# 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}
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**System Setup** & **ML Algorithm** & **Positioning Algorithm** & **Cell Size** & **Accuracy** & **Transmitter** & **Receiver** \\ \hline OOK Sim. [150] & MLP & TDOA & \(5\times 5\times 3\) m & \(1.662\) cm & 4 LEDs & PD \\ \hline OOK FDM 50 kbps [79] & MLP & RSS & \(1\times 1\times 2.5\) m & \(3.65\) cm & 3 LEDs & PD \\ \hline QPSK FDM 50 kbps [50] & MLP & RSS & \(0.9\times 1\times 0.4\) m & \(<\)1 cm & 3 LEDs & PD \\ \hline OOK 50 kbps [151] & MLP & Transformation & \(0.85\times 0.85\times 1.85\) m & \(1.49\) cm & 4 LEDs & Camera \\ \hline OOK 50 kbps [152] & MLP & RSS based finger-printing & \(5\times 5\times 3\) m & \(21.4\) cm & multiple IR-LEDs & PD \\ \hline OOK SDMA 9 W LED [80] & ELM & RSS based finger-printing & \(0.8\times 0.8\times 1.5\) m & \(3.65\) cm & multiple LEDs & PD \\ \hline OOK TDM 2 Mbps 25 W 3 MHz LED Sim. [154] & ELM & RSS based fingerprinting & \(0.8\times 0.8\times 1.5\) m & \(3.65\) cm & multiple LEDs & PD \\ \hline OOK SDMA 9 W LED [80] & SVM, RF, DT, KNN & RSS & \(5\times 5\times 2.5\) m & \(8.6\)–\(13\) cm & 4 LEDs & PD \\ \hline OOK Exp. [155] & TLFM & Fingerprints (RSS, DIFF, HLF) & \(3\times 2\times 2.5\) m & \(5\) cm & 4 LEDs & PD \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A summary of current ML based indoor localization performance. and 2.17\({}^{\circ}\), respectively, and the CNN can achieve 17.15 cm, 12.5\({}^{\circ}\) and 2.136\({}^{\circ}\), respectively. Besides, ref. [157] proposed a MLP algorithm to correct the position error caused by the tilt of the receiver in an OCC-based indoor positioning system, since when the tilt angle is different, the features like positions and shapes of the LEDs on the captured images are also different. At the input layer, five features of the projected ellipse image of the LEDs, including two position coordinates \((x,y)\), two axis parameters (major axis and minor axis) and the area, are fed into the MLP. The sigmoid activation function is used to map the nonlinear relationship between the input and the hidden nodes. The output feature is the distance between the transmitter and the receiver. Results show that with different numbers of hidden nodes 10, 30, and 50, the average positioning error is 3.6 cm, 2.1 cm and 1.9 cm, respectively, which significantly reduces the impact caused by the tilt camera. So far, most papers of ML-based indoor OWC localization applications only focus on improving the accuracy of position, while ignoring the importance of LED-ID detection and recognition. In [158], the authors design a ML-based LED identity (LED-ID) detection and recognition method for visible light-based positioning systems. It adopts the pulse width modulation (PWM) method with different frequencies and duty cycles to create unique features for each LED, so that the unique pattern of the image can be captured by camera. Two typical linear classifiers, named fisher classifier and SVM, are applied to classify the captured bright and dark stripes patterns from different LEDs. Experimental results show that the proposed scheme can improve the ability of LED-ID detection to classify a large number of LEDs for large scale indoor positioning systems.