Multi-User Dynamic Spectrum Access Based on LRQ Deep Reinforcement Learning Network
Author:
Affiliation:
1. Chongqing University of Posts and Telecommunications(CQUPT),School of Communication and Information Engineering(SCIE),Chongqing,P. R. China,400-065
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10079232/10079243/10079537.pdf?arnumber=10079537
Reference16 articles.
1. Deep Reinforcement Learning Approach to QoE-Driven Resource Allocation for Spectrum Underlay in Cognitive Radio Networks
2. Distributed Q-learning based dynamic spectrum access in high capacity density cognitive cellular systems using secondary LTE spectrum sharing
3. Deep Reinforcement Learning for Multiobjective Optimization
4. Performance analysis of two-way relay cooperation underlay cognitive radio networks based on WIPT;xu;Journal on Communications,2019
5. Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach
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