论文标题
基于学习的预测，渲染和关联优化对MEC启用的无线虚拟现实（VR）网络
Learning-based Prediction, Rendering and Association Optimization for MEC-enabled Wireless Virtual Reality (VR) Network
论文作者
论文摘要
无线连接的虚拟现实（VR）为任何地方的VR用户提供沉浸式体验。但是，由于在VR设备的有限计算能力下，为其对高质量的经验（QOE）和低VR相互作用延迟的要求提供了高质量的无缝连接性和实时VR视频的无缝连接性和实时VR视频。为了解决这些问题，我们提出了一个启用MEC的无线VR网络，可以使用复发性神经网络（RNN）实时预测每个VR用户的视野（FOV），并且具有呈现模型迁移能力的VR设备的VR设备的渲染从VR设备转移到MEC服务器。考虑到地理和FOV请求相关性，我们提出了集中式和分布的解耦深入学习（DRL）策略，以最大程度地利用VR交互潜伏期约束的VR用户的长期QOE。仿真结果表明，与VR设备的渲染相比
Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from any-where at anytime. However, providing wireless VR users with seamless connectivity and real-time VR video with high quality is challenging due to its requirements in high Quality of Experience (QoE) and low VR interaction latency under limited computation capability of VR device. To address these issues,we propose a MEC-enabled wireless VR network, where the field of view (FoV) of each VR user can be real-time predicted using Recurrent Neural Network (RNN), and the rendering of VR content is moved from VR device to MEC server with rendering model migration capability. Taking into account the geographical and FoV request correlation, we propose centralized and distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize the long-term QoE of VR users under the VR interaction latency constraint. Simulation results show that our proposed MEC rendering schemes and DRL algorithms substantially improve the long-term QoE of VR users and reduce the VR interaction latency compared to rendering at VR devices
