Beyond Freshness and Semantics: A Coupon-Collector Framework for Effective Status Updates

TL;DR

This paper introduces a coupon-collector framework for status updates over unreliable wireless channels, optimizing freshness and control effectiveness through a novel scheduling policy and learning algorithm.

Contribution

It formulates a new Markov decision process model, derives optimal scheduling policies, and develops a structure-aware Q-learning algorithm for resource-constrained status update systems.

Findings

SAQ matches optimal value iteration performance

SAQ converges faster than baseline Q-learning

Expiration-aware scheduling improves reward by up to 50%

Abstract

For status update systems operating over unreliable energy-constrained wireless channels, we address Weaver's long-standing Level-C question: do my packets actually improve the plant's behavior? Each fresh sample carries a stochastic expiration time -- governed by the plant's instability dynamics -- after which the information becomes useless for control. Casting the problem as a coupon-collector variant with expiring coupons, we (i) formulate a two-dimensional average-reward MDP, (ii) prove that the optimal schedule is doubly thresholded in the receiver's freshness timer and the sender's stored lifetime, (iii) derive a closed-form policy for deterministic lifetimes, and (iv) design a Structure-Aware Q-learning algorithm (SAQ) that learns the optimal policy without knowing the channel success probability or lifetime distribution. Simulations validate our theoretical predictions: SAQ matches optimal Value Iteration performance while converging significantly faster than baseline Q-learning, and expiration-aware scheduling achieves up to 50% higher reward than age-based baselines by adapting transmissions to state-dependent urgency -- thereby delivering Level-C effectiveness under tight resource constraints.