通过能量收集源的机会采样实现信息年龄最小化
Age-of-information minimization via opportunistic sampling by an energy harvesting source
AI总结：
本文研究能量收集源在无线衰落信道下的信息年龄最小化问题，利用无限时间MDP建模i.i.d.和马尔可夫两种场景，证明i.i.d.下最优采样为阈值策略，并针对未知动态提出Q-learning变体算法以学习最优策略。
摘要：
本文研究了在能量收集（EH）源设置下，时间平均信息年龄（AoI）的最小化问题。EH源在离散时间点上机会性地对一个或多个过程进行采样，并通过无线衰落信道将状态更新发送至汇聚节点。每次决策时，EH节点需决定是否探测链路质量，随后根据信道探测结果决定是否对过程进行采样并通信。此处的权衡在于汇聚节点可用信息的鲜度与源节点的可用能量之间。我们使用无限时间马尔可夫决策过程（MDP）来构建两种场景下的AoI最小化问题，这两种场景的能量到达和信道衰落过程分别为：（i）独立同分布（i.i.d.），（ii）马尔可夫性。在i.i.d.设置中，研究表明信道探测后的最优源采样策略为阈值策略。此外，针对未知的信道状态和EH特性，针对两阶段动作模型提出了一种Q-learning算法的变体，旨在学习最优策略。对于马尔可夫系统，该问题再次被构建为MDP，并针对未知动态提供了一种学习算法。最后，数值结果展示了策略结构和性能权衡。
英文摘要：
Herein, minimization of time-averaged age-of-information (AoI) in an energy harvesting (EH) source setting is considered. The EH source opportunistically samples one or multiple processes over discrete time instants and sends the status updates to a sink node over a wireless fading channel. Each time, the EH node decides whether to probe the link quality and then decides whether to sample a process and communicate based on the channel probe outcome. The trade-off is between the freshness of information available at the sink node and the available energy at the source node. We use infinite horizon Markov decision process (MDP) to formulate the AoI minimization problem for two scenarios where energy arrival and channel fading processes are: (i) independent and identically distributed (i.i.d.), (ii) Markovian. In i.i.d. setting, after channel probing, the optimal source sampling policy is shown to be a threshold policy. Also, for unknown channel state and EH characteristics, a variant of the Q-learning algorithm is proposed for the two-stage action model, that seeks to learn the optimal policy. For Markovian system, the problem is again formulated as an MDP, and a learning algorithm is provided for unknown dynamics. Finally, numerical results demonstrate the policy structures and performance trade-offs.
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。
