# 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  
Nowadays, indoor localization is an attractive research topic due to its application in the location-based services, such as indoor navigation, opportunistic marketing and smart healthcare [145]. Since the Global Positioning System (GPS) in indoor applications suffers from severe attenuation and multipath dispersion, it is difficult to provide accurate position information. Therefore, alternative solutions have risen including RF-based and OWC-based methods [146]. For indoor localization, the RF-based methods such as Wi-Fi, Bluetooth, RFID and Zigbee have been widely investigated and applied [147]. However, due to the signal attenuation and multipath dispersion, the accuracy for RF-based indoor localization schemes is typically limited to several tens of centimeters. Compared to RF based techniques, the OWC based approaches can provide better accuracy in the centimeter scale and offer immunity to electromagnetic interference in sensitive environment, like hospitals and airplanes [148]. Similar with RF-based positioning systems, the OWC-based indoor positioning can be classified into three categories: the RSS, angle-of-arrival (AOA) and time-of-arrival (TOA)/time difference of arrival (TDOA) based approaches. For RSS-based methods, the received signal intensity reduces as the distance between transmitter and receiver increases, thus providing location estimation at the receiver side. TOA/TDOA explores the absolute or different time of signal arrival from the transmitter to the receiver to calculate the distance for localization. AOA refers to the propagation direction of light incident on the receiver for localization, which can typically be obtained from the image transformation and channel model [149]. However, the aforementioned approaches require precise channel models, accurate time synchronization and low background noise interference to realize accurate indoor localization. Compared to traditional OWC localization methods, the promising ML algorithms can bring better performance and efficiency to indoor OWC positioning applications. The Table 4 provides a summary of current ML based indoor localization performance. The study [150] proposed a TDOA based ANN method for two-dimensional indoor localization. The obtained time difference of the received signal from different LEDs works as the input feature to the ML model. A simple MLP algorithm is used to train the datasets, and a sigmoid activation function is used to map the actual position at the output layer. Simulation results show that 1.662 cm positioning error can be realized in a \(5\times 5\times 3\) m room. In [151], the researchers demonstrated a two-layer ANN to achieve RSS-based three-dimensional indoor positioning. The RSS information is collected from three LEDs as the input of the ANN. The sigmoid activation function is applied in the two hidden layers, with 8 nodes and 5 nodes, respectively, and there is no activation function used in the output layer. The Adam optimization algorithm is adopted for training the proposed deep learning model. The measured results show that the average positioning error is less than