## 5 ML Intersection with 6G Enabling Technologies
### ML and VLC Integration for Optimization of 6G Wireless Network  
The VLC is a wireless communication approach that utilizes light-emitting diodes (LEDs) for signal transmission. It is a key component for future 6G enablers. The VLC is an optical wireless communication approach that has become known for its high speed and long-distance transmission success in wireless underground communication. The VLC is suited for highly directional, no license, and no interference transmission, which is one major requirement of the future 6G network [15]. VLC applications that integrate ML are becoming increasingly deployed for optimization purposes. [PERSON] et al. [185] proposed a unified resource allocation and mobility management algorithm for indoor VLC networks. The approach was compared to classical existing approaches such as Round Robin, Best Channel Quality Information (CQI), and Genetic algorithms in terms of system throughput and user satisfaction index. The authors reported that the DCO-OFDM method outperformed all the other methods. The highest user satisfaction index was attained after 50 iterations when the number of users N = 6 users. As users increased, longer iterations were required to achieve convergence. [PERSON] et al. [186] integrated a deep reinforcement learning approach with VLC access points to achieve a seamless handover protocol and a selection algorithm. System performance was compared with four different RL algorithms (DQN, Q-learning, Sarsa, HS). The results showed that the proposed algorithm outperformed the explored approaches by over 66% in terms of the number of handovers at a UD speed of 0.5 m/s. Similar technological convergence has been observed to improve positioning in VLC networks [187]. Wk-NN achieved the least positioning average error (3.04 cm) and the 90 th percentile error (5.10 cm) compared to the other methods. Other positioning applications have reported significant improvement by integrating ML techniques into VLC networks [188; 189]. An LSTM-based signal detection method was compared to traditional LS estimation, and with CNN in terms of SERs for different SNRs for VLC application [190]. The authors reported that the LSTM-based approach achieved better performance than both the LS and CNN methods. Though the authors varied the amount of training data in the range of 100%, 90%, and 80% for fixed scenarios and 20% and 10% for random scenarios, the ML approaches (LSTM and CNN) showed better performance compared to the conventional LS method. Similar success has been reported for augmented spatial modulation applications [191], where the authors explored three ML approaches (SVM, LR, and NN) integrated with massive MIMO, VLC, and IoT technologies. The results showed that ML integration enhanced the accurate identification of 1000 transmitters with an accuracy above 99%. The single-layer NN showed constant computation time as the number of transmitters increased and yielded better identification accuracy. ML has also been reported to demonstrate linear time complexity, lower transmission delays, and improve the overall capabilities of a VLC- and D2D-enabled heterogeneous network [192]. A DRL-based