# On Minimizing the Maximum Age-of-Information For Wireless Erasure Channels

## TL;DR

This paper derives an exact optimal scheduling policy to minimize the maximum Age-of-Information in wireless erasure channels, ensuring uniform freshness for delay-sensitive applications, and analyzes its theoretical properties and practical performance.

## Contribution

It provides the first exact optimal policy for minimizing maximum AoI in erasure channels, with a detailed theoretical analysis and a heuristic for peak-AoI with throughput constraints.

## Key findings

- Optimal policy is explicitly derived and proven to be optimal.
- The age process under the policy is positive recurrent with an exponential tail.
- Numerical simulations show the policy outperforms existing scheduling methods.

## Abstract

Age-of-Information (AoI) is a recently proposed metric for quantifying the freshness of information from the UE's perspective in a communication network. Recently, Kadota et al. [1] have proposed an index-type approximately optimal scheduling policy for minimizing the average-AoI metric for a downlink transmission problem. For delay-sensitive applications, including real-time control of a cyber-physical system, or scheduling URLLC traffic in 5G, it is essential to have a more stringent uniform control on AoI across all users. In this paper, we derive an exactly optimal scheduling policy for this problem in a downlink cellular system with erasure channels. Our proof of optimality involves an explicit solution to the associated average-cost Bellman Equation, which might be of independent theoretical interest. We also establish that the resulting Age-process is positive recurrent under the optimal policy, and has an exponentially light tail, with the optimal large-deviation exponent. Finally, motivated by typical applications in small-cell residential networks, we consider the problem of minimizing the peak-AoI with throughput constraints to specific UEs, and derive a heuristic policy for this problem. Extensive numerical simulations have been carried out to compare the efficacy of the proposed policies with other well-known scheduling policies, such as Randomized scheduling and Proportional Fair.