人工智能辅助改进 5G NR 上低延迟 XR 的服务供应

Moyukh Laha;Dibbendu Roy;Sourav Dutta;Goutam Das
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摘要

扩展现实(XR)是最重要的 5G/6G 媒体应用之一,它将从根本上改变人类的交互方式。然而,确保低延迟、高数据速率和可靠性以支持 XR 服务是一项重大挑战。这封信提出了一种新颖的人工智能辅助服务供应方案,该方案利用预测帧进行处理,而不是完全依赖实际帧。这种方法无形中增加了网络延迟预算,从而改善了服务供应,尽管代价是会出现轻微的预测误差。大量的仿真验证了所提出的方案,表明支持的 XR 用户数量增加了数倍,同时也提供了重要的网络设计见解。
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AI-Assisted Improved Service Provisioning for Low-Latency XR Over 5G NR
Extended Reality (XR) is one of the most important 5G/6G media applications that will fundamentally transform human interactions. However, ensuring low latency, high data rate, and reliability to support XR services poses significant challenges. This letter presents a novel AI-assisted service provisioning scheme that leverages predicted frames for processing rather than relying solely on actual frames. This method virtually increases the network delay budget and consequently improves service provisioning, albeit at the expense of minor prediction errors. The proposed scheme is validated by extensive simulations demonstrating a multi-fold increase in supported XR users and also provides crucial network design insights.
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Table of Contents IEEE Networking Letters Author Guidelines IEEE COMMUNICATIONS SOCIETY IEEE Communications Society Optimal Classifier for an ML-Assisted Resource Allocation in Wireless Communications
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