A Grouping-based Scheduler for Efficient Channel Utilization under Age of Information Constraints

TL;DR

This paper introduces a grouping-based scheduler for large-scale status update systems with age of information constraints, significantly improving channel utilization through an innovative two-step grouping algorithm.

Contribution

It proposes a novel two-step grouping algorithm that optimally partitions sources based on AoI constraints to enhance channel efficiency in large-scale systems.

Findings

Channel usage is significantly reduced with the proposed TGA.

Performance is within 0.42% of the theoretical lower bound.

The method effectively handles sources with harmonic AoI constraints.

Abstract

We consider a status information updating system where a fusion center collects the status information from a large number of sources and each of them has its own age of information (AoI) constraints. A novel grouping-based scheduler is proposed to solve this complex large-scale problem by dividing the sources into different scheduling groups. The problem is then transformed into deriving the optimal grouping scheme. A two-step grouping algorithm (TGA) is proposed: 1) Given AoI constraints, we first identify the sources with harmonic AoI constraints, then design a fast grouping method and an optimal scheduler for these sources. Under harmonic AoI constraints, each constraint is divisible by the smallest one and the sum of reciprocals of the constraints with the same value is divisible by the reciprocal of the smallest one. 2) For the other sources without such a special property, we pack the sources which can be scheduled together with minimum update rates into the same group. Simulations show the channel usage of the proposed TGA is significantly reduced as compared to a recent work and is 0.42% larger than a derived lower bound when the number of sources is large.