## 6 Conclusions  
This article provided insights into the various contributions of technological advancements in 5G and B5G networks to enable ultra-low latency for a high-quality immersive experience in AR/VR technologies. The main categories of AR/VR applications along with their benefits in each category were discussed. The major requirements of the powerful technologies in different emerging AR/VR use cases were highlighted from the hardware and software perspectives to enable a low-latency AR/VR system. Several factors responsible for maintaining the trade-offs of the performance parameters in order to enable a low-latency and reliable wireless network were presented. An innovative wireless network design integrated with ground-breaking technologies such as WiFi 7, the Tactile Internet, and edge computing were shown as the key enablers of 5G and the IoT in AR/VR applications. The usage of these technologies was highlighted as the most-powerful tools to perform intensive tasks while operating through an ultra-reliable and sub-millisecond-latency communication to enable a high-connectivity ecosystem. Future directions with a focus on high update rates for package delivery for low latency transmission were explored in this article. An optimal transmission queuing strategy was described with the use of several advanced technologies, such as the deployment of MIMO-based mmWave beamformers and intelligent reflecting surfaces for the timely delivery of the user's tracking information. Moreover, the combination of VLC with holomorphic MIMO was presented as a potential solution to enable high reliability and data throughput in an AR/VR wireless architecture. An accurate route for transmission can be obtained by using this optimal strategy by exploring the semantic level of features by selecting or predicting the shortest path for high delivery of video frames. Visionary solutions, including the introduction of learning algorithms using RL and IL based on different scenarios, were described to achieve an optimal selection of the best scheduling policy for an evolved and extremely immersive AR/VR experience. Figure 7: This diagram describes different methodologies involved in the proposed learning algorithm to decide the optimal scheduling policy for low-latency data delivery. Two main categories of scenarios were assumed, and reinforcement learning (RL), as well as imitation learning (IL) are tailored to them accordingly. * [1] [PERSON]; [PERSON] _6G: The Road to the Future Wireless Technologies 2030_; CRC Press: Boca Raton, FL, USA, 2022. * [2] Physical Layer Enhancements for NR Ultra-Reliable and Low Latency Communication (URLLC), Document 3 GPP RP-191584, TSG-RAN#84, 2019. Rel-16. Available online: [https://portal.3 gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3498](https://portal.3 gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3498) (accessed on 12 February 2023). * [3] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Wireless access in ultra-reliable low-latency communication (URLLC). _IEEE Trans. Commun._**2019**, _67_, 5783-5801. [CrossRef]
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