论文标题
通过能量收集来源进行机会抽样的信息年龄最小化
Age-of-information minimization via opportunistic sampling by an energy harvesting source
论文作者
论文摘要
在此，考虑了在能量收集（EH）源设置中最小化时间平均信息（AOI）。 EH源在离散的时间瞬间进行了机会主义示例一个或多个过程，并将状态更新发送到无线褪色通道上的水槽节点。每次，EH节点都会决定是否探测链路质量，然后决定是否根据通道探针结果进行采样并进行交流。权衡是在接收器节点可用的信息的新鲜度与源节点的可用能量之间。我们使用无限的地平线马尔可夫决策过程（MDP）来制定两个方案的AOI最小化问题，其中能量到达和通道褪色过程为：（i）独立且相同分布（i.i.d.），（ii）Markovian。在I.I.D.设置频道探测后，最佳源采样策略被证明是阈值策略。同样，对于未知的频道状态和EH特征，为两阶段动作模型提出了Q学习算法的变体，该模型旨在学习最佳策略。对于马尔可夫系统，该问题再次被提出为MDP，并为未知动力学提供了学习算法。最后，数值结果证明了政策结构和绩效权衡。
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.
