## 3 Experimental Setup
### Li-Fi Transmission Link Testbed Based on Sparse Coded Mask Model
The mean squared error (MSE) was used to measure the difference between the recovered DMT signal and the input DMT one. Learning was performed to optimize various hyperparameters such as patch size, batch size, and learning rate by minimizing the obtained MSE value. Following the demodulation process using a Discrete Fourier Transform (DFT), the QPSK symbols were obtained. In this paper, our primary focus was not on achieving the highest transmission capacity in the field of Li-Fi research, but rather on verifying whether the proposed technique functions conceptually in line with the theoretical framework. Therefore, we implemented an optical wireless Li-Fi transmission link with a relatively low data rate to experimentally validate the proposed technique. The specifications of the optical and RF components used to implement the experimental setup shown in Figure 2, as well as the values of the transmission link parameters, are summarized in Table 3.  
The primary objective of this research was to verify the feasibility and theoretical validity of the proposed technique in a controlled and measurable environment, rather than achieving maximum transmission distance or data rate. The key reasons for selecting a 1 m optical link are as follows. * Controlled Channel Characteristics: Short-range optical transmission minimizes the impact of unpredictable environmental variables such as multipath reflections, non-line-of-sight propagation, and ambient noise. This enables us to isolate and evaluate the effect of our proposed masking, compression, and reconstruction techniques on the DMT waveform. * Fine-grained Signal Capture: At shorter distances, signal distortion is minimized, allowing us to accurately measure subtle waveform differences introduced by the MAE process. These include time-domain waveform shape, constellation distortion (EVM), and patch recovery quality. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value/Specification** & **Component/Reference** \\ \hline \multirow{2}{*}{Transmitter LED} & OSRAM\({}^{\text{\textcircled{\textcircled{\textcircled{\textcircled{\textcircled{\textcircled{\textcircled{\text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@text@texttext@text@  
* Reproducibility and System Stability: A 1 m setup ensures consistent illumination intensity (300-440 lux) and signal power at the photo-detector, making it easier to reproduce results and compare performance under controlled variations in masking ratio, patch number, and batch size. * Focus on Concept Validation: As stated in the manuscript (Section 3, p. 6), the main focus of this work was to validate the concept and behavior of the proposed sparse coded mask modeling technique when applied to Li-Fi DMT signals, rather than to develop a fully optimized long-range Li-Fi system. Although the current experiment was performed over a short distance, the proposed technique is inherently scalable and can be adapted to longer-range or more complex Li-Fi scenarios. Here is why:  
* Transmission over Extended Distances: The proposed MAE-based framework is agnostic to transmission distance as long as the signal-to-noise ratio (SNR) at the receiver is sufficient to recover the transmitted DMT patches. In fact, as shown in Figure 7 of the manuscript, our system successfully recovered signals even at SNR levels as low as 22.5 dB, indicating robustness to moderate noise or signal degradation that may occur over longer distances. * Adaptation to Ambient Illumination and Multipath Effects: The proposed method already accounts for variable lighting conditions, as demonstrated in Figure 8. Additionally, the use of transformer-based MAE architecture enables modeling of non-local dependencies, which may help in learning signal patterns distorted by multipath effects in larger indoor environments.