Hybrid automatic repeat request

Channel (broadcasting)

Energy (signal processing)

Scheduling (production processes)

Mathematics

Structural engineering

Reinforcement learning

Energy harvesting

Statistics

Mathematical optimization

Engineering

Artificial intelligence

Computer science

Transmitter

Reinforcement

Real-time computing

Transmission (telecommunications)

Telecommunications

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.

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