Naguib Khaled Mohamed, Ibrahim Ibrahim Ismail, Elmessalawy Mahmoud Mohamed, Abdelhaleem Ahmed Mostafa
CCAS Department, School of Engineering, New Giza University (NGU), Giza, Egypt.
Department of Electronics and Communications, Faculty of Engineering, Helwan University, Cairo, Egypt.
Sci Rep. 2024 Oct 28;14(1):25695. doi: 10.1038/s41598-024-75575-y.
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.
虚拟现实（VR）的数据传输在提供强大的VR体验方面发挥着重要作用。这对高带宽和低延迟的需求不断增加。软件定义网络（SDN）和资源切片等6G新兴技术有望满足VR用户的传输需求。高效的资源管理对于确保令人满意的用户体验至关重要。深度强化学习（DRL）的集成实现了动态网络资源平衡，最大限度地减少通信延迟并无线最大化数据传输速率。采用切片技术进一步有助于跨网络管理不同服务（如增强型移动宽带（eMBB）和超可靠低延迟通信（URLLC））的分布式资源。所提出的用于6G蜂窝网络的基于VR的SDN系统模型有助于集中管理资源，增强VR用户之间的通信。这种创新解决方案旨在为6G蜂窝网络中VR视频传输所需的有效且简化的资源管理做出贡献。深度强化学习（DRL）方法的应用作为一种替代解决方案被提出，通过比较结果展示了显著的性能和特征差异。我们的结果表明，在动态和大规模网络中，实施基于DRL的策略会使资源管理过程以及可实现的数据速率有相当大的提高，并减少所需的延迟。
