When Freshness Is Not Enough: Distribution-Aware Age of Information for Networked LQR Control
2026-06-03 • Multiagent Systems
Multiagent SystemsRobotics
AI summaryⓘ
The authors explore whether minimizing the average Age of Information (AoI) truly leads to the best control performance in systems where updates are sent over a network. They find that for certain simple systems, the control cost depends on more than just the average AoI — it also depends on how the times between updates are distributed and their statistical properties. This means two systems with the same average AoI can perform very differently. By analyzing real vehicle data, the authors confirm that focusing only on mean AoI is not enough for designing effective networked control systems.
Age of Information (AoI)Linear Time-Invariant (LTI) SystemsNetworked Control SystemsLinear Quadratic Regulator (LQR)Inter-scheduling IntervalsStatistical MomentsExponential MomentsDelayed UpdatesAutocorrelationNGSIM Dataset
Authors
Abdullah Y. Etcibasi, C. Emre Koksal, Eylem Ekici
Abstract
Age of Information (AoI) has become a central metric for the design of wireless update systems, especially in applications where fresh measurements support tracking, estimation, and control. Despite its popularity, the use of mean AoI or peak AoI as a surrogate for closed-loop performance is often motivated by intuition rather than by a control-theoretic derivation. This paper examines whether minimizing the mean AoI is in fact optimal for networked control systems. For scalar linear time-invariant systems with delayed intermittent updates, we show that, under state-independent scheduling policies, the infinite-horizon LQR tracking problem reduces to an optimization over the distribution of inter-scheduling intervals. The resulting objective depends on higher-order statistical moments, and in unstable or correlated regimes on exponential moments, of the inter-scheduling process rather than only on its mean. Consequently, policies with identical mean AoI can induce substantially different tracking costs. We further extend the analysis to disturbances with exponentially decaying autocorrelation and derive equivalent cost formulations that expose the role of the full interval distribution. Finally, we validate the theory using real vehicle trajectories from the NGSIM US-101 dataset. The empirical results match the predicted performance trends, demonstrating that mean AoI alone is insufficient for control-oriented network design.