Joint Sampling and Transmission Policies for Minimizing Cost under AoI Constraints
Emmanouil Fountoulakis, Marian Codreanu, Anthony Ephremides, Nikolaos, Pappas

TL;DR
This paper addresses the challenge of minimizing combined sampling and transmission costs in wireless status update systems while satisfying average AoI constraints, proposing dynamic and randomized scheduling policies with proven near-optimality.
Contribution
It introduces three novel scheduling policies for joint sampling and transmission, including a dynamic optimal policy and two state-independent randomized policies, with theoretical performance guarantees.
Findings
The dynamic policy achieves near-optimal cost performance.
Randomized policies effectively balance AoI constraints and costs.
Allowing retransmission of old packets reduces overall costs.
Abstract
In this work, we consider the problem of jointly minimizing the average cost of sampling and transmitting status updates by users over a wireless channel subject to average Age of Information (AoI) constraints. Errors in the transmission may occur and a scheduling policy has to decide if the users sample a new packet or attempt for retransmission of the packet sampled previously. The cost consists of both sampling and transmission costs. The sampling of a new packet after a failure imposes an additional cost on the system. We formulate a stochastic optimization problem with the average cost in the objective under average AoI constraints. To solve this problem, we propose three scheduling policies; a) a dynamic policy, that is centralized and requires full knowledge of the state of the system, b) two stationary randomized policies that require no knowledge of the state of the system. We…
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Taxonomy
TopicsAge of Information Optimization · Congenital Heart Disease Studies · Health, Environment, Cognitive Aging
