# Machine Learning Applications for Short Reach Optical Communication
## 4 Machine Learning Applications for Indoor Optical Wireless Communication and Positioning
### Machine Learning Applications for OWC Based Indoor Positioning
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wavelengths** & **Applications** & **ML Algorithms** & **References** \\ \hline \multirow{8}{*}{Visible light} & \multirow{2}{*}{Encoder/decoder} & CNN, DBN, & [128; 139; 140] \\  & & AdaBoost ANN & [128; 139; 140] \\ \cline{2-4}  & \multirow{2}{*}{Equalization} & K-means, LSTM, MLP, & \\  & & CNN, DNN-LMS & [129; 130; 141; 132] \\ \cline{2-4}  & \multirow{2}{*}{Physics layer security} & CNN, K-means & [137; 142] \\ \cline{2-4}  & \multirow{2}{*}{Heterogeneous handover} & \multirow{2}{*}{ANN} & [138; 143; 144] \\ \hline \multirow{4}{*}{Near-infrared} & \multirow{2}{*}{Delay tolerance} & LSTM-RNN, & \\  & & ALSTM-RNN & \\ \cline{1-1} \cline{2-4}  & FPGA-based & & \\ \cline{1-1} \cline{2-4}  & hardware accelerator & RNN & [136] \\ \hline \hline \end{tabular}  
* DBN: deep belief network; AdaBoost: adaptive boosting; DNN-LMS: deep neural network-least mean square.  
\end{table} 
Table 3: A summary of current ML based applications in indoor OWC system. \(1\)cm within a \(0.9\times 1\times 0.4\) m unit, which has been significantly improved when compared with the one hidden layer algorithm. Currently, OWC-based localization schemes mostly assume the receiver and transmitter planes are parallel, which ignore the critical tilt angle of the receiver in practical scenarios. To address this problem, some researchers applied the inertial measurement unit (IMU) to measure the user equipment (UE) tilt angle for positioning [156]. However, the accuracy of IMU in indoor environment may not be guaranteed, and the extra device can increase the cost of the system. From this point of view, ML algorithms can provide a promising solution to estimate the orientation of receiver and to achieve better positioning performance. In [153], the authors employed two deep ANN models, one based on the MLP and the other based on CNN, to map the instantaneous received SNR to the user 3D position and the UE orientation efficiently. Figure 16 illustrates the proposed simultaneous position and orientation estimation algorithm. For the proposed MLP, the pre-processed SNR of the received signals work as input features. There are \(d\) hidden layers where each hidden layer consists of the dense layer, ReLU layer, dropout layer and normalization layer. The proposed CNN has a similar structure except replacing the dense layer by the convolution layer. The output layer consists of six neurons, where each output neuron is related to one of the three position parameters and the three angle parameters \((x,y,z,\alpha,\beta,\gamma)\). Results show that for an indoor room with a size of \(5\times 5\times 3\) m and considering both LOS and NLOS channels, when compared with the popular KNN algorithm, which achieves estimation accuracies of 29.5 cm, \(17.7^{\circ}\) and \(3.925^{\circ}\) for the position, \(\alpha\), and \(\beta/\gamma\), respectively, and the proposed MLP and CNN can realized better performance: the MLP algorithm can achieve 21.4 cm, \(15.5^{\circ}\)