## 5 Simulation Results and Discussion  
This section discusses the performance of the proposed adaptation algorithm for CWI and MCWI suppression scenarios. The proposed algorithm uses a second IIR NF based on a direct-form structure to control the update filter coefficient and notch depth in the TD. The \(SNR_{od}dB\) and the BER are used to evaluate the system performance for the proposed algorithm in terms of varying the SOI power (\(E_{b}/N_{o}\)) and JSR. In this work, the number of CWIs is considered to be 2 for the \(i\)th jamming signal, with center frequencies (\(f_{1}=0.03\) and \(f_{2}=0.08\)) and with an unknown amplitude (\(A_{i}\)). The simulation was run for 1e7 data bits to mitigate CWI and MCWI. \(\beta\) was set to 0.98 in order to provide more acceptable outcomes in various scenarios. There was a trade-off between some properties of ANF \(H_{d1}(z)\) and ANF \(H_{d2}(z)\), and \(\beta\) was chosen. A large \(\beta\) value gave more accurate frequency estimates and JSR, but slower convergence and tracking [54] in \(H_{d1}(z)\). However, for \(H_{d2}(z)\), a large \(\beta\) gave a larger NB noise at the output but a smaller broadband noise [40]. The BER was quantified in terms of JSR changes. All simulation results were obtained using MATLAB\({}^{(r)}\) software. In Figure 9, the notch depth is a function of the estimated JSR. As the JSR increases, the magnitude of the notch depth and bandwidth decreases. The CWI's power determines the depth of the notch magnitude response--the higher the CWI's power, the deeper the notch magnitude response. The BER performance as \(E_{b}/N_{o}\) changes is depicted in the figures below. In the simulations, various values of \(E_{b}/N_{o}\) and a constant value of JSR were used. Figures 10 and 11 illustrate the performance in the presence of CW and MCW interference, with a constant JSR, and controlling the depth of the NF with respect to estimating the power of interference. The results demonstrate that the proposed IIR NF achieves a better BER with CWI--especially when the JSR is small or large, as shown in Figures 12 and 13, respectively. Thus, the ANF can reduce interference and obtain a better BER. Figures 14 and 15 show the BER performance with variations of the JSR. Simulations were carried out with various JSR values and a constant value of \(E_{b}/N_{o}\), while adjusting the depth of the NF. The simulation results were compared between the proposed IIR NF (filtering) and no filtering cases; they show that the proposed algorithm outperforms the no filtering in the case of filtering. Figures 16 and 17 illustrate the proposed ANF algorithm in comparison with full suppression (\(k_{1}=1\)) while using a Single Adaptive IIR NF (S-ANF) and Multiple Adaptive NFs (MANF). The notch depth was efficiently controlled by the proposed algorithm, and achieved better results than full suppression for both low and high JSR. Figure 9: Notch depth vs. JSR. Figure 11: BER vs. \(\frac{E_{b}}{N_{e}}\) in the presence of MCWI. Figure 12: BER vs. \(\frac{E_{b}}{N_{e}}\) (in the presence of CWI/MCWI, with JSR = \(-12\) dB). Figure 10: BER vs. \(\frac{E_{b}}{N_{e}}\) in the presence of CWI. Figure 14: BER vs. JSR, using an S-ANF. Figure 13: BER vs. \(\frac{E_{a}}{N_{o}}\) (in the presence of CWI/MCWI with JSR = 10 dB). Figure 15: BER vs. JSR, using MANF. Figures 18 and 19 show the performance of the SNR vs. the JSR. The proposed IIR NF algorithm efficiently adjusts the notch depth in each step, making it deeper for a high JSR. In the latter case, the results show that full suppression outperforms the case without an NF (no filtering), indicating that full suppression is better for a high JSR, while no filtering is better for a low JSR. This further illustrates that the proposed IIR NF approaches full suppression when the JSR increases (irrespective of the stage), because the notch depth deepens in this case; thus, the notch depth becomes smaller for a lower JSR value and deeper for a higher JSR value.