## 4 Experimental Results
The reason why EVM increases as batch size increases can be attributed to the following reasons: It is known that when the batch size is small, it helps the learning model to prevent overfitting and learn various patterns by adding noise to the gradient estimation at each update process, thereby increasing the recovery rate [19; 20; 21]. Similarly, in the case of the DMT signal, it is possible to effectively recover the masked part of the DMT patch by capturing the delicate changes in the DMT signal waveform with time-series characteristics. On the other hand, as the batch size increases, the diversity of DMT patch data decreases, which may cause the proposed learning model to become biased toward a specific pattern, resulting in overfitting. This will increase errors in the recovery process of the DMT signal waveform. However, when the batch size is small, the memory usage of the proposedlearning model increases, which may lead to longer training times. Therefore, it is necessary to optimize the batch size within the EVM threshold, considering the latency allowed by the network. As shown in Figure 7, the BER of both QPSK and 16-QAM symbols decreases consistently as the input SNR increases from 15 dB to 40 dB. The QPSK signal achieves a BER below the GFEC threshold at around 23 dB, while the 16-QAM signal reaches this point slightly earlier, around 22 dB. This performance trend is consistent with the EVM results presented earlier, where 16-QAM showed lower error magnitudes than QPSK. The superior BER performance of 16-QAM may be attributed to its higher symbol resolution, allowing the masked autoencoder to better infer complex symbol patterns during the reconstruction process. At higher SNRs (above 30 dB), the BER of 16-QAM drops to near-zero levels, demonstrating the effectiveness of the proposed reconstruction approach even for high-order modulation schemes. These findings validate the robustness of the proposed MAE-based sparse coded mask modeling technique not only in terms of constellation-level accuracy (via EVM) but also in terms of bit-level reliability, as verified through BER analysis. Figure 8 illustrates the variation in EVM for two modulation formats, QPSK and 16-QAM, under repeated measurements, as the intensity of light incident on the APD changes after 1 m of optical wireless transmission. The experimental conditions included a masking ratio of 75%, 16 DMT patches, and a batch size of 32. The received light intensity was measured within the range of 200 to 440 lux, reflecting the typical illuminance levels of indoor environments. This range was chosen considering the use of Li-Fi networks, which employ LEDs for indoor illumination. As illustrated in Figure 8, the EVM floor was observed for both modulation formats within the received light intensity range of 300 to 440 lux. This indicates that the received SNR does not exhibit further improvement with an increase in light intensity beyond 300 lux. Furthermore, based on the first generation GFEC  
Figure 6: Measured EVM variation against the batch size. Filled squares: EVM of QPSK symbols, Open squares: EVM of 16-QAM symbols. EVM threshold, it was determined that the received light intensity must be approximately 240 lux or higher to ensure successful transmission for both QPSK and 16-QAM symbols. For your information, we primarily focused on identifying the optimal masking ratio and patch number/size for different DMT subcarrier modulation formats. However, we acknowledge that other DMT system parameters, such as IFFT size and CP length, could also significantly affect these optimal values. When the IFFT size is large, the subcarrier spacing  
Figure 8: Measured EVM changes in QPSK and 16-QAM symbols against the received light intensity. Filled squares: EVM of QPSK symbols, Open squares: EVM of 16-QAM symbols. Figure 7: Measured BER versus input SNR for QPSK and 16-QAM symbols after 1 m optical wireless transmission using the proposed MAE-based sparse coded mask modeling technique. The masking ratio was fixed at 75%.