Reinforcement Learning for Mixed Cooperative/Competitive Dynamic Spectrum Access

被引：11

作者：

Bowyer, Caleb ^{[1
]}

Greene, David ^{[1
]}

Ward, Tyler ^{[1
]}

Menendez, Marco ^{[1
]}

Shea, John ^{[1
]}

Wong, Tan ^{[1
]}

机构：

[1] Univ Florida, Gainesville, FL 32611 USA

来源：

2019 IEEE INTERNATIONAL SYMPOSIUM ON DYNAMIC SPECTRUM ACCESS NETWORKS (DYSPAN) | 2019年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/dyspan.2019.8935725

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：479 / 484

页数：6

共 4 条

[1] A Survey on Machine-Learning Techniques in Cognitive Radios [J].