Deep Q-Network Based Dynamic Spectrum Access for Cognitive Networks with Limited Spectrum Sensing Capability SUs-Reference-Cited by-同舟云学术

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Deep Q-Network Based Dynamic Spectrum Access for Cognitive Networks with Limited Spectrum Sensing Capability SUs

Published:2022-05-13 Issue: Volume: Page:
ISSN:
Container-title:2022 11th International Conference on Communications, Circuits and Systems (ICCCAS)
language:
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Author:

Miao Benjing¹,Pan Zhiwen¹,Wang Bin²,Zhang Yu³,Liu Nan¹

Affiliation:

1. Southeast University,Research Laboratory,National Mobile Communications,Nanjing,China

2. Science and Technology on Communication Networks Laboratory,Shijiazhuang,China

3. National University of Defense Technology,The Sixty-third Research Institute,Nanjing,China

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9823956/9823977/09824895.pdf?arnumber=9824895

Reference17 articles.

1. Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach

2. A New Distributed Dynamic Spectrum Access Model Based on DQN

3. Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks

4. A Novel Deep Q-learning Method for Dynamic Spectrum Access

5. Dropout: a simple way to prevent neural networks from overfitting;srivastava;Journal of Machine Learning Research,2014

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