## 4 Experimental Results
However, further increasing the patch count to 64 resulted in higher EVM due to excessive fragmentation, which hinders temporal continuity learning and increases model complexity. Therefore, a trade-off exists between information density and spatial resolution, requiring careful tuning depending on the modulation scheme and waveform complexity. * Figure 6 illustrates the influence of batch size on EVM. A smaller batch size introduces stochastic gradient noise during training, promoting generalization and aiding the MAE in learning diverse temporal patterns. On the other hand, large batch sizes tend to stabilize training but may lead to overfitting limited data patterns. An optimal batch size of 32 was selected to balance memory efficiency and reconstruction performance in our implementation. For your information, the practical feasibility of integrating the proposed masked autoencoder-based sparse coded mask modeling into real-world Li-Fi systems can be considered as follows:  
* Practical Feasibility in Real-World Li-Fi Systems:
* Hardware Compatibility:
* Our proposed method operates entirely in the digital baseband domain. The masking and reconstruction processes are applied to the digital representation of DMT waveforms before digital-to-analog conversion. Therefore, no modifications to the optical front-end (e.g., LEDs, photodiodes) are required. This makes our method highly compatible with the existing Li-Fi transceiver hardware. * Integration into DSP Chains:
* The MAE-based reconstruction module can be embedded as part of the digital signal processing (DSP) pipeline on the receiver side. Given that many commercial Li-Fi systems already include FPGA- or SoC-based DSP platforms, deploying our model using an efficient inference engine (e.g., TensorRT, ONNX, or custom hardware accelerators) is feasible. Quantization-aware training can further reduce computational overhead for edge deployment. * Latency and Complexity:
* Our proposed system is based on a relatively lightweight transformer encoder-decoder architecture (4 layers, 128-dim embeddings), and inference time per DMT frame is in the sub-millisecond range on modern embedded GPUs. Furthermore, the patch-based nature of our method supports parallelization across hardware threads. * Adaptability to Dynamic Environments:
* The masking ratio can be dynamically adjusted based on real-time channel conditions (e.g., under high interference or signal loss). The model's robustness to high masking ratios (up to 85%) provides flexibility and resilience under varying Li-Fi deployment scenarios (e.g., mobility, partial blockage). * Training and Update Strategy:
* Since the MAE model follows a self-supervised training scheme, it can be periodically retrained or fine-tuned using unlabeled waveform data collected from the field. This supports lifelong learning in deployed systems without manual annotation. * Energy Efficiency Considerations:
* Because the proposed method reduces the total number of transmitted samples via masking, it has the potential to reduce average power consumption on the transmitter side--an important factor in energy-sensitive Li-Fi applications. In summary, the proposed model is both computationally tractable and practically deployable within existing Li-Fi system architectures. Also, the generalizability of our proposed technique extends beyond DMT-based Li-Fi transmissions. Since the masked autoencoder framework is particularly effective at reconstructing multivariate time-series data, the following potential applications are envisioned. * OFDM-based Visible Light Communications (VLC)
* Similar to DMT, Orthogonal Frequency Division Multiplexing (OFDM) systems also encode signals over multiple subcarriers. The proposed masking and reconstruction strategy can be applied to compress OFDM waveforms for high-throughput VLC systems, especially in scenarios constrained by physical LED bandwidth.