Taming the Tail of Maximal Information Age in Wireless Industrial Networks

TL;DR

This paper proposes a novel resource allocation strategy for wireless industrial networks that minimizes power and delay while ensuring ultra-reliable, low-latency data transmission by modeling the maximal age of information using extreme value theory.

Contribution

It introduces a new approach combining EVT and Lyapunov optimization to effectively manage the maximal AoI in finite blocklength wireless transmissions.

Findings

Maximal AoI tail behavior can be effectively characterized using EVT.

Sensors can transmit larger data with longer blocks at lower power.

Faster AoI tail decay results in higher average age.

Abstract

In wireless industrial networks, the information of time-sensitive control systems needs to be transmitted in an ultra-reliable and low-latency manner. This letter studies the resource allocation problem in finite blocklength transmission, in which the information freshness is measured as the age of information (AoI) whose maximal AoI is characterized using extreme value theory (EVT). The considered system design is to minimize the sensors' transmit power and transmission blocklength subject to constraints on the maximal AoI's tail behavior. The studied problem is solved using Lyapunov stochastic optimization, and a dynamic reliability and age-aware policy for resource allocation and status updates is proposed. Simulation results validate the effectiveness of using EVT to characterize the maximal AoI. It is shown that sensors need to send larger-size data with longer transmission blocklength at lower transmit power. Moreover, the maximal AoI's tail decays faster at the expense of higher average information age.