# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 5 Conclusions and Outlook
### Current Challenges  
In promoting the practical implementation and large-scale deployment of VLC in 6G integrated scenarios, MFR technology still faces challenges in multiple aspects. These challenges cover algorithm limitations, data dependency, hardware constraints, and environmental complexity, as described below. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Input** & **Model** & **Modulation Types** & **Typical Accuracy (\%)** & **Typical Conditions** \\ \hline [PERSON] et al. & 2024 & Time-frequency diagram & YOLOv5s & BPSK, QPSK, BPSK, 16 QAM & 0.993 (mAP) & SNR = 20 dB \\ \hline [PERSON] et al. & 2024 & \begin{tabular}{c} Hough transform constellation \\ diagonalization \\ \end{tabular}  & Pre-Trained AlexNet & QPSK, 8/16/5K, 4/8/16/32/64 QAM & 100 & SNR = 12 dB \\ \hline [PERSON] et al. & 2024 & \(4\times\) N sequence & BGRU & \begin{tabular}{c} 2/4/8/16/32/64 QAM, \\ 8/16/32/64/APSK \\ \end{tabular}  & \textgreater{}96 & Linear working area \\ \hline [PERSON] et al. & 2020 & Constellation diagram & PGML-CNN & \begin{tabular}{c} 4/8/16/32/64/128/256 \\ QAM, 2/4/8 ASK, \\ 2/4/8/16/32/PSK \\ \end{tabular}  & 95.63 & SNR = 6 dB \\ \hline [PERSON] et al. & 2022 & \ \begin{tabular}{c} Contour stellar \\ image \\ \end{tabular}  & AlexNet-AL & 2/4/8/16/32/64 QAM & 88.78 & SNR = 0–15 dB \\ \hline [PERSON] et al. & 2023 & \begin{tabular}{c} IQ samples \\ processed by \\ coordinate \\ transformation and \\ folding algorithm. \\ \end{tabular}  & Reservoir & OOK, 4 QAM, 8 QAM-DAL, 8 QAM-CIR, 16 APSK,1. Algorithm limitations in dynamic and non-stationary channels Traditional likelihood-based (LB) methods, although theoretically optimal, have excessively high computational complexity when applied to VLC systems. For instance, hybrid likelihood ratio tests (HLRT) require iterative parameter estimation and statistical averaging, which become intractable under LED nonlinearity and multipath scattering effects. Feature-based (FB) methods, while less computationally intensive, show vulnerability in distinguishing modulation formats with overlapping feature distributions under low-SNR conditions (e.g., high-order QAM and APSK). Deep learning (DL) models mitigate these issues through automatic feature learning but introduce new bottlenecks: mainstream CNN and RNN models struggle to balance model depth and real-time inference latency, especially in resource-constrained edge and miniaturized devices. 2. Data scarcity and domain-shift issues The lack of standardized open-source datasets for VLC-MFR forces researchers to rely on simulation or laboratory-generated data under limited conditions, restricting model generalizability. Current research mostly employs narrow-range datasets simulating specific communication scenarios, which fail to capture the full spectrum of real-world variations (e.g., dynamic mobility, heterogeneous interference). When models encounter unforeseeable unfamiliar scenarios and interferences, significant performance degradation occurs. Moreover, DL models require large amounts of labeled data, which is impractical for field deployments with high labeling costs. 3. Bottlenecks in hardware-algorithm co-design VLC mobile terminal hardware systems prioritize low-power operation, but mainstream DL architectures impose heavy computational loads. Lightweight models, pruning operations, and quantization methods can effectively reduce the burden on hardware devices but still sacrifice some feature richness. Meanwhile, hardware imperfections (such as LED nonlinearity and photodetector response delay) unpredictably distort signal features. Additionally, synchronization errors in practical systems (e.g., symbol timing offset) have not yet been considered in simulations. Future work should combine with actual hardware for the co-design of software and hardware. 4. Cross-scenario robustness and scalability Most MFR solutions have been optimized for specific isolated environments (indoor, underwater, vehicular), lacking general adaptability.