## 4 Experimental Results
The two insets in Figure 5 show the constellations of the QPSK symbols and the 16-QAM symbols measured when the number of DMT patches was set to 8. As shown in Figure 5, we confirmed that the EVM of both signals decreases as the number of DMT patches increases from 8 to 32. The reduction in EVM observed as the  
Figure 5: Measured EVM variation against the total number of DMT patches. Filled squares: EVM of QPSK symbols, Open squares: EVM of 16-QAM symbols. number of DMT patches increases from 8 to 32 can be attributed to the following technical factors: As the total number of divided DMT patches decreases, the length of each patch increases. Consequently, the temporal range of DMT information contained within each patch expands. Thus, random masking of DMT patches at a fixed mask ratio results in the removal of relatively longer patches, which arise due to a smaller total number of patches. Consequently, this increases the likelihood of failing to capture and learn the detailed time-series patterns within critical DMT patches. Ultimately, the proposed learning model leads to an increased error rate in the reconstruction of the overall DMT signal. Conversely, as the total number of segmented patches increases and the length per patch becomes shorter, the time-series pattern information contained in each DMT patch becomes more fragmented. When utilizing these more fragmented DMT patches, the recovery of masked patches becomes significantly easier by leveraging the surrounding unmasked patches, even at the same masking ratio. In other words, smaller DMT patches enable the proposed learning model to learn detailed time-series DMT patterns and changes more accurately in order to recover the DMT signal waveform, thus reducing the error rate. As shown in Figure 5, even when the total number of DMT patches was increased to 32, the recovery error rate decreased. This was because the detailed pattern variations in the DMT signal are more accurately learned, as discussed above, leading to a gradual reduction in the EVM for both QPSK and 16-QAM symbols. However, when the total number of divided DMT patches reached 64, an increase in EVM was observed for both symbols. This increase in EVM can be attributed to the following factors: the waveform of a DMT signal encodes critical information regarding its continuity and temporal pattern variations. When the length of each DMT patch becomes excessively short, maintaining this continuity and capturing these pattern variations may become challenging. Furthermore, as the number of divided patches increases excessively, the complexity of the proposed learning model also rises, adversely impacting its ability to achieve accurate learning. For these technical reasons, it could be concluded that the EVM for both symbols increased when the total number of DMT patches was raised to 64. Based on the experimental results presented in Figure 5, it can be concluded that optimizing the number of divided patches is crucial when transmitting data using waveforms other than the DMT signal. Figure 6 shows the EVM variation in QPSK and 16-QAM symbols with respect to the change in batch size. At this time, the masking ratio was 75% and the number of divided DMT patches was 16. Since batch refers to the number of samples used each time the proposed learning model is updated, it can affect the error rate that occurs in the process of recovering the DMT signal. Therefore, we measured how the transmission performance of the two symbols (QPSK and 16-QAM) changes according to the change in batch size. As shown in Figure 6, as the batch size increases from 4 to 64, the EVM of the QPSK symbols increases from 16.5% to 21.1%, and for the 16-QAM symbols, the EVM increases from 2.5% to 5.4%.