Reinforcement Learning for Mixed Cooperative/Competitive Dynamic Spectrum Access
| Autor: | David J. Greene, Tyler Ward, John M. Shea, Caleb Bowyer, Marco Menendez, Tan F. Wong |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Artificial neural network Computer science 020206 networking & telecommunications 02 engineering and technology Admission control Missing data computer.software_genre Expert system 0202 electrical engineering electronic engineering information engineering State space Reinforcement learning 020201 artificial intelligence & image processing computer Selection (genetic algorithm) Communication channel |
| Zdroj: | DySPAN |
| DOI: | 10.1109/dyspan.2019.8935725 |
| Popis: | A dynamic spectrum sharing problem with a mixed collaborative/competitive objective and partial information about peers’ performances that arises from the DARPA Spectrum Collaboration Challenge is considered. Because of the very high complexity of the problem and the enormous size of the state space, it is broken down into the subproblems of channel selection, flow admission control, and transmission schedule assignment. The channel selection problem is the focus of this paper. A reinforcement learning algorithm based on a reduced state is developed to select channels, and a neural network is used as a function approximator to fill in missing values in the resulting input-action matrix. The performance is compared with that obtained by a hand-tuned expert system. |
| Databáze: | OpenAIRE |
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