Lagrange Index based Scheduling for Minimizing Age of Updates from Heterogeneous Sources

2026-04-20

AI Summary

This work addresses the problem of minimizing the weighted average Age of Information (AoI) in single-hop wireless uplink networks with heterogeneous sources under medium access constraints and non-preemptive transmission. The problem is formulated as a restless multi-armed bandit (RMAB) with semi-Markov decision process dynamics. Leveraging Lagrangian relaxation, the authors propose an efficient index-based heuristic scheduling policy that exploits structural properties of the optimal policy to accommodate heterogeneous update packets and non-preemptive transmissions. Numerical experiments demonstrate that the proposed method significantly outperforms existing approaches in non-preemptive scheduling scenarios, offering a practical and effective solution for AoI optimization in heterogeneous freshness-sensitive systems.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Multiagent Systems: Mechanism Design
• Machine Learning: Online Learning & Bandits

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Modern sensing systems generate heterogeneous updates ranging from small status packets to large data objects. We study a single-hop wireless uplink network where sensors generate updates at will, each consisting of a sensor dependent number of packets. Under a strict medium-access constraint and non-preemptive (no-switching) transmissions, decision stages become action-dependent and stochastic. We formulate the problem as a restless multi-armed bandit (RMAB) with semi-Markov decision process (SMDP) dynamics and develop a Lagrange index based heuristic for minimizing weighted average AoI cost. For the weighted AoI setting, we utilize the structural properties of the heuristic to enable efficient index computation. Numerical results demonstrate consistent performance gains over existing non-preemptive scheduling policies, providing a practical solution for heterogeneous freshness-aware systems.

Problem

• Age of Information
• Heterogeneous Sources
• Non-preemptive Scheduling
• Wireless Uplink Network
• Medium-access Constraint

Innovation

• Lagrange index
• Age of Information (AoI)
• restless multi-armed bandit
• heterogeneous sources
• non-preemptive scheduling