## 4 Experimental Results  
It is necessary to first theoretically verify whether the learning model implemented using the proposed technique is properly trained for the DMT signal. Therefore, we calculated how the training loss converges according to the masking ratio to evaluate the performance of the learning model. Figure 3 shows the mean square error (MSE) as the training loss of the learning model according to the masking ratio of the DMT patches. Here, the masking ratio refers to the proportion of the waveform removed from the overall DMT signal, representing the compression ratio from the perspective of digital signal processing. The solid black line represents the MSE curve at a masking ratio of 25%, the solid gray line represents the MSE curve at a masking ratio of 55%, and the gray dashed line represents the MSE curve at a masking ratio of 75%. Since the difference in MSE according to the masking ratio change in epochs from 400 to 500 was not discernible to the naked eye, the result of enlarging the MSE curve in the corresponding epoch range is shown in the inset of Figure 3. Notably, as  
illustrated in the inset, the MSE at a masking ratio of 75% was observed to be consistently lower, within the epoch range of 400 to 500. These results suggest that the errors associated with recovering masked DMT patches would be decreased progressively as the masking ratio increased from 25% to 75%. To experimentally validate these simulation result, the error vector magnitude (EVM) was measured for two different symbols (QPSK and 16-quadrature amplitude modulation (QAM)) under varying masking ratios using the optical wireless Li-Fi transmission link implemented in Figure 2.  
Figure 4 shows the error vector magnitude (EVM) of the QPSK symbols and 16-QAM symbols repeatedly measured while changing the masking ratio. The filled squares represent the EVM of the QPSK symbols, while the open squares indicate the EVM of the 16-QAM symbols. Each inset shows the constellation diagram of QPSK and 16-QAM symbols when the masking ratio is 75%. At this time, the total number of DMT patch was 16, and the batch size was 32. Here, the masking ratio refers to the proportion of the waveform removed from the overall DMT signal, representing the compression ratio from the perspective of digital signal processing. As shown in Figure 4, the EVM of both symbol types, QPSK and 16-QAM, decreased progressively as the masking ratio increased from 15% to 75%. From the perspective of general digital signal processing, an increase in the masking ratio, i.e., compression ratio, leads to a higher error rate during the recovery process, which inevitably results in an increase in EVM. However, with the application of the proposed technique, it was observed that, as the masking ratio increases to 75%, the recovery process for each signal exhibited a reduction in error. Notably, the EVM decreased to 17% for QPSK symbols and 2.7% for 16-QAM symbols. These results can be explained by the following technical reasons. At low masking ratio, the proposed MAE-based learning model is exposed to a substantial portion of the original DMT signal waveform data encoded with each symbol type (QPSK and 16-QAM). As a result, the model may become overly reliant on, or overfitted to, the abundant original DMT data during the recovery process. Consequently, the proposed learning model primarily emphasizes local relationships, rather than capturing the overall structure of the entire DMT signal waveform. As a result, the model tends to reconstruct the missing waveform based on the surrounding information from the unmasked DMT patches. If these processes are repeated, the model fails to capture the overall characteristics of the  
Figure 3: MSE curves of the proposed learning model against masking ratio. randomly varying time-series data, leading to an increased error rate during the recovery of the DMT waveform data.