# Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks

arXiv: https://arxiv.org/abs/1801.01803 · HTML: https://mlsanity.com/paper/1801.01803/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

## Built on by (most cited first, 22 of 22)

- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ](https://mlsanity.com/paper/2102.09774/) (2021)
- [Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization](https://mlsanity.com/paper/2312.12863/) (2023)
- [An Online Learning Approach to Optimizing Time-Varying Costs of AoI](https://mlsanity.com/paper/2105.13383/) (2021)
- [Timely Communications for Remote Inference](https://mlsanity.com/paper/2404.16281/) (2024)
- [Learning Augmented Index Policy for Optimal Service Placement at the Network Edge](https://mlsanity.com/paper/2101.03641/) (2021)
- [How Does Data Freshness Affect Real-time Supervised Learning?](https://mlsanity.com/paper/2208.06948/) (2022)
- [A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks](https://mlsanity.com/paper/2209.09206/) (2022)
- [Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning](https://mlsanity.com/paper/2501.01828/) (2025)
- [Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling](https://mlsanity.com/paper/2408.07205/) (2024)
- [MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks](https://mlsanity.com/paper/2608.01128/) (2026)
- [Pull-Based Query Scheduling for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2503.06725/) (2025)
- [Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernels](https://mlsanity.com/paper/2506.18186/) (2025)
- [Multimodal Remote Inference](https://mlsanity.com/paper/2508.07555/) (2025)
- [Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach](https://mlsanity.com/paper/2307.01366/) (2023)
- [On the Regularity and Fairness of Combinatorial Multi-Armed Bandit](https://mlsanity.com/paper/2509.12457/) (2025)
- [Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics](https://mlsanity.com/paper/2509.18654/) (2025)
- [Timely Information Updating for Mobile Devices Without and With ML Advice](https://mlsanity.com/paper/2512.17381/) (2025)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)
- [Outcome-Fair Restless Multi-Armed Bandits for Stochastic Deadline Scheduling](https://mlsanity.com/paper/2607.23772/) (2026)
- [Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning](https://mlsanity.com/paper/2607.27626/) (2026)
- [Age of Learning: Temporal Persistence of Prediction Errors as a Learning Signal](https://mlsanity.com/paper/2609.32593/) (2026)

## 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.