Optimizing Information Freshness in Wireless Networks under General Interference Constraints
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Abstract (arXiv)
Age of information (AoI) is a recently proposed metric for measuring information freshness. AoI measures the time that elapsed since the last received update was generated. We consider the problem of minimizing average and peak AoI in a wireless networks, consisting of a set of source-destination links, under general interference constraints. When fresh information is always available for transmission, we show that a stationary scheduling policy is peak age optimal. We also prove that this policy achieves average age that is within a factor of two of the optimal average age. In the case where fresh information is not always available, and packet/information generation rate has to be controlled along with scheduling links for transmission, we prove an important separation principle: the optimal scheduling policy can be designed assuming fresh information, and independently, the packet generation rate control can be done by ignoring interference. Peak and average AoI for discrete time G/Ber/1 queue is analyzed for the first time, which may be of independent interest.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 2; 2026: 1.
5 papers.
- 2021An Online Learning Approach to Optimizing Time-Varying Costs of AoIcited by 6
- 2025Multimodal Remote Inferencecited by 1
- 2024Enhancing Information Freshness: An AoI Optimized Markov Decision Processcited by 0
- 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learningcited by 0
- 2026Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?cited by 0
- 2026Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?22 Jun 2026
- 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning24 Dec 2025
- 2025Multimodal Remote Inference11 Aug 2025
- 2024Enhancing Information Freshness: An AoI Optimized Markov Decision Process4 Sep 2024
- 2021An Online Learning Approach to Optimizing Time-Varying Costs of AoI27 May 2021