论文标题
用相关来源优化信息时代
Optimizing Age of Information with Correlated Sources
论文作者
论文摘要
我们开发了一个简单的模型，以及时监视无线网络对相关源的监视。使用此模型，我们研究了如何在存在相关性的情况下优化加权和平均信息年龄（AOI）。首先，我们讨论如何找到最佳的固定随机策略，并表明它们是一般两倍远离最佳策略。然后，我们制定了一种基于Lyapunov漂移的最大重量策略，该政策在实践中的性能比随机策略的表现更好，并表明它也是远离最佳的两倍。接下来，我们得出缩放结果，该结果表明在存在相关性的情况下，大型网络中的AOI如何改善。我们还表明，对于固定的随机策略，平均AOI的表达对于对相关结构进行建模的方式很强。最后，对于相关参数未知且随时间变化的设置，我们制定了一种启发式策略，该策略通过以在线方式学习相关参数来适应其计划决策。我们还提供数值模拟以支持我们的理论结果。
We develop a simple model for the timely monitoring of correlated sources over a wireless network. Using this model, we study how to optimize weighted-sum average Age of Information (AoI) in the presence of correlation. First, we discuss how to find optimal stationary randomized policies and show that they are at-most a factor of two away from optimal policies in general. Then, we develop a Lyapunov drift-based max-weight policy that performs better than randomized policies in practice and show that it is also at-most a factor of two away from optimal. Next, we derive scaling results that show how AoI improves in large networks in the presence of correlation. We also show that for stationary randomized policies, the expression for average AoI is robust to the way in which the correlation structure is modeled. Finally, for the setting where correlation parameters are unknown and time-varying, we develop a heuristic policy that adapts its scheduling decisions by learning the correlation parameters in an online manner. We also provide numerical simulations to support our theoretical results.
