## 4 Experimental Results
In contrast, at higher masking ratios, the proposed learning model is required to reconstruct a larger portion of the missing waveform using limited information from the unmasked DMT patch data. This constraint enhances the ability of the learning model to deeply analyze the DMT patch data and perform recovery based on learned patterns. This process allows generalized pattern learning to occur, which helps prevent overfitting. In other words, the proposed learning model does not simply predict the masked portions of the DMT waveform data randomly using limited DMT data information. Instead, it learns the overall structure and patterns of the DMT data, enabling it to effectively predict the missing DMT data waveform based on this understanding. Ultimately, this recovery process leads to a reduction in EVM by minimizing errors. In addition, we can see that, as the masking ratio increases beyond 75%, the EVM of each signal increases. This increase in EVM can be attributed to the following reason: when the masking ratio becomes excessively high, the amount of DMT patch data available for the learning model is significantly reduced. As a result, the learning process becomes challenging, leading to a decrease in recovery performance. Therefore, it is necessary to optimize the masking ratio according to the characteristics of various signals in order to apply the proposed technique to signals other than the DMT signal. Another notable observation is that the EVM of the 16-QAM symbols was consistently lower than that of the QPSK symbols at the same masking ratio. This result implies that the DMT waveform encoded by a 16-QAM symbol, which has a more complex waveform shape than that encoded by a QPSK symbol, is better recovered and closer to the original one. In a typical digital signal processing environment, the DMT waveform encoded by a 16-QAM symbol exhibits greater complexity compared to that encoded by a QPSK symbol. Consequently, the intricate and delicate structure of the DMT waveform encoded by the 16-QAM symbols is more susceptible to loss during the compression process. Therefore,  
Figure 4: Variation in measured EVM as the masking ratio increases from 15% to 95%. Filled squares: EVM of QPSK symbols. Open squares: EVM of 16-QAM symbols. as the compression ratio increases, the 16-QAM symbols will have more errors during the recovery process. However, contrary to conventional digital signal processing environments, the proposed technique demonstrates superior recovery of the more complex DMT waveform encoded by the 16-QAM symbols, as shown in Figure 4. This can be attributed to the following reasons. Complex DMT waveforms inherently contain more information due to their detailed features, such as frequent abrupt changes in the waveform. In other words, complex DMT waveforms encompass a greater number of structural features of the DMT data. This allows the proposed learning model to leverage a richer set of structural DMT information during the learning process, enabling it to better comprehend the overall DMT signal waveform structure and achieve more accurate recovery, closer to the original DMT waveform. This allows the proposed learning model to reduce the error rate when recovering missing DMT waveforms based on unmasked DMT patches. Finally, it was observed that both symbols (QPSK and 16-QAM) can be successfully transmitted even at masking ratio up to at least 85%, based on the first generic forward error correction (GFEC) EVM threshold (QPSK: 26.3%, 16-QAM: 12.3%), as shown in Figure 4. These results indicate that, based on the 10 MHz physical bandwidth in the experimental setup of Figure 2, the transmission rate can be enhanced to up to 37 Mb/s (calculated as 20 Mb/s \(\times\) (1 + 0.85)) for QPSK symbols and up to 74 Mb/s (calculated as 40 Mb/s \(\times\) (1 + 0.85)) for 16-QAM symbols. Figure 5 shows how the EVM of QPSK and 16-QAM symbols can change as the total number of DMT patches increases from 8 to 64 in a power of 2. At this time, the masking ratio was 75%, and the batch size was 32.