While the notion of age of information (AoI) has recently been proposed for analyzing ultra-reliable low-latency communications (URLLC), most of the existing works have focused on the average AoI measure. Designing a wireless network based on average AoI will fail to characterize the performance of URLLC systems, as it cannot account for extreme AoI events, occurring with very low probabilities. In contrast, this paper goes beyond the average AoI to improve URLLC in a vehicular communication network by characterizing and controlling the AoI tail distribution. In particular, the transmission power minimization problem is studied under stringent URLLC constraints in terms of probabilistic AoI for both deterministic and Markovian traffic arrivals. Accordingly, an efficient novel mapping between AoI and queue-related distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem considering both long and short packets transmissions. Simulation results show over a two-fold improvement, in shortening the AoI distribution tail, versus a baseline that models the maximum queue length distribution, in addition to a tradeoff between arrival rate and AoI.

中文翻译：

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车联网中超可靠低延迟通信的信息尾优化时代

虽然最近提出了信息年龄 (AoI) 的概念来分析超可靠低延迟通信 (URLLC)，但大多数现有工作都集中在平均 AoI 度量上。设计基于平均 AoI 的无线网络将无法表征 URLLC 系统的性能，因为它无法解释发生概率非常低的极端 AoI 事件。相比之下，本文通过表征和控制 AoI 尾部分布，超越了平均 AoI，以改进车载通信网络中的 URLLC。特别是，根据确定性和马尔可夫流量到达的概率 AoI，在严格的 URLLC 约束下研究了传输功率最小化问题。因此，提出了 AoI 和队列相关分布之间的有效新映射。随后，采用极值理论（EVT）和李雅普诺夫优化技术来制定和解决同时考虑长短包传输的问题。仿真结果表明，除了到达率和 AoI 之间的权衡之外，在缩短 AoI 分布尾部方面，与模拟最大队列长度分布的基线相比，还有两倍的改进。