S. S. Vilni, M. Moltafet, M. Leinonen and M. Codreanu, "Average AoI Minimization in an HARQ-based Status Update System under Random Arrivals," 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), BALI, Indonesia, 2022, pp. 160-165, doi: 10.1109/IoTaIS56727.2022.9975894
Average AoI minimization in an HARQ-based status update system under random arrivals
|Author:||Sadeghi Vilni, Saeid1; Moltafet, Mohammad1; Leinonen, Markus1;|
1Centre for Wireless Communications – Radio Technologies, University of Oulu, Finland
2Department of Science and Technology, Linköping University, Sweden
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023021026823
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-02-10
We consider a status update system consisting of one source, one butter-aided transmitter, and one receiver. The source randomly generates status update packets and the transmitter sends the packets to the receiver over an unreliable channel using a hybrid automatic repeat request (HARQ) protocol. The system holds two packets: one packet in the butter, which stores the last generated packet, and one packet currently under service in the transmitter. At each time slot, the transmitter decides whether to stay idle, transmit the last generated packet, or retransmit the packet currently under service. We aim to find the optimal actions at each slot to minimize the average age of information (AoI) of the source under a constraint on the average number of transmissions. We model the problem as a constrained Markov decision process (CMDP) problem and solve it for the known and unknown learning environment as follows. First, we use the Lagrangian approach to transform the CMDP problem to an MDP problem which is solved with the relative value iteration (RVI) for the known environment and with deep Q-learning (DQL) algorithm for the unknown environment. Second, we use the Lyapunov method to transform the CMDP problem to an MDP problem which is solved with DQL algorithm for the unknown environment. Simulation results assess the effectiveness of the proposed approaches.
IEEE International Conference on Internet of Things and Intelligence System
|Pages:||160 - 165|
2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)
IEEE International Conference on Internet of Things and Intelligence Systems
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research has been financially supported by the Infotech Oulu, the Academy of Finland (grant 323698), and Academy of Finland 6Genesis Flagship (grant 318927). The work of M. Leinonen has also been financially supported by the Academy of Finland (grant 340171).
|Academy of Finland Grant Number:||
323698 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
340171 (Academy of Finland Funding decision)
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