Statistical Age of Information: A Risk-Aware Metric and Its Applications in Status Updates

TL;DR

This paper introduces Statistical AoI, a risk-aware metric for information freshness in wireless systems, addressing limitations of traditional AoI measures by incorporating risk sensitivity and optimizing update strategies.

Contribution

It proposes the Statistical AoI framework based on EVaR, analyzes optimal sampling and transmission schemes under risk constraints, and demonstrates improved performance for critical status update services.

Findings

Statistical AoI adapts from average to maximum peak AoI based on risk constraints.

Optimal sampling varies from step to constant functions with respect to channel gain.

Proposed schemes outperform baseline approaches in diverse risk-sensitive scenarios.

Abstract

Age of information (AoI) is an effective measure to quantify the information freshness in wireless status update systems. It has been further validated that the peak AoI has the potential to capture the core characteristics of the aging process, and thus the average peak AoI is widely used to evaluate the long-term performance of information freshness. However, the average peak AoI is a risk-insensitive metric and therefore may not be well suited for evaluating critical status update services. Motivated by this concern, and following the spirit of entropic value-at-risk (EVaR) in the field of risk analysis, in this paper we present a concept, termed Statistical AoI, for providing a unified framework to guarantee various requirements of risk-sensitive status-update services with the demand on the violation probability of the peak age. In particular, as the constraint on the violation probability of the peak age varies from loose to strict, the statistical AoI evolves from the average peak AoI to the maximum peak AoI. We then investigate the statistical AoI minimization problem for status updates over wireless fading channels. It is interesting to note that the corresponding optimal sampling scheme varies from step to constant functions of the channel power gain with the peak age violation probability from one to zero. We also address the maximum statistical AoI minimization problem for multi-status updates with time division multiple access (TDMA), where longer transmission time can improve reliability but may also cause the larger age. By solving this problem, we derive the optimal transmission time allocation scheme. Numerical results show that our proposals can better satisfy the diverse requirements of various risk-sensitive status update services, and demonstrate the great potential of improving information freshness compared to baseline approaches.