### Other Existing Works and Key Contributions of Proposed Technique  
First, as shown in Table 1, we have organized and presented the differences between the previously proposed techniques mentioned in the references and our proposed method to clearly highlight the distinctions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Other Techniques** & \multicolumn{2}{c}{**Key Aspects of Other Techniques**} & \multicolumn{2}{c}{**Differences from Our Proposed Technique**} \\ \hline \hline  & & & Our Approach: We apply an MAE-based method that physically ’masks out’ up to 75-85\% of the DMT waveform before transmission and then reconstruct it at the receiver. \\  & & & By contrast, [9] transmits the entire waveform and focuses on reducing channel distortion, we shorten the time-domain signal itself. \\  & & & This effectively increases channel capacity by transmitting fewer samples within the same bandwidth—an entirely different perspective. \\ \hline  & & & Our Approach: Targets short-range Li-Fi (visible light) rather than fiber. \\  & & & Uses a self-supervised MAE framework that can reconstruct heavily masked (deleted) DMT patches. \\  & & & Unlike fiber modeling, we focus on actual time-domain compression (masking) of the waveform to enhance throughput—this is not merely channel compensation but a fundamental signal-length reduction that increases capacity. \\ \hline  & & & Our Approach: Not merely mapping inputs to outputs in an end-to-end manner; we delete (mask) a portion of the time-domain signal and then reconstruct it. \\  & & & This results in a reduced physical signal length, thereby boosting capacity, rather than just improving channel equalization. \\  & & & We physically transmit only part of the waveform—a fundamentally different approach from standard end-to-end solutions that use the entire waveform. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of existing techniques and our proposed one. The key contribution of our proposed technique can be summarized as follows. * True Time-Domain Compression Through Random Masking
* Unlike many deep learning approaches that transmit the complete waveform and focus on post-transmission equalization, our method removes (masks) up to 75-85% of the DMT signal pre-transmission. * A masked autoencoder then reconstructs these missing segments at the receiver, which increases the effective channel capacity up to 1.85\(\times\) over a 10 MHz bandwidth. * Viewing Multicarrier DMT Waveforms as Time-Series 'Patches'
* We divide the QPSK/16-QAM DMT waveforms into 1-D patches and apply positional encoding to identify each patch's location. * This design captures long-range dependencies via self-attention, facilitating robust patch-wise masking and inpainting--even under high masking ratios. * Extensive Experimental Validation
* We systematically measure EVM versus masking ratio, number of patches, and batch size in an actual Li-Fi testbed (1 m range, 300-440 lux). * Notably, our results show that higher masking ratios can sometimes reduce errors because the MAE is forced to learn global waveform structure instead of overfitting local features. * Our approach remains practically feasible above ~240 lux, typical of indoor illumination levels. In sum, our work differentiates itself by physically removing large portions of the transmitted signal and then reconstructing them--a methodology not found in conventional