# ROAD Statistics-Based Noise Detection for DME Mitigation in LDACS
## 6 Conclusions  
In this paper, a new DME mitigation scheme named ROAD PB is proposed to mitigate the DME interference using pulse blanking. The ROAD PB detects DME interference with a method named ROAD statistics-based detection. The method detects the interference from the ROAD values of the received signal. The performance of the new detection method is compared with the conventional amplitude-based sensing method. The results guided to the following conclusions:  
1. ROAD statistics detection method outperformed conventional amplitude-based sensing in identifying the location of the DME interference (Figure 11). 2. The probability of detection of ROAD value-based sensing remains the same for both low and high SNR values, whereas the same found be varying for amplitude-based sensing. The accuracy of detection of the proposed method always excelled over conventional amplitude-based sensing (Figure 14). \begin{table}
\begin{tabular}{l l} \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 \(\upmu\)s \\ Subcarrier spacing \(f\) & 9.765625 KHz \\ Used subcarriers \(N_{u}\) & 50 \\ Useful symbol time \(N_{u}\) & 102.4 \(\upmu\)s \\ Cyclic prefix time \(T_{cp}\) & 17.6 \(\upmu\)s \\ Total OFDM symbol time \(T_{s}\) & 120 \(\upmu\)s \\ Guard time \(T_{g}\) & 4.8 \(\upmu\)s \\ Windowing time \(T_{w}\) & 12.2 \(\upmu\)s \\ Number of lower frequency guard subcarriers \(N_{g,left}\) & 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 [9]. Figure 27: Performance of ROADPB vs. pulse blanking, GAE and OBE enhanced pulse processors. 3. Though ROAD value-based sensing showed an increase in missed detection in comparison to amplitude-based sensing, the method could always achieve enlarged accuracy due to the substantial decrease in false detection. For an SNR value of 0 dB, regardless of the 2% increase in probability of missed detection compared to amplitude-based sensing, the proposed method achieved a 3% increase in probability of detection (accuracy) with the help of a 4% reduction in the probability of false detection (Figures 14-16). 4. The ROAD statistic-based interference sensing always shows a considerable reduction in false detection of the DME signal. 5. The optimum threshold value of detection is observed to be 8. The reduction in performance for a threshold value lower than 8 is due to the presence of more numbers of false detection than threshold 8. The reduction in performance for a threshold value higher than 8 is due to the increase in missed detection at higher threshold values. From the results obtained with the comparative study of the proposed method (ROADPB) with amplitude-based detection methods such as pulse blanking and pulse peak processors, we observed the following:  
1. ROAD PB exhibited improved performance than the pulse blanking method which uses the amplitude of the received signal for the detection of DME interference. 2. ROAD PB always outperformed the three types of GAE enhanced pulse peak processors in its optimum threshold value of operation. 3. ROAD PB showed better performance than OBE enhanced pulse peak attenuator and comparable performance with OBE enhanced pulse peak limiters. In comparison to pulse blanking, ROAD PB could achieve the SNR saving of 2.7 dB at a BER of \(10^{-1}\) by introducing some amount of complexity in the receiver. Moreover, at a BER of \(10^{-1}\), ROAD PB could accomplish SNR savings of 2.7, 1.1, 0.7, 0.25 and 0.2 dBs compared to GAEPPA, GAEPPL, OBEPPA and OBEPPL, respectively. The proposed ROAD PB is significant due to its improved performance at low SNR regions in comparison to pulse blanking. Moreover, ROAD value-based detection can be used to sense impulse noise in any type of OFDM-based communication systems where threshold-based detection can be used.