# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Other Innovative Deep Learning Models
On the other hand, representation vector decoupling (RVD) was introduced to decompose the teacher model's output probabilities into target and non-target class knowledge, enhancing dark knowledge transfer through dynamically weighted KL divergence:  
\[L_{\text{Decouple}}=L_{KL}\left(b^{T},b^{S}\right)+\left(1-p_{t}^{T}\right)L_{ KL}\left(\widehat{p^{T}},\widehat{p^{S}}\right), \tag{31}\]  
here, \(\widehat{p^{T}}\) and \(\widehat{p^{S}}\) represent the non-target class distributions after removing the target class probability \(p_{t}^{T}\), with \(\left(1-p_{t}^{T}\right)\) as a weight to suppress interference from high-confidence target classes, and \(\widehat{p^{T}}\) and \(\widehat{p^{S}}\) being the renormalized probabilities. This enhances the fine-grained discrimination ability for similar modulation formats. Ultimately, by jointly optimizing the feature alignment loss \(\widehat{L_{1}}\), decoupled distillation loss \(\widehat{L_{2}}\), and classification loss \(L_{3}\):  
\[Loss_{CIKD}=\widehat{aL_{1}}+\widehat{\beta L_{2}}+(1-\alpha-\beta)L_{3} \tag{32}\]  
The deep coupling between the communication prior module and the knowledge distillation path was effectively established. Experimental validation demonstrated that the solution exhibited strong robustness across 12 driving voltage points ranging from 100 to 1200 mV. For eight modulation formats (PAM4, QPSK, 8 QAM-CIR, 8 QAM-DIA, 16 QAM, 16 APSK, 32 QAM, and 32 APSK), at the optimal operating point of 800 mV, the student model achieved a recognition accuracy of 100%, representing an 87% improvement over the non-distilled baseline model. Even in the region of strong nonlinearity at 1200 mV, the accuracy remained at 70%, thereby confirming the solution's robustness against complex interference. Moreover, the student model had only 18% of the parameters of the teacher model, with reduced inference latency, highlighting the advantage of CIKD in terms of hardware deployment efficiency. [PERSON] et al. (2018) proposed a two-stage processing framework called VLCMnet to address the performance degradation caused by constellation diagram distortion in modulation format recognition for indoor VLC systems. The framework initially employs a hybrid model that combines temporal convolutional networks (TCN) and LSTM networks for channel equalization. This model extracts local signal features through five layers of residual modules that include dilated and causal convolutions, effectively mitigating the gradient vanishing problem through cross-layer connections. It then retains essential temporal information using a two-layer LSTM, significantly improving the quality of constellation diagrams and enabling accurate modulation format recognition even under low SNR conditions. In the classification stage, a multi-mixed attention network (MMAnet) is designed, which serially connects single-attention (SA) modules on the channel-spatial dual path and mixed-attention (MA) modules integrated with the squeeze-and-excitation (SE) mechanism. This design enhances sensitivity to constellation point distribution and improves the model's ability to discern edge features of higher-order modulations. Experiments utilized a dataset generated from DCO-OFDM that comprised five types of MQAM (M = 4, 8, 16, 32, 64) with SNRs ranging from \(-\)10 to 30 dB. The results indicated that VLCMnet achieved a recognition accuracy of 99.2% at a SNR of 4 dB and maintained an overall accuracy of 93.3% at a lower SNR of 0 dB, significantly surpassing the baseline CNN (78.8%), VGG16 (72.8%), and ResNet (68.4%).