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

Autor: Elif Tugce Ceran, András György, Deniz Gunduz
Rok vydání: 2019
Předmět:
Battery (electricity)
Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer science
Computer Science - Information Theory
cs.NI
Real-time computing
Hybrid automatic repeat request
02 engineering and technology
Computer Science - Networking and Internet Architecture
cs.IT
0202 electrical engineering
electronic engineering
information engineering
FOS: Electrical engineering
electronic engineering
information engineering
Reinforcement learning
0601 history and archaeology
math.IT
Electrical Engineering and Systems Science - Signal Processing
Networking and Internet Architecture (cs.NI)
Social and Information Networks (cs.SI)
Information Age
060102 archaeology
Information Theory (cs.IT)
Transmitter
eess.SP
020206 networking & telecommunications
Computer Science - Social and Information Networks
06 humanities and the arts
Optimal scheduling
cs.SI
Energy harvesting
Communication channel
Zdroj: INFOCOM Workshops
DOI: 10.48550/arxiv.1902.09467
Popis: 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.
Databáze: OpenAIRE
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