# Machine Learning Applications for Short Reach Optical Communication
## 4 Machine Learning Applications for Indoor Optical Wireless Communication and Positioning
### Machine Learning Applications for Indoor Optical Wireless Communication System
#### 4.1.1 Machine Learning Applications for Indoor Visible Light Communications  
As the encoder/decoder, the paper [128] focused on the design of convolutional autoencoder (C-AE) to support the multi-carrier OFDM based OCC system. In the encoder network, one-hot vector that comes from the quantizer passes through the neural network, which includes two dense layers with the ReLU activation function, two convolution and max pooling layers, and another convolution layer with the sigmoid activation function. In the decoder network, the reverse order of the neural network structure is implemented. The results yield a 100% and 99% accuracy at the SNR of 10 dB for 16 QAM and 64 QAM transmitted signals, respectively.  
The ML technique is more widely used as the signal processor in indoor VLC systems. A memory-controlled deep LSTM neural network post equalizer to mitigate both linear and nonlinear impairments in a high-speed PAM-based VLC system has been studied [129]. The  
Figure 14: General applications of ML in indoor OWC systems.  
Figure 13: General architecture of indoor OWC systems.  
proposed post equalizer has a hidden layer consisting of a memory controlled deep LSTM neural network (32 layers), and a softmax function-based output layer corresponding to the modulation order. Experiment results show that compared with the conventional FIR-based equalizer, the Q factor can be improved by 1.2 dB using the LSTM-based post-equalizer, and the transmission distance is increased by over 30%. When further compared with the Volterra-based equalization nonlinear scheme, the complexity can be significantly reduced.  
In [130], the researchers proposed a Z-Score Averaging neural network (Z-NN) algorithm to reduce the ISI in rolling shutter based OCC system. The raw gray-scale values in R, G, B channels are processed by the Z-score averaging algorithm [131], and then fed into the FFNN, which has a hidden layer with four neurons and a sigmoid activation function output layer. The cross-entropy loss function is used to update the weight parameters. Compared to the results of two classic methods, at a transmission distance of 2.5 m, the traditional Extreme-Value-Averaging (EVA) scheme cannot decode the received pattern, whereas the logistic regression scheme can satisfy the FEC threshold at 960 bit/s, and the Z-NN algorithm can further improve the bit rate to 1200 bit/s. The ISI in rolling shutter based OCC systems has also been suppressed using a more complicated two-dimensional CNN (2D-CNN) to further improve the data transmission performance [132]. This 2D-CNN takes extracted signals in R, G, B channels as the input, followed by two cascaded convolution layers with different numbers of kernels and kernel sizes. Each CNN layer also adopts the ReLU activation function and batch normalization to improve the performance. A fully connected layer with eight neurons is then used for classification, and the softmax function produces the probability distribution of the symbols. The experiment compares the BER performance obtained by the ANN, 1D-CNN and double-equalization [133] at different data rates, and results show the 2D-CNN has the lowest BER.