为增强现实服务设计低功耗资源分配方案，保障端到端延迟与可靠性。
Learn to Optimize Resource Allocation under QoS Constraint of AR
- 构建级联队列模型，推导端到端服务质量上限。
- 深度学习策略降低发射功率，满足服务质量约束。
- 适合研究移动边缘计算中AR应用的系统优化者。
本文研究交互式增强现实（AR）服务的上行和下行功率分配问题，其中AR设备捕获的实时视频上传至网络边缘，随后下载增强视频。将AR传输过程建模为级联队列系统，推导出关于端到端延迟与可靠性的概率服务质量（QoS）要求的上界。在QoS约束下的资源分配导致一个函数优化问题。为此，设计了一种深度神经网络来学习功率分配策略，利用最优功率分配结构提升学习性能。仿真结果表明，所提方法能在满足QoS要求的前提下有效降低发送功率。
原文摘要 · Abstract (English)
This paper studies the uplink and downlink power allocation for interactive augmented reality (AR) services, where the live video captured by an AR device is uploaded to the network edge, and then the augmented video is subsequently downloaded. By modeling the AR transmission process as a tandem queuing system, we derive an upper bound for the probabilistic quality of service (QoS) requirement concerning end-to-end latency and reliability. The resource allocation under the QoS requirement results in a functional optimization problem. To address it, we design a deep neural network to learn the power allocation policy, leveraging the optimal power allocation structure to enhance learning performance. Simulation results demonstrate that the proposed method effectively reduces transmit power while meeting the QoS requirement.
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