AI 中文总结
研究交互式AR服务的UL和DL资源联合优化，通过建模为串联排队系统得出QoS上限，转化为服务时间条件，制定加权发射功率最小化问题，用基于学习框架联合优化，设计基于GNN策略，仿真显示满足AR可靠性要求且降低发射功率。
AI 中文摘要
本文研究了用于交互式增强现实（AR）服务的上行链路（UL）和下行链路（DL）资源联合优化，其中AR设备捕获的实时视频上传到网络边缘，然后下载增强视频。通过将AR传输过程建模为串联排队系统，我们得出了关于端到端延迟和可靠性的概率服务质量（QoS）要求的上限。该上限将概率QoS要求转化为一个易于处理的服务时间条件，该条件共同表征了UL和DL服务过程。基于此条件，我们制定了一个加权UL-DL发射功率最小化问题，并提出了一个基于学习的框架来联合优化UL功率分配和DL波束成形。为了实现基于梯度的训练，我们进一步推导了服务时间条件的可微上限。此外，我们设计了基于GNN的UL功率分配和DL波束成形策略，其中UL GNN利用置换不变性（PE），DL GNN结合了PE和宽带DL波束成形的最优结构。仿真结果表明，与分别优化UL和DL资源的基线相比，该方法满足了AR可靠性要求并降低了加权发射功率。
英文摘要
This paper studies joint uplink (UL) and downlink (DL) resource optimization 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 derived bound transforms the probabilistic QoS requirement into a tractable service-time condition that jointly characterizes the UL and DL service processes. Based on this condition, we formulate a weighted UL-DL transmit-power minimization problem and propose a learning-based framework to jointly optimize UL power allocation and DL beamforming. To enable gradient-based training, we further derive a differentiable upper bound for the service-time condition. Moreover, we design GNN-based policies for UL power allocation and DL beamforming, where the UL GNN exploits permutation equivariance (PE) and the DL GNN incorporates both PE and the optimal structure of wideband DL beamforming. Simulation results show that the proposed method satisfies the AR reliability requirement and reduces the weighted transmit power compared with baselines that optimize UL and DL resources separately.
