# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Convolutional Neural Networks (CNN)
Experiments demonstrated that this approach achieved a recognition rate of 99.98% under strong turbulence channel conditions. [PERSON] et al. [30] addressed the limitations of traditional coordinate feature representation and the decline in recognition performance under low-SNR conditions by introducing a novel method for constructing a four-dimensional composite feature matrix that combines I/Q components and polar coordinates (\(A/\theta\)) within the VLC scenario. This approach aims to overcome the recognition bottleneck encountered in low-SNR environments. Experimental verification shows that this multi-domain feature fusion enables CNNs to simultaneously capture the Cartesian spatial distribution patterns of QAM-type modulations, which rely on I/Q components, as well as the phase-ring characteristics of PSK/APSK-type modulations, which depend on the \(\theta\) component. The coordinate fusion training method proposed in this paper effectively identifies 16 th-order signals. Under high-SNR conditions, the MFR accuracy can reach 98.3%. Even under extremely poor SNR conditions, this method can still distinguish high-order signals, achieving an accuracy improvement of over 10% compared to traditional IQ signal training models. [PERSON] et al. [31] proposed a modulation format recognition algorithm based on the improved YOLOv5s to address the decreased recognition accuracy caused by background noise and multipath fading interference in complex channel environments. The research team integrated Mixup and Mosaic data augmentation methods at the input stage, thereby enhancing the model's generalization ability against noise interference and signal distortion through linear mixing of time-frequency diagrams and multi-image stitching techniques. Additionally, an adaptive spatial feature fusion (ASFF) mechanism was introduced in the feature fusion layer, optimizing the integration of multi-scale features through dynamic weight allocation and mitigating the issue of feature redundancy commonly encountered in traditional feature pyramid networks. The enhanced algorithm achieved a mean average precision ([EMAIL_ADDRESS]) of 0.903 under mixed-SNR conditions ranging from 0 to 20 dB, reflecting a 0.7% improvement over the original YOLOv5s. At 20 dB high SNR, it reached an mAP of 0.993, all while maintaining a model parameter size of 24.17 MB, which represents a 79.3% reduction compared to YOLOv3. This study effectively enhanced the algorithm's robustness under mixed-SNR conditions through the use of bimodal data augmentation and adaptive feature fusion strategies. However, while the Mixup and Mosaic data augmentation methods in this reference enhance the model's robustness on the current dataset, they may not completely account for all noise and interference patterns present in real-world channel conditions. In vehicular visible light communication (V-VLC) scenarios, dynamic traffic environments, and complex channel conditions present significant challenges for modulation format recognition. [PERSON] et al. [32] proposed a fusion scheme that integrates the Hough transform with deep learning techniques. Focusing on the infrastructure-to-vehicle (I2V) communication scenario, the study constructed a three-dimensional road model usingnon-sequential ray tracing and developed a channel model that incorporates weather attenuation effects. Innovatively, the Hough transform was applied to constellation diagram feature extraction through polar coordinate transformation, represented by the equation:  
\[\rho=x\mathrm{cos}\,\theta+y\mathrm{sin}\,\theta. \tag{23}\]  
This transformation projects the modulated signal constellation into the \((\rho,\theta)\) parameter space, enhancing the amplitude and phase features of modulation formats such as QPSK and QAM.