# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 5 Results and Discussions
\begin{table}
\begin{tabular}{c c} \hline \hline
**OFDM Parameters** & **Values** \\ \hline Effective RF BW (FL or RL) & 498.05 KHz \\ FFT Size \(N_{FFT}\) & 64 \\ Sampling time \(T_{sa}\) & 1.6 μs \\ Subcarrier spacing \(f\) & 9.765625 KHz \\ Used subcarriers \(N_{u}\) & 50 \\ Useful symbol time \(N_{u}\) & 102.4 μs \\ Cyclic prefix time \(T_{cp}\) & 17.6 μs \\ Total OFDM symbol time \(T_{s}\) & 120 μs \\ Guard time \(T_{g}\) & 4.8 μs \\ Windowing time \(T_{w}\) & 12.2 μs \\ Number of lower frequency guard subcarriers \(N_{gleft}\) & 7 \\ Number of higher frequency guard subcarriers & 6 \\ Lower frequency guard subcarriers \(N_{g,right}\) & 625 KHz \\ Total FFT BW \(B_{g}\) & 625 KHz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: OFDM Parameters for LDACS1 [12]. (pulse blanking) improved the performance of the receiver. The threshold value used for the pulse blanking method is 0.3 [12]. Careful analysis of Figure 7 reveals the fact that the pulse blanking technique showed a significant improvement in the performance of the receiver at the high SNR powers and a slight decrease at low SNR values. K-GMM modeling of the received signal is inevitable for the proper functioning of any of the proposed GAE enhanced pulse processing techniques. In our proposed methods, we considered the simplest K-GMM method, which splits the total noise associated with each OFDM symbol into a statistical combination of two components. Thus, with the help of 2-GMM modeling of the received OFDM data (after CP removal), the total noise power in the channel for each OFDM symbol is separated into two mutually exclusive Gaussian variables. From (6), it can be easily recognized that with 2-GMM, the resulting components are thermal noise corresponding to \(k=0,i_{0}\approx G(i_{0},\sigma_{0}^{2})\) and impulse noise (DME noise) \(k=1,i_{1}\approx G(i_{1},\sigma_{1}^{2})\). The resulting variances \(\sigma_{0}^{2}\) and \(\sigma_{1}^{2}\), along with their probability of occurrences \(p_{0}\) and \(p_{1}\) are used to calculate \(\sigma_{k}^{2}\) using (8) and is displayed in Figure 8 (right). Comparing these values of \(\sigma_{k}^{2}\) with Figure 8 (left), which shows the absolute values of CP removed OFDM symbols of the LDACS FL Data/CC frame, it is clearly understood that \(\sigma_{k}^{2}\) could predict the OFDM symbols affected with impulse noise, though not perfectly. The value of \(\sigma_{k}^{2}\) obtained from the 2-GMM modeling of the signal \(y\) and the variance of transmitted signal \(\sigma_{x}^{2}\) for each OFDM symbol is utilized in calculating the nonlinear scaling factor \(\rho_{k}\) in (9). From Figure 9, it can be observed that though GAE could reduce  
Figure 8: **(left) Absolute values of received OFDM data frame. (right) Variance k obtained from the 2-GMM of data. **  
Figure 7: Performance of conventional pulse blanking technique vs. without DME interference and with DME interference. the amplitude of the impulsive noise, it is not sufficient to remove the DME noise. Hence, no reduction in BER is observed from the DME noise affected system. Though the GAE block works, it fails to eliminate DME noise appropriately when the complex received signal is modeled using the 2-GMM distribution. One of the possible reasons behind this could be the minimum value of K = 2, which is used to model the received complex data, which leads to inaccurate scaling of subcarriers. Another reason could be the scaling of data in subcarriers that are not affected by impulse noise, as each value of \(\sigma_{k}^{2}\) is the same for all the subcarriers in a single OFDM symbol. Hence, though the GAE claims to predict the active component at each time epoch, it is not always practical. It may be possible to overcome these two reasons either by increasing the K value in K-GMM or by applying nonlinear scaling only to the noise-affected subcarriers. The second possibility gives way to the development of GAE enhanced PPA discussed in the following section. The performance of the GAE can be improved with the knowledge of other side information [32].