Energy cooperation in comp system based on Q-learning

This paper studies energy harvesting communication systems in which the base station sends data packets using the energy harvested from the surrounding natural environment. We assume that at each time slot only information about the current and past state of the base station is available, modeling t...

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Bibliographic Details
Main Authors: Lv, Yabo, Li, Baogang, Yao, Yuanbin, Guo, Dandan
Format: Conference Proceeding
Language:English
Subjects:
Aerospace electronics
Algorithm design and analysis
approximate linear dependence
Base stations
Batteries
coordinated multipoint
energy cooperation
Energy harvesting
Learning (artificial intelligence)
Q-learning
Transmitters
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Summary:This paper studies energy harvesting communication systems in which the base station sends data packets using the energy harvested from the surrounding natural environment. We assume that at each time slot only information about the current and past state of the base station is available, modeling the scenario as a Markov decision process and propose a reinforcement learning approach based on Q-learning for the transmitter to learn to cooperation trough energy sharing. For the problem of continuous state space in reinforcement learning, we propose an online algorithm based on approximate linear dependence to sparse sample pool. Furthermore, we use a neural network as approximator for the value function to improve the generalization ability. Numerical results show that our proposed algorithm can significantly improve the system performance even when the future environment change and energy harvested is uncertain for the BS.
ISSN:2163-5056
DOI:10.1109/ICASID.2017.8285750