## 2 Literature Review  
Besides the design of protection systems for Satcom communication, it is also necessary to study the interfered system under jamming situation and also the effect of the mitigation in performance enhancement or QoS degradation [17]. Study [10] depictures inside the body of a typical receiver with details and shows the reaction of the essential digital receiver blocks under RFI situation. The study proves that in an attack, not only the power of interference, but also its center frequency plays an important role in system corruption. It is shown that, a smart and low power jammer with a center close to the Doppler value can completely stop the receiver. Moreover, it is shown that even if a low power CWI does not stop the receiver, it still decreases the QoS and affects the SNR and bit error rate (BER) significantly. Thus, it is crucial to design a detector with the capability of very low power detection for this type of receiver. Because a narrowband modulated signal carries a lot of information in a dense band, the mitigation step must also be fulfilled very carefully. A notch filter is proposed as a computationally efficient solution for protecting the system against narrowband attacks. Several criteria like SNR measurement before and after filtering are proposed for optimum adjustment of the notch filter bandwidth. However, center frequency estimation and bandwidth adjustment based on the RFI power is postponed to future works. Prior to this study, several protection methods for Satcom communication have been researched. Artificial intelligence techniques is an emerging topic for RFI detection and characterization [18]. Research [1] extracts different features from the input signal and utilizes them as the input for machine learning (ML) and multi-layer perceptron (MLP) for RFI recognition and automatic classification. Based on the results, MLP is able to classify RFI in three categories CWI, MCWI, and Chirp with robust and high precision. Artificial intelligence is able to classify the mixed interference in one of the narrowband, multi-narrowband, or wideband. Knowledge about the input type can be considered as the first step in RFI characterization. It helps the detector to utilize the proper method and extract the necessary information about RFI easier and with more accuracy. In a general view, the detector tries to estimate the center frequency and power of interference with maximum accuracy in different ways. Detection methods can be divided in three categories: temporal domain, frequency domain monitoring, and spatial domain detection techniques [12]. Time domain algorithms like least mean square (LMS) [19] and recursive least square (RLS) [20] recursively converge to the center frequency of the narrowband interference. The LMS method is used vastly for narrow band interference detection in GNSS systems. The main advantage of the LMS algorithm is its simplicity. As discussed earlier, the structure of the modulated signal power spectrum is completely different from the GNSS signal and that makes the detection of CWI more complicated. Time domain detectors are more sensitive to the noise and they are not suitable for weak interference mixed with modulated signals because of their special power spectrum shape. Compared to the time domain algorithms, second class of detectors transfers a window of input samples into frequency [21] or wavelet domain [22] and tries to analyze abnormalitiesin samples and characterize them as interferences. In [23], wavelet-packet-transform is utilized to detect and mitigate the powerful CWI and Chirp jamming. The fast Fourier transform (FFT), which is an efficient computational version of the DFT, is commonly used to present the signal in the frequency domain [24]. The squared amplitude of FFT samples represents the first estimation of power spectrum distribution in the frequency domain. It is common to compare the power spectrum (or Wavelet) samples with a dynamic threshold and consider larger samples as the interference [17; 23; 25].