# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Convolutional Neural Networks (CNN)
Under the bit error rate (BER) threshold, the modulation format of each DMT subcarrier can be classified with 100% accuracy using only 75 received symbols. Furthermore, the accuracy of the proposed scheme is not only higher but also more stable compared to the cumulant-based method, while requiring fewer received symbols. [PERSON] et al. [28] addressed the challenge of blind recognition of high-order modulations in optical wireless communication by introducing fan-beam projection (FBP) from computed tomography (CT) imaging technology as a feature enhancement method. This technique transforms the two-dimensional constellation diagram into high-discrimination projection features, effectively overcoming the classification failures of traditional methods under low optical signal-to-noise ratio (OSNR \(<\) 10 dB) conditions due to histogram similarity. The FBP performs multi-angle line integrals using diverging ray clusters to compute projection data of constellation diagram image matrices, extracting more distinguishable features. The geometric principle of FBP is illustrated in Figure 8. The rotation angle of the light source is characterized by \(\beta\), which determines its offset relative to the initial position. The direction of each projection ray is precisely defined by the coordinate pair \((\sigma,\beta)\), where \(\sigma\) represents the angle between the ray and the central axis. The fan-beam (FB) projection transforms the ray coordinates \((\sigma,\beta)\) into polar coordinates \((s,\theta)\) using the following formulas:  
\[s=Dsin\sigma, \tag{21}\]  
\[\theta=\sigma+\beta, \tag{22}\]  
where \(D\) is the distance from the projection source to the image center. This transformation normalizes the ray coordinates to polar form. By rotating the angle \(\beta\) from 0 to 2\(\pi\), multi-angle projection data are obtained, forming a feature vector. This process transforms the constellation image, significantly enhancing inter-class separability. The researchers created a simulation dataset with eight modulation formats (2/4/8/16-PSK and 8/16/32/64-QAM) across an OSNR range of 5-30 dB. During preprocessing, constellation diagrams were converted to grayscale, and FBP was used to generate feature maps. These maps were then fed into networks like AlexNet and VGG for training. Experimental results demonstrated that FBP enabled AlexNet to achieve a full-format recognition rate of 100% at an OSNR of \(\geq\)15 dB, thereby reducing the required OSNR for classification by 5-8 dB compared to the original constellation diagram, highlighting the strong noise robustness of the FB features. This work, through interdisciplinary technological integration, established a mapping mechanism between FBP and constellation diagram feature enhancement, providing a low-complexity, noise-insensitive solution for adaptive modulation systems. In 2022, [PERSON] et al. [29] tackled the challenge of modulation recognition in the dynamic channel environment of satellite-to-ground free-space optical communication (FSO) by propos  
Figure 8: Geometry of FBP in reference [28]. ing a blind recognition framework based on CNN. This study moved away from the traditional reliance on prior channel information, transforming the received complex signal into two-dimensional I/Q data as input to simulate an image-processing mechanism for end-to-end classification. To enhance the model's generalization capability, the Gamma-Gamma channel model was employed to accurately simulate the effects of weak, moderate, and strong turbulence. Training data were generated by randomly combining channel parameters within an SNR range of 10-30 dB, compelling the network to learn common features across various scenarios. The network architecture consisted of a six-layer convolutional block structure, which balanced feature abstraction and detail retention by utilizing max pooling and average pooling. In strong turbulence scenarios, robustness was enhanced by integrating average pooling and batch normalization techniques, with a dropout rate of 0.6.