## 4 Experimental Results
The dashed line indicates the first-generation GFEC threshold (BER = \(1\times 10^{-4}\)). becomes narrower, increasing frequency resolution and enabling more precise spectral representation. This allows the MAE model to utilize finer frequency details, potentially supporting a higher masking ratio. Additionally, smaller patch sizes may be more effective since the higher resolution ensures sufficient detail for reconstruction. Conversely, when the IFFT size is small, the subcarrier spacing widens, reducing frequency resolution but making the signal more resistant to frequency-selective fading. In this case, a high masking ratio might degrade reconstruction quality due to the limited frequency resolution, and larger patch sizes may be necessary to retain sufficient information for accurate reconstruction. Similarly, when the CP length is long, more redundancy is introduced in the signal, allowing a higher masking ratio as the MAE can leverage this redundancy, while smaller patch sizes can still preserve enough information for accurate reconstruction. However, when the CP length is short, less redundancy is present, meaning that a high masking ratio might lead to excessive information loss. To compensate, a lower masking ratio is needed, and larger patches may be necessary to capture enough information within each patch. Therefore, future studies need to investigate various IFFT sizes and CP lengths to understand their respective roles in optimizing MAE-based DMT signal reconstruction. Based on the experimental results presented in Section 4, we can summarize the increase in transmission capacity of the Li-Fi system achieved by the proposed method, as follows. * Summary of the Experimental Transmission System
* Physical bandwidth used: 10 MHz
* Modulation formats: QPSK and 16-QAM
* Number of subcarriers per DMT symbol: 512
* Cyclic Prefix (CP): Length of 16
* Total samples per transmission: 2272 (including four DMT symbols and zero-padding)
* Masking ratio: Up to 85%
* Sampling rate: 40 MS/s (Arbitrary Waveform Generator)
* Data Rate Estimation
* QPSK Transmission Rate
* Baseline rate (without masking):
* Since QPSK carries 2 bits per symbol:
* Baseline = 10 MHz \(\times\) 2 bits = 20 Mbps
* Enhanced rate with 85% masking (i.e., a 1.85\(\times\) increase in channel capacity):
* Effective rate = 20 Mbps \(\times\) (1 + 0.85) = 37 Mbps
* \(16\)-QAM Transmission Rate
* Baseline rate:
* 16-QAM carries 4 bits per symbol:
* Baseline = 10 MHz \(\times\) 4 bits = 40 Mbps
* Enhanced rate with 85% masking:
* Effective rate = 40 Mbps \(\times\) (1 + 0.85) = 74 Mbps  
Therefore, our experimental setup demonstrates that the proposed method can achieve up to 37 Mbps with QPSK and 74 Mbps with 16-QAM, under a 10 MHz bandwidth scenario. Also, the optimization of parameters such as the masking ratio, patch numbers, and batch size is indeed a crucial component of the proposed MAE-based sparse coded mask modeling technique. These parameters can be summarized as follows:  
Masking Ratio* The masking ratio determines the degree of compression applied to the DMT signal. As demonstrated in our experimental results (Figures 3 and 4 of the manuscript), we observed that increasing the masking ratio up to 75% resulted in a decrease in error vector magnitude (EVM) for both QPSK and 16-QAM symbols. This somewhat counterintuitive result is attributed to the self-supervised learning capability of the MAE, which encourages global pattern learning and reduces overfitting when less information is directly provided. However, if the masking ratio exceeds 85%, reconstruction performance starts to degrade due to the lack of sufficient unmasked patches, as explained on page 6 of the manuscript. * Patch Numbers
* As shown in Figure 5, increasing the number of DMT patches from 8 to 32 led to a decrease in EVM, indicating improved reconstruction. Smaller patches preserve finer time-domain details, making it easier for the MAE model to learn local and global structures.