Resource Management for Wireless Virtual Reality: Machine Learning Meets Multi-Attribute Utility
Resource Management for Wireless Virtual Reality: Machine Learning Meets Multi-Attribute Utility
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DOI：
10.1109/glocom.2017.8254650
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Mingzhe Chen;W. Saad;Changchuan Yin
中科院分区：
文献类型：
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作者：
Mingzhe Chen;W. Saad;Changchuan Yin
本文研究了基于小蜂窝网络（SCNs）的无线虚拟现实（VR）用户网络的资源管理问题。为了捕捉虚拟现实用户的服务质量（QoS），提出了一种基于多属性效用理论的虚拟现实模型。该模型综合考虑了跟踪精度、处理延迟和传输延迟等VR指标。在该模型中，小型基站（SBSs）作为VR控制中心，通过蜂窝上行链路收集VR用户的跟踪信息。一旦收集到这些信息，SBSs就会通过下行链路将三维图像和伴随的环绕立体声音频发送给VR用户。因此，VR无线网络中的资源分配问题必须同时考虑上行链路和下行链路。然后将该问题表述为一个非合作博弈，并提出了一种基于回声状态网络（ESNs）机器学习框架的分布式算法来求解该博弈。所提出的回声状态网络算法使各SBS能够预测各SBS的VR QoS，并保证收敛到混合策略纳什均衡。仿真结果表明，与Q-learning相比，本文提出的算法在VR QoS的总效用值方面取得了显著的提高，最高可达22%。结果表明，该算法具有比Q学习更快的收敛速度，能够保证虚拟现实业务的低延迟。
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), 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 three dimensional images and accompanying surround stereo 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 guarantees the convergence to a mixed-strategy Nash equilibrium. Simulation results show that the proposed algorithm yields significant gains, in terms of total utility value of VR QoS, that reach up to 22% compared to Q-learning. The results also show that the proposed algorithm has a faster convergence time than Q- learning and can guarantee low delays for VR services.