Average AoI Minimization for Energy Harvesting Relay-aided Status Update Network Using Deep Reinforcement Learning

A dual-hop status update system aided by energy-harvesting (EH) relays with finite data and energy buffers is studied in this work. To achieve timely status updates, the best relays should be selected to minimize the average age of information (AoI), which is a recently proposed metric to evaluate i...

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Bibliographic Details
Published in:IEEE wireless communications letters 2023-08, Vol.12 (8), p.1-1
Main Authors: Huang, Sin-Yu, Liu, Kuang-Hao
Format: Article
Language:English
Subjects:
Age of information
buffer-aided relaying
Buffers
Deep learning
Energy harvesting
Freshness
Internet of Things
Markov processes
Measurement
Minimization
Optimization
Reinforcement learning
Relay
relay selection
Relays
Sensors
status update
Three-dimensional displays
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Summary:A dual-hop status update system aided by energy-harvesting (EH) relays with finite data and energy buffers is studied in this work. To achieve timely status updates, the best relays should be selected to minimize the average age of information (AoI), which is a recently proposed metric to evaluate information freshness. The average AoI minimization can be formulated as a Markov decision process (MDP), but the state space for capturing channel and buffer evolution grows exponentially with the number of relays, leading to high solution complexity. We propose a relay selection (RS) scheme based on deep reinforcement learning (DRL) according to the instantaneous channel packet freshness and buffer information of each relay. Simulation results show a significant improvement of the proposed DRL-based RS scheme over state-of-art approaches.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2023.3278864