# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Other Innovative Deep Learning Models  
[PERSON] et al. [34] proposed a few-shot modulation recognition framework based on progressive growth meta-learning (PGML) to tackle the signal degradation caused by underwater turbulence and LED nonlinearity. This framework enables rapid adaptation with minimal samples through a staged training approach and a complexity-incremental mechanism. In the early stages of training, tasks from fewer batches are utilized to learn the common features associated with channel fading. Subsequently, more batches including more diverse task combinations are gradually introduced, creating a progressive knowledge path. The framework employs a double-layer optimization structure inherent to meta-learning, facilitating dynamic knowledge transfer. The inner layer quickly adjusts parameters within a single task, while the outer layer aggregates and optimizes across multiple tasks. Coupled with data compensation strategies such as rotation augmentation, the framework effectively captures essential features, including phase rotation invariance. Simulation results indicate that the framework achieves a validation accuracy of 95.63% under a low-SNR scenario of 6 dB, demonstrating good robustness against Poisson noise and nonlinear distortion. Compared to the traditional model-agnostic meta-learning (MAML) [35] method, it exhibits significant improvements and reduces the average training time per episode by 23.27%. [PERSON] et al. [36] proposed an enhanced solution that introduces active learning (AL) and transfer learning (TL) to address the challenge of acquiring labeled data in VLC systems. They employed the AlexNet-AL/TL model to classify six types of MQAM (M = 2, 4, 8, 16, 32, 64) modulated signals. To tackle the difficulty of feature extraction from small sample sizes, they utilized neighborhood statistics and tricolor encoding on the constellation points of the received signal, constructing a contour stellar image that enhanced density features. Additionally, they applied random geometric augmentation with rotations ranging from \(-90^{\circ}\) to \(90^{\circ}\) to increase data diversity. Using AlexNet as the base CNN model, the AL approach predicted unlabeled samples in each training round, selecting the most uncertain samples for labeling and subsequent training based on the margin sampling (MS) strategy. The TL approach involved fine-tuning a model pre-trained on ImageNet specifically for the VLC-AMC task. The results indicated that in an extremely small sample scenario with only 60 labeled samples, the introduction of AL and TL achieved accuracy improvements of 6.82% and 14.6%, respectively, compared to the traditional AlexNet model. Following the application of data augmentation techniques, the classification accuracy of the AlexNet-AL model increased from 77.45% to 88.78%. In this reference, there appears to be a significant domain shift between the source domain (ImageNet classification) and the target domain (VLC MFR), which may limit the effectiveness of transfer learning. Future research could benefit from using the RadioML series datasets. These datasets are widely utilized in RF signal modulation format recognition. They could serve as a valuable resource for pre-training. [PERSON] et al. [37] proposed a lightweight solution based on reservoir computing (RC) to address the challenge of real-time modulation recognition in UVLC. To tackle the issue of non-distinctive features of PSK signals in the Cartesian coordinate system, they introduced a dual-domain coordinate transformation method. For the original complex signal \(y(i)\), they extracted both the IQ constellation map in the Cartesian coordinate system and the joint feature map of amplitude and phase in the polar coordinate system. They then designed a folding function based on the signal-phase symmetry to iteratively compress the feature space.