# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
## 5 Performance Evaluation  
In this section, we use the CP method to estimate and compensate for the CFO in MR-OFDM; this simulation is based on the characteristic parameters shown in Table 2.  
In this paper, we illuminate three speed cases, which can occur with this system. There are three popular cases concerning the velocity of UAV: constant, increased, and decreased. In the constant velocity case, we simulate the velocity of UAV of 75 m/s. In the decreased velocity scenario, we simulate the velocity of UAV decreasing from 75 m/s to 0 m/s with the acceleration of \(a=-0.5\) m/s\({}^{2}\). With the increased  
Figure 4: (**a**) Constellation of the MR-OFDM with the CFO effect; (**b**) Constellation of the MR-OFDM after CFO compensation over the Gaussian channel.  
Figure 5: MSE of the cyclic prefix technique with four options.  
velocity scenario, we simulate the velocity of UAV increasing from 0 m/s to 75 m/s with the acceleration of \(a=0.5\) m/s\({}^{2}\). After achieving 75 m/s, the UAV will move with the constant velocity of 75 m/s.  
Figures 6-8 represent the simulated CFO over time and the estimated CFO over time by using the CP algorism shown in Equation (15) with three cases. We can see that the CFO value and the Doppler shift are proportional. If the value of CFO changes during the transmission time, then we need a technique that can estimate the CFO in real time to compensate the CFO and then, increase the system performance. In our proposed scheme, the CP technique can exactly estimate the CFO value for every 120 us of the OFDM symbol frame. Therefore, the CP technique is suitable for the system when the shape of the estimated CFO value is the same as the shape of the practical CFO in real time. In addition, their values are the same and show the effectiveness of the proposed algorism in real time.  
Figures 9-11 show the BER performance when the receiver moves at the three velocities over the Gaussian channel and the Rician channel with \(k=10\). The blue line represents the BER performance of the MR-OFDM without the CFO effect. The green line represents the BER performance of the MR-OFDM at the three velocities: constant, decreased and increased, and the red line shows the BER performance of the MR-OFDM system after the proposed scheme. A comparison between the blue line and the green line in the three cases shows the influence of the Doppler effect on the system performance. In addition, the red line and the blue line almost overlap in all three cases indicating that the proposed scheme can almost estimate and compensate for the Doppler shift in the Rician channel and the Gaussian channel. Because the proposed scheme can estimate the CFO in each OFDM symbol, then, in the future, we could apply this scheme for movements with random velocity changes in the transceiver.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Antenna height of the transmitter & 6 m \\ Height of the unmanned aerial vehicle & 10 m \\ Distance between the routes with the transmitter (d) & 10 m \\ Velocity of the unmanned aerial vehicle (\(v\)) & 75 m/s \\ MR-OFDM Option mode 1 & \(N_{FFT}=128\), QPSK \\ Frequency & 2.4 GHz \\ Channel & AWGN, Rician \\ Bit rate & 773.809 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic parameters for simulation.  
Figure 6: CFO estimation over time with a constant velocity (v = 75 m/s).  
Figure 8: CFO estimation over time with the acceleration of \(a=0.5\) m/s\({}^{2}\).  
Figure 7: CFO estimation over time with the acceleration of \(a=-0.5\) m/s\({}^{2}\).  
Figure 9: Simulation BER per Eb/N0 of MR-OFDM over the Gaussian and Rician channels with a constant velocity (v = 75 m/s).