\frac{t-\tau}{\alpha})dt)|^{2} \tag{4}\]  
where \(z\) and \(\Phi^{*}\) denote scalogram and the complex conjugate of the mother wavelet function, \(\alpha\), and \(\tau\) are the oscillatory frequency and shifting position of the wavelet, respectively [25]. CWT is widely applied for nonstationary and transient signal analysis, mainly through its scalogram [26]. The main difference between wavelet transform and short-time Fourier transform (STFT) is that STFT has a fixed signal analysis window whereas the wavelet transform utilizes short windows at high frequencies and long windows at low frequencies [12].  
Therefore, the wavelet transform provides superior time and frequency resolution at high and low frequencies [12]. Hence, the wavelet-based analysis is considered an appropriate choice when the signal at hand has high-frequency components for a short duration and low-frequency components for a longer period, as is considered in this study [12]. As shown in Figure 3, the scalogram of SoI and its combination with CWI, CI, and MCWI jammers is computed using the Morse wavelet [27] to calculate the wavelet transform as well as the coherence analysis of the time series. For further processing, the computed scalogram is converted to an RGB image.