Deep Reinforcement Learning based Usage Aware Spectrum Access Scheme
Author:
Affiliation:
1. Ibaraki University,Graduate School of Science and Engineering,Ibaraki,Japan
2. Nanzan University,Faculty of Science and Technology,Nagoya,Japan
3. National Institute of Informatics,Information Systems Architecture Research Division,Tokyo,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9700404/9700405/09700468.pdf?arnumber=9700468
Reference13 articles.
1. Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
2. Human-level control through deep reinforcement learning;mnih;Nature,2015
3. IMPACT OF COGNITIVE RADIO;macaluso;June 2012,2012
4. Q-learning
5. On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance
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3. A Deep Reinforcement Learning based Analog Beamforming Approach in Downlink MISO Systems;2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring);2022-06
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