# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Introduction to the VLC MFR Datasets  
Currently, there is no comprehensive open-source dataset for MFR in VLC. Most researchers collect data using simulation software such as MATLAB (see [[https://www.mathworks.com](https://www.mathworks.com)]([https://www.mathworks.com](https://www.mathworks.com)) accessed on 12 May 2025) and Optisystem (see [[https://optiwave.com](https://optiwave.com)]([https://optiwave.com](https://optiwave.com)) accessed on 12 May 2025), or through laboratory VLC systems. However, these datasets are not publicly available, and different teams consider varying communication scenarios and channel conditions when constructing their datasets. Next, we briefly introduce some commonly used open-source datasets in the field of RF modulation recognition as references and discuss the current status of dataset construction in VLC-MFR research. In the field of RF-MFR, several open-source datasets are widely used, including the RML2016 and RML2018 series from RadioML and HisarMod2019 (introduced in Table 2). The RML2016.10a dataset [26] simulates time-varying random channel effects (e.g., carrier frequency offset) using GNU Radio, generating 220,000 samples with 11 modulation types. The SNR ranges from \(-\)20 dB to 18 dB with a step size of 2 dB. This dataset has been widely utilized. The RML2018.01a dataset [41] is generated in a real laboratory environment with better conditions and more complex channel scenarios. It contains 24 modulation types, with a sample dimension of 2 \(\times\) 1024 and a total of 2,555,904 samples. The SNR ranges from \(-\)20 dB to 30 dB. While this dataset is large-scale, it also demands more resources. The HisarMod2019.1 dataset [42] provides multiple channel conditions, including ideal, static Rayleigh, Rician, and Nakagami-m channels. It contains 26 modulation types and 780,000 samples. This dataset is suitable for studying modulation recognition performance under different channel conditions and offers a rich and diverse data resource for AMR research. Next, we introduce some typical self-built datasets in current VLC modulation recognition research (the summary is presented in Table 3). In paper [28], [PERSON] et al. constructed an OWC system using Optisystem. They simulated eight modulation formats: 2/4/8/16-PSK and 8/16/32/64-QAM. The signals were generated at a bit rate of 10 Gbps and transmitted through a 4-km free-space optical link with an attenuation of 0.43 dB/km and an OSNR range of 5-30 dB. At the receiver, a constellation diagram analyzer estimated the constellation diagrams and converted them into grayscale images, each containing 4000 sampling points. Feature vectors were extracted using FB projection technology to form the dataset. The size of the dataset was not explicitly stated. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Channel Conditions** & **Modulation Types** & **File Format** & **Data Shape** & **Dataset Size** & **SNR Range (dB)** \\ \hline RML 2016.10a & Including time-varying channels such as carrier frequency offset, sampling rate offset, AVNC, multipath, and fading & & & & \\ \hline \multirow{4}{*}{RML 2018.01a} & Trend and generated in a real laboratory environment, considering more complex & 1675,  
In paper [29], [PERSON] et al. built a simulation system using MATLAB to emulate the satellite-to-ground FSO channel conditions. They considered four modulation formats: OOK, BPSK, QPSK, and 16-QAM. For each format, 20,000 signal frames were generated, with each frame containing 1024 sampling points. The signals were transmitted through a Gamma-Gamma atmospheric channel with AWGN added at SNR values ranging from 10 to 30 dB. The dataset consisted of 80,000 frames, with 80% allocated for training and 20% for validation. An additional 16,000 test frames were generated using the same method as the training data but with a different random seed. In paper [31], [PERSON] et al. generated a dataset using MATLAB. They considered interference factors such as AWGN, Doppler shift, Rician multipath fading, and clock offset.