# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 5 Results and Discussions
To avoid the unwanted scaling happening with data present over subcarriers that are not actually affected with noise, we applied the nonlinear scaling of GAE (11) only to subcarriers that are affected with impulse noise. The schematic of the absolute value of complex data input to GAE enhanced PPA and the obtained output is shown in Figure 10 (left) and Figure 10 (right) respectively. We utilized the basic logic applied in the pulse blanking technique to identify the subcarriers affected by DME noise. Thus when the nonlinear scaling is applied only to selected subcarriers as in (11), a reduction in BER is achieved in comparison to pulse blanking. The results are shown in Figure 11. Thus, GAE enhanced PPA could achieve a better result in terms of reduction in BER with variation in SNR, compared to both GAE and pulse blanking. GAE enhanced PPL performed nonlinear scaling only to the subcarriers, which are affected with impulse noise in such a way that the amplitude of the resulting signal after nonlinear scaling is at least equal to the threshold value used for detecting the pulse peak (12). Comparing Figure 10 (right) and Figure 12 (right), we could clearly observe that the nonlinear output displays in Figure 12 (right) is similar to Figure 10 (right) with the maximum output value limited to the threshold value used for identifying the affected subcarriers. As discussed in the system model number, this limiting operation is possible in two ways as in (12) or (14). Performance comparison of these proposed methods with GAEPPA and conventional pulse blanking are shown in Figure 13.  
The DME mitigation of joint GAE enhanced PPA over a received data packet is better than GAE enhanced PPL or GAE enhanced PPA, which can be observed by comparing Figure 14 (right) with Figures 10 (right) and 12 (right). The performance of joint GAE enhanced PPL is slightly better than joint GAE enhanced PPA, which can be verified from Figure 15.  
Figure 9: **(left)** OFDM data at the receiver along with DME interference and AWGN noise. **(right)** Output of genie-aided estimator in the time domain. The K-GMM modeling of the received signal is unavoidable for the accurate functioning of OBE. The proposed nonlinear estimators discussed in this paper utilize the statistical variables obtained from the 2-GMM modeling of complex valued received signal \(y_{t}[n]\) resulting in two components with variances \(\sigma_{0}^{2}\) and \(\sigma_{1}^{2}\) and probability of occurrences \(p_{0}\) and \(p_{1}\). Figure 16a shows the absolute value of the OFDM data frame for all the '\(t\)' values. Figure 16b,c shows the variance \(\sigma_{0}^{2}\) and the corresponding probability \(p_{0}\). Similarly,  
Figure 11: Performance comparison of the proposed GAE enhanced PPA vs. the conventional pulse blanking and GAE. Figure 12: (**left**) OFDM data at the receiver along with DME interference and AWGN noise. (**right**) Output of GAEPPL in the time domain. Figure 10: (**left**) OFDM data at the receiver along with DME interference and AWGN noise. (**right**) Output of GAEPPA in the time domain. variance \(\sigma_{1}^{2}\) and probability \(p_{1}\) are displayed in Figure 16d and Figure 16e respectively. The numerical calculation of the scaling factor of OBE (\(\beta_{o}(y)\)), which itself is a function of the received signal \(\mathbf{y}\) is calculated from the statistical information (\(\sigma_{0}^{2},\sigma_{1}^{2}\)\(p_{0}\) and \(p_{1}\)) obtained from 2-GMM modeling. Each term of this scaling factor is well explained in the system model. The value of \(\beta_{o}(y)\) mathematically calculated from the statistical parameters obtained from the 2-GMM modeling is utilized in OBE in finding the Bayesian estimated value as in (23). Figure 17 (left) characterizes the CP removed OFDM data symbols in the time domain at the input of OBE, and Figure 17 (right) shows the corresponding optimum Bayesian estimated output.