基于部分观测的无线上行网络信息年龄优化
Optimizing Age of Information in Wireless Uplink Networks with Partial Observations
摘要：
我们考虑由多个终端设备和接入点（AP）组成的无线上行链路网络。每个设备都监视一个物理过程，状态更新随机到达，并通过共享信道将这些更新发送到AP。AP旨在调度这些设备的传输，以优化由信息年龄（AoI）度量量化的网络范围的信息新鲜度。由于状态更新在设备处的随机到达，AP在做出调度决策时仅具有对设备处的最新状态更新的系统时间的部分观测。我们制定这样的决策问题作为一个信念马尔可夫决策过程（信念MDP）。原始形式的信念-MDP由于其状态的维数可以达到无穷大且其信念空间是不可数的而难以求解。通过利用状态更新到达的属性（即，Bernoulli）过程，我们设法将置信MDP的可行状态简化为二维向量。在此基础上，我们设计了一个低复杂度的调度策略。我们推导出的低复杂度的政策的AoI性能的上限和分析的性能保证，通过比较其性能与一个普遍的下限。数值结果验证了我们的分析。
英文摘要：
We consider a wireless uplink network consisting of multiple end devices and an access point (AP). Each device monitors a physical process with stochastic arrival of status updates and sends these updates to the AP over a shared channel. The AP aims to schedule the transmissions of these devices to optimize the network-wide information freshness, quantified by the Age of Information (AoI) metric. Due to the stochastic arrival of the status updates at the devices, the AP only has partial observations of system times of the latest status updates at the devices when making scheduling decisions. We formulate such a decision-making problem as a belief Markov Decision Process (belief-MDP). The belief-MDP in its original form is difficult to solve as the dimension of its states can go to infinity and its belief space is uncountable. By leveraging the properties of the status update arrival (i.e., Bernoulli) processes, we manage to simplify the feasible states of the belief-MDP to two-dimensional vectors. Built on that, we devise a low-complexity scheduling policy. We derive upper bounds for the AoI performance of the low-complexity policy and analyze the performance guarantee by comparing its performance with a universal lower bound. Numerical results validate our analyses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
