Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost

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2019-01-01

Author

Ceran Arslan, Elif Tuğçe
Gunduz, Deniz
Gyorgy, Andras

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The time average expected age of information (AoI) is studied for status updates sent from an energy-harvesting transmitter with a finite-capacity battery. The optimal scheduling policy is first studied under different feedback mechanisms when the channel and energy harvesting statistics are known. For the case of unknown environments, an average-cost reinforcement learning algorithm is proposed that learns the system parameters and the status update policy in real time. The effectiveness of the proposed methods is verified through numerical results.

URI

https://hdl.handle.net/11511/92626

DOI

https://doi.org/10.1109/infcomw.2019.8845182

Conference Name

IEEE Conference on Computer Communications (IEEE INFOCOM)

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. T. Ceran Arslan, D. Gunduz, and A. Gyorgy, “Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost,” presented at the IEEE Conference on Computer Communications (IEEE INFOCOM), Paris, Fransa, 2019, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/92626.