University of Oulu

A. Zakeri, M. Moltafet, M. Leinonen and M. Codreanu, "Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-based Scheduling," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2023.3278460.

Minimizing the AoI in resource-constrained multi-source relaying systems : dynamic and learning-based scheduling

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Author: Zakeri, Abolfazl1; Moltafet, Mohammad2; Leinonen, Markus1;
Organizations: 1Centre for Wireless Communications–Radio Technologies, University of Oulu, Oulu, Finland
2Department of Electrical and Computer Engineering, University of California Santa Cruz (UCSC), Santa Cruz, CA, USA
3Department of Science and Technology, Linköping University, Linköping, Sweden
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-06-08


We consider a multi-source relaying system where independent sources randomly generate status update packets which are sent to the destination with the aid of a relay through unreliable links. We develop transmission scheduling policies to minimize the weighted sum average age of information (AoI) subject to transmission capacity and long-run average resource constraints. We formulate a stochastic control optimization problem and solve it using a constrained Markov decision process (CMDP) approach and a drift-plus-penalty method. The CMDP problem is solved by transforming it into an MDP problem using the Lagrangian relaxation method. We theoretically analyze the structure of optimal policies for the MDP problem and subsequently propose a structure-aware algorithm that returns a practical near-optimal policy. Using the drift-plus-penalty method, we devise a near-optimal low-complexity policy that performs the scheduling decisions dynamically. We also develop a model-free deep reinforcement learning policy for which the Lyapunov optimization theory and a dueling double deep Q-network are employed. The complexities of the proposed policies are analyzed. Simulation results are provided to assess the performance of our policies and validate the theoretical results. The results show up to 91% performance improvement compared to a baseline policy.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: In press
DOI: 10.1109/TWC.2023.3278460
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research has been financially supported by the Academy of Finland (grant 323698), and 6G Flagship program (grant 346208). The work of M. Leinonen has also been financially supported in part by the Academy of Finland (grant 340171). The work of M. Codreanu has also been financially supported in part by the Swedish Research Council (grant 2022-03664).
Academy of Finland Grant Number: 323698
Detailed Information: 323698 (Academy of Finland Funding decision)
346208 (Academy of Finland Funding decision)
340171 (Academy of Finland Funding decision)
Copyright information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see