mlsanityData to 7 Oct 2026

Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks

arXiv 1801.01803 ↗First posted 5 Jan 2018cs.NI cs.IT math.ITcited by 22

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Abstract (arXiv)

We consider a wireless broadcast network with a base station sending time-sensitive information to a number of clients through unreliable channels. The Age of Information (AoI), namely the amount of time that elapsed since the most recently delivered packet was generated, captures the freshness of the information. We formulate a discrete-time decision problem to find a transmission scheduling policy that minimizes the expected weighted sum AoI of the clients in the network. We first show that in symmetric networks a Greedy policy, which transmits the packet with highest current age, is optimal. For general networks, we develop three low-complexity scheduling policies: a randomized policy, a Max-Weight policy and a Whittle's Index policy, and derive performance guarantees as a function of the network configuration. To the best of our knowledge, this is the first work to derive performance guarantees for scheduling policies that attempt to minimize AoI in wireless networks with unreliable channels. Numerical results show that both Max-Weight and Whittle's Index policies outperform the other scheduling policies in every configuration simulated, and achieve near optimal performance.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 2; 2023: 2; 2024: 2; 2025: 8; 2026: 4.

22 papers.

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  1. 2021Link Scheduling using Graph Neural Networkscited by 26
  2. 2021A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQcited by 8
  3. 2023Federated Learning While Providing Model as a Service: Joint Training and Inference Optimizationcited by 8
  4. 2021An Online Learning Approach to Optimizing Time-Varying Costs of AoIcited by 5
  5. 2024Timely Communications for Remote Inferencecited by 5
  6. 2021Learning Augmented Index Policy for Optimal Service Placement at the Network Edgecited by 4
  7. 2022How Does Data Freshness Affect Real-time Supervised Learning?cited by 3
  8. 2022A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networkscited by 3
  9. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  10. 2024Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Schedulingcited by 1
  11. 2025Pull-Based Query Scheduling for Goal-Oriented Semantic Communicationcited by 1
  12. 2025Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernelscited by 1
10 more
  1. 2025Multimodal Remote Inferencecited by 1
  2. 2026MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networkscited by 1
  3. 2023Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approachcited by 0
  4. 2025On the Regularity and Fairness of Combinatorial Multi-Armed Banditcited by 0
  5. 2025Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statisticscited by 0
  6. 2025Timely Information Updating for Mobile Devices Without and With ML Advicecited by 0
  7. 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learningcited by 0
  8. 2026Outcome-Fair Restless Multi-Armed Bandits for Stochastic Deadline Schedulingcited by 0
  9. 2026Real-Time Hard Peak Age-of-Information Safety with No-Regret Learningcited by 0
  10. 2026Age of Learning: Temporal Persistence of Prediction Errors as a Learning Signalcited by 0