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

2019/01/24 by Elif Tuğçe Ceran, Ceran, Elif Tuğçe, Denız Gündüz +3 · 1 citation
Computer Science · Engineering · #Age of Information Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #IoT Networks and Protocols #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #Social and Information Networks (cs.SI) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1902.09467

openalex publication_date 2019/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The time average expected age of information (AoI) is studied for status\nupdates sent from an energy-harvesting transmitter with a finite-capacity\nbattery. The optimal scheduling policy is first studied under different\nfeedback mechanisms when the channel and energy harvesting statistics are\nknown. For the case of unknown environments, an average-cost reinforcement\nlearning algorithm is proposed that learns the system parameters and the status\nupdate policy in real time. The effectiveness of the proposed methods is\nverified through numerical results.\n

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