## 4 Performance Evaluation  
In this section, we evaluate the mARF technique for various maritime channels. For this purpose, we developed a VDE-TER simulator using MATLAB and implemented mARF and existing ARF techniques. The existing ARF technique was used to increase the MCS profile when communication was successful 10 times and to lower the MCS profile when communication failed 2 times continuously. The PHY layer parameters for the VDE-TER, which were employed in constructing the simulator, are listed in Table 3.  
First, we simulated the performance of the VDE-TER system while changing the physical layer parameters to verify the performance of the VDE-TER in an ideal environment which is the AWGN case without a deep null phenomenon. Figure 4 shows the system throughput of the VDE-TER. As shown in Figure 4, it can be verified that a higher throughput can be obtained using higher ID numbers in the channel environment, primarily because higher ID numbers have a higher channel bandwidth (BW), FEC rate, and modulation degree. It is confirmed that the optimal ID number depends on the channel environment represented by the SNR. That is, when using a BW fixed at 100 kHz, it can be confirmed that IDs 17, 18, and 19 must be changed based on the signal-to-noise ratio (SNR). Figure 4: VDE-TER throughput vs. SNR. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & & **Value** \\ \hline Carrier frequency (MHz) & \(f_{c}\) & 160 \\ TX power (EIRP) (W) & \(P_{EIRP}\) & 12.5 \\ Height of effective evaporation (m) & \(h_{e}\) & 12 \\ Channel BW (kHz) & \(B\) & 100 \\ Receiver antenna gain (dB) & \(G_{RX}\) & 0 \\ Receiver noise power spectral density (dBm/Hz) & \(N_{o}\) & \(-150\) \\ Receiver noise figure (dB) & \(F\) & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters for VDE-TER. For comparison under identical conditions, we evaluated mARF performance in an environment in which the bandwidth was fixed at 100 kHz. At this time, to apply the phenomenon of rapid signal attenuation in the maritime environment, the random deterioration phenomenon of SNR was applied in the performance evaluation based on Equations (1)-(3). First, a performance evaluation was performed in a two-ray channel model environment, and the throughputs of the ideal case, traditional ARF, and mARF were evaluated. As shown in Figure 5, it can be confirmed that mARF has a performance closer to the ideal case than ARF, even when a deep null phenomenon occurs that results in a rapid SNR change. Figure 6 shows the results that represent the throughput of the ideal case and the techniques compared with the SNR in a three-ray channel environment. As shown in Figure 6, mARF follows the channel capacity in response to the channel capacity of the ideal case, and it can be confirmed that it has a performance close to that of the ideal case, even if a deep null phenomenon occurs and rapid signal attenuation occurs. However, it can be confirmed that the ARF experiences a deterioration of approximately 1 dB or more compared with the ideal case owing to the deep null phenomenon. We also optimized the performance of mARF by dynamically adjusting the threshold instead of using a fixed MCS profile change threshold. To achieve this, simulations were conducted to assess the performance of mARF by incrementally increasing the \(\alpha\) value, which determines the MCS-changing counter, from 0 to 1 in intervals of 0.1. The simulation results, as depicted in Figure 7, confirmed that the optimal performance was achieved when \(\alpha\) had a value of 0.7. As a result, the default value for \(\alpha\) was set to 0.7. Figure 5: Comparison of throughputs in two-ray channel model. Finally, we simulated the average throughput of the fixed MCS case, traditional ARF techniques, and mARF, as shown in Figure 8. We confirmed that, on average, the mARF technique improves the performance by 14% compared to the traditional ARF technique because the mARF technique can respond appropriately to the maritime environment.