# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 2 Status and Challenges of VLC
### Advantages and Disadvantages of ML in VLC  
To show the performance benefits and costs of machine learning in visible light communication, we simply construct a visible light simulation model based on [19; 25; 30], as shown in Figure 3. The second-order Volterra series is used to replace the one single tap time-discretization, which represents the memory rate equation. The conversion curve of voltage, current, and optical power is used to represent the memoryless optical transform. _exp_ is used as the overall channel frequency response. It should be emphasized that this is only a simplified simulation model; some parameters are determined by some communication experimental data. This simulation channel is not the focus of this article, but it is enough to illustrate the characteristics of machine learning. In order to visualize the performance of machine learning, we chose to compare it in the field of channel equalization. Figure 4 shows the performance difference between no channel equalization, traditional nonlinear equalization, and machine learning. We used the least mean square (LMS)-based second-order Volterra algorithm as a representative of the traditional nonlinear equalizer. Both linear and nonlinear taps were set to 31. A one-layer hidden-layer multilayer perceptron (MLP) was used as a representative of machine learning. The size of the input layer was 31 and the size of the hidden layer was 128. As demonstrated in the figure, both nonlinear algorithms can have a good performance improvement. MLP outperforms the Volterra algorithm at various SNR. However, at low SNR, the enhancement is not as much, which is because this is an additive noise-limited system at this point. At high SNR, the MLP's ability to compensate for nonlinearities is even more evident. Figure 3: Block diagram of VLC simulation model. Figure 2: PAPR of different modulation formats in VLC. As indicated by the above results, machine learning does improve performance. However, we also need to consider its drawbacks. The first is the computational complexity, as shown in Table 2. In this simulation, the trainable parameters of MLP reach 4225, while it is 527 for the Volterra algorithm. MLP has a much higher computational complexity than Volterra, which will be more obvious in actual systems where nonlinearities are more severe. This also means that machine learning algorithms need more convergence time. Another problem with machine learning is generalizability. Figure 5 shows some results of generalizability studies on the Volterra algorithm and MLP. It can be seen that different training data lengths affect the performance of the equalizer. If the trained model is used for other random seed-generated data, the performance is degraded by a specific degree. This degree of degradation is relatively less in the Volterra algorithm. As mentioned above, there are many different modulation formats in visible light communication. For this reason, we also tried to use the model trained based on OFDM signals for CAP signal recovery. In this respect, one can see the more serious generalizability problems of MLP. While machine learning has better performance, data-driven learning can cause it to learn features that do not belong to the channel, for example, the data stream itself. The issues mentioned above can significantly slow down the application and development of machine learning in intelligent visible light communication. Much work should be carried out to address these aspects. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Input Layer** & **1 st Weight Layer** & **2 nd Weight Layer** & **Drd Weight Layer** & **Trainable Parameters** \\ \hline \hline
**MLP** & \(N\) & \(W_{1}\) & \(W_{2}\) & \(W_{D}\) & \((N+1)\times W_{1}+\) \\
**(general)** & & & & & \(\sum\limits_{i=2}^{D}(W_{i-1}+1)\times W_{i}\) \\
**Volterra** & \(N_{lin}\), \(N_{non-lin}\) & / & / & / & \(N_{lin}+\frac{N_{non-lin}+1}{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational complexity of MLP and Volterra.