This paper considers utilizing the knowledge of age gains to reduce the network average age of information (AoI) in random access with event-driven periodic updating for the first time. Built on the form of slotted ALOHA, we require each device to determine its age gain threshold and transmission probability in an easily implementable decentralized manner, so that the unavoided contention can be limited to devices with age gains as high as possible. For the basic case that each device utilizes its knowledge of age gain of only itself, we provide an analytical modeling approach by a multi-layer discrete-time Markov chains (DTMCs), where an external infinite-horizon DTMC manages the jumps between the beginnings of frames and an internal finite-horizon DTMC manages the evolution during an arbitrary frame. Such modelling enables that optimal access parameters can be obtained offline. For the enhanced case that each device utilizes its knowledge of age gains of all the devices, we require each device to adjust its access parameters for maximizing the estimated network \textit{expected AoI reduction} (EAR) per slot, which captures the essential for improving the contribution of the throughput to the AoI performance. To estimate the network EAR, we require each device to use Bayes' rule to keep a posteriori joint probability distribution of local age and age gain of an arbitrary device based on the channel observations. Numerical results validate our theoretical analysis and demonstrate the advantage of the proposed schemes over the existing schemes in a wide range of network configurations.

翻译：本文首次考虑利用年龄增益知识来降低事件驱动周期性更新随机接入中的网络平均信息年龄。基于时隙ALOHA框架，我们要求每个设备以易于实现的分布式方式确定其年龄增益阈值和传输概率，从而将不可避免的冲突尽可能限制在年龄增益较高的设备之间。针对各设备仅利用自身年龄增益知识的基本场景，我们通过多层离散时间马尔可夫链建立解析模型：外部无限时域DTMC管理帧起始时刻的跳转，内部有限时域DTMC描述任意帧内的状态演化。该建模方法支持离线获取最优接入参数。在增强场景中，各设备利用所有设备的年龄增益知识，通过调整接入参数以最大化每时隙的网络期望年龄降低量估计值，该指标捕捉了吞吐量对AoI性能贡献的核心机制。为估计网络EAR，各设备基于信道观测结果，采用贝叶斯规则持续更新任意设备的本地年龄与年龄增益后验联合概率分布。数值结果验证了理论分析，并证明所提方案在广泛网络配置下优于现有方案。