AI-Assisted E2E Network Slicing for Integrated Sensing and Communication in 6G Networks

Hossain, Mohammad Arif; Xiang, Amanda; Kiani, Abbas; Saboorian, Tony; Kaippallimalil, John; Ansari, Nirwan

doi:10.1109/jiot.2023.3326761

Cited by 4 publications

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Naguib,

Ibrahim,

Elmessalawy

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

show abstract