Virtual Reality Over Wireless Networks: Quality-of-Service Model and Learning-Based Resource Management
Virtual Reality Over Wireless Networks: Quality-of-Service Model and Learning-Based Resource Management
复制标题
无线网络虚拟现实：服务质量模型和基于学习的资源管理
DOI：
10.1109/tcomm.2018.2850303
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发表时间：
2018-11-01
影响因子：
8.3
通讯作者：
Yin, Changchuan
中科院分区：
文献类型：
--
作者：
Chen, Mingzhe;Saad, Walid;Yin, Changchuan
本文研究了无线虚拟现实（VR）用户通过小小区网络（SCN）进行通信的网络资源管理问题。为了获取虚拟现实用户在供应链网络中的服务质量，提出了一种基于多属性效用理论的虚拟现实模型。该模型共同考虑了VR指标，例如跟踪精度，处理延迟和传输延迟。在该模型中，小型基站（SBS）充当VR控制中心，通过蜂窝上行链路收集来自VR用户的跟踪信息。一旦收集到这些信息，SBS将通过下行链路向VR用户发送3D图像和附带的音频。因此，VR无线网络中的资源分配问题必须联合考虑上行链路和下行链路。这个问题，然后制定为一个非合作的游戏和分布式算法的回声状态网络（ESNs）的机器学习框架的基础上，提出了找到这个游戏的解决方案。该算法使SBS能够预测每个SBS的VR QoS，并保证收敛到混合策略纳什均衡。分析结果表明，每个用户的VR QoS共同取决于VR跟踪精度和无线资源分配。仿真结果表明，所提出的算法产生显着的收益，在VR的QoS效用方面，分别达到22.2%和37.5%，相比Q学习和基线比例公平算法。实验结果还表明，该算法比Q学习算法具有更快的收敛时间，并且可以保证VR服务的低延迟。
In this paper, the problem of resource management is studied for a network of wireless virtual reality (VR) users communicating over small cell networks (SCNs). In order to capture the VR users' quality-of-service (QoS) in SCNs, a novel VR model, based on multi-attribute utility theory, is proposed. This model jointly accounts for VR metrics, such as tracking accuracy, processing delay, and transmission delay. In this model, the small base stations (SBSs) act as the VR control centers that collect the tracking information from VR users over the cellular uplink. Once this information is collected, the SBSs will then send the 3-D images and accompanying audio to the VR users over the downlink. Therefore, the resource allocation problem in VR wireless networks must jointly consider both the uplink and downlink. This problem is then formulated as a noncooperative game and a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed to find the solution of this game. The proposed ESN algorithm enables the SBSs to predict the VR QoS of each SBS and is guaranteed to converge to mixed-strategy Nash equilibrium. The analytical result shows that each user's VR QoS jointly depends on both VR tracking accuracy and wireless resource allocation. Simulation results show that the proposed algorithm yields significant gains, in terms of VR QoS utility, that reach up to 22.2% and 37.5%, respectively, compared with Q-learning and a baseline proportional fair algorithm. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays for VR services.