Minimizing the Age-of-Critical-Information: An Imitation Learning-Based Scheduling Approach Under Partial Observations

Xiaojie Wang, Zhaolong Ning, Song Guo, Miaowen Wen, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users' decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives.

Original languageEnglish (US)
Pages (from-to)3225-3238
Number of pages14
JournalIEEE Transactions on Mobile Computing
Issue number9
StatePublished - Sep 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Electrical and Electronic Engineering
  • Computer Networks and Communications


  • Age of information
  • critical levels
  • imitation learning
  • mobile edge networks
  • scheduling policy


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