## 5 Discussion and Future Scope
Hence, the use of VLC can reduce latency by its capability to simultaneously enhance illumination and communication to the users through a single device. MIMO structures [20] are required to ensure higher reliability and data throughput to provide a reduced latency and increased robustness to the VLC system. We can utilize a holographic MIMO [122] as a part of this novel beamforming technology to enable the evolution of wireless networks by allowing pervasive communications between humans and AR/VR devices. Such MIMO structures designed for 5 GB have an intelligent and software-reconfigurable paradigm responsible for fulfilling thevisions of ultra-low-latency communications, high throughput, and high connectivity. This holographic MIMO structure, consisting of an intelligent reflecting surface (IRS), known to increase transmission efficiency and generate radiation in the desired direction, is highly recommended to reduce the interference arising from the simultaneous transmission of packets. Then, tracking is performed by the mmWave/THz/VLC bands through the highly focused directional beams of the proposed beamforming technology to exploit user information (6 DoF), which includes location (up to 1 mm) and orientation (up to 0.5\({}^{\circ}\)). As the position and motion of the user over time are unknown to the radio access network (RAN) due to the dynamic nature of the headsets and wireless channel, the beam faces difficulties in adjusting its alignment according to the posture of the user through the HMD. As several AI-/ML-based techniques [123; 124] relying mostly on synthetic datasets for training and testing for performance verification produce unrealistic results due to the huge difference between the simulation settings and real network environments, we propose a learning algorithm for interacting with the dynamic and random virtual environment where the information packets can be successfully transmitted using an optimal scheduling policy. Furthermore, an optimal transmission queuing strategy can be utilized for the user's information based on the tracking results for their timely delivery, as presented in Figure 6. This figure shows the usage of several advanced technologies such as the deployment of a MIMO-based mmWave beamformer coupled with an intelligent reflecting surface (IRS) to produce guided beams to the users for low-latency packet processing. We would like to highlights the requirement for a novel learning approach of an optimal routing protocol or scheduling policy through beam alignment to meet the ultra-low-latency requirements in AR/VR. There are different techniques involved in obtaining an effective scheduling policy based on the AR/VR applications to avoid video buffering. The steps involved in the learning algorithm for different scenarios are as follows:  
* We divide the environment into simple scenarios (watching movies, attending virtual meetings, etc.) and hard scenarios (playing games, virtual training, etc.) based on the movement of the user's HMD. * For simple scenarios, we can consider reinforcement learning [125], where the the environment is defined by utilizing highly focused directional beams to track the posture of the HMD. Then, the action is based on the selection of the optimal beam for data transmission by learning (exploiting) the user's environment, i.e., the posture tracking of the user. Finally, the reward is decided by the action, which is an optimal scheduling policy that minimizes the delay during data delivery. * For hard scenarios, we can apply the combination of reinforcement learning and supervised learning, known as imitation learning (IL) [126], to tackle the rapid loss of the buffer packets as a result of frequent multi-modal interactions in such scenarios. This includes a preprocessing step, which involves generating an expert dataset through supervised training based on the possible outcome of the guided beams given that the pose of tracking (feedback) is known to the user.