# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Summary of Section 4  
Deep learning-based modulation recognition techniques have significantly improved classification performance in complex channel environments by leveraging automatic feature extraction and nonlinear mapping capabilities. Current research primarily concentrates on three major directions: innovation in neural network architectures, the optimization of feature representation, and cross-scenario generalization. Together, these areas form a technical system centered on deep neural networks, while also integrating new paradigms such as meta-learning and knowledge distillation. The innovations in relevant studies primarily focus on aspects of feature engineering, classification models, and training strategies, as illustrated in Figure 9. The multidimensional feature decomposition method using I, Q coordinates and polar coordinates has been consistently employed in recent years. This enables deep learning models to learn unique signal representations from four dimensions of the signal sequence. Constellation diagrams and their variants are also frequently used, thanks to the IQ space features displayed in the constellation diagrams. Processing signals into image formats is also conducive to directly inputting them into the rapidly developing image classification models for modulation recognition tasks. Novel data-processing methods, such as folding algorithms, have continuously emerged. The use of neural network-based pre-equalizers has also effectively alleviated the classification difficulties under low-SNR conditions. A summary and comparison of deep learning-based modulation recognition methods are presented in Table 4. CNNs exhibit significant advantages in the spatial feature extraction of signals, thanks to their local perception and weight-sharing mechanisms. Multi-dimensional feature fusion strategies effectively mitigate the limitations of traditional methods that rely on single-signal representations. Additionally, network designs that incorporate residual structures and attention mechanisms further enhance robustness against frequency offsets, phase noise, and other types of interference. However, the complexity of these models and their computational overhead still hinder their application in scenarios requiring high real-time performance. On the other hand, RNNs and their variants are designed to focus on modeling temporal dependencies, effectively capturing the dynamic characteristics of signals through gating mechanisms. They perform well under non-stationary channel conditions but inherently suffer from issues such as gradient vanishing and limited long-range modeling efficiency. Innovative models have sought to achieve a balance between accuracy and efficiency in specific scenarios by introducing physical priors, applying lightweight architectures, and implementing few-shot learning mechanisms. Nevertheless, further breakthroughs are still necessary in areas such as cross-domain generalization and complex interference suppression. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Year** & **Input** & **Model** & **Modulation Types** & **Typical Accuracy (\%)** & **Typical Conditions** \\ \hline [PERSON] et al. & 2020 & Pseudo constellation & \multirow{2}{*}{GoogLeNet V3} & BPSK, & BPSK, & \multirow{2}{*}{SNR = 15 dB} \\ \(\left[27\right]\) & & diagram & & & 4/8/16/32/64 QAM & & \\ \hline [PERSON] et al. & 2022 & Fan-beam & \multirow{2}{*}{AlexNet} & 2/4/8/16 PSK, & \multirow{2}{*}{100} & \multirow{2}{*}{OSNR = 15 dB} \\ \(\left[28\right]\) & & constellation & & 8/16/32/64 QAM & & \\ \hline [PERSON] et al. & 2022 & 2 \(\times\) 1024 IQ & \multirow{2}{*}{CNN} & OOK, BPSK, QPSK, & \multirow{2}{*}{99.98} & \multirow{2}{*}{SNR = 10–30 dB} \\ \(\left[29\right]\) & & sequence & & 16 QAM & \\ \hline [PERSON] et al. & 2022 & 2 \(\times\) 2N matrix & DrCNN & 4/8/16 QAMSPSK, & \multirow{2}{*}{98.3} & \multirow{2}{*}{SNR = 20 dB} \\ \(\left[30\right]\) & & & & OOK, 16 AFSK & \\ \hline \hline \end{tabular}
\end{table}