Channel Selection in Multi-channel Multi-user RF Energy Harvesting Cognitive Radio Networks

被引：0
|
作者：
Kumbhar, Jayant M. [1 ]
Kulkarni, Varada Potnis [1 ]
机构：
[1] Coll Engn Pune, Dept Elect & Telecommun, Pune, Maharashtra, India
来源：
2018 4TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT) | 2018年
关键词：
Cognitive Radio; Reinforcement Learning; Regret and Energy Harvesting;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Recently in wireless networks, spectrum scarcity and energy consumption are becoming important issues. Radio Frequency Energy Harvesting Cognitive Radio (RF-EH-CR) networks play a key role to solve this problem. Channel selection is an important aspect which affects the throughput of the CR network. In this paper, we compare the performance of DRQoSUCB and DRCA reinforcement learning algorithms for the network of energy harvesting cognitive radio nodes. The secondary nodes are non-cooperative and are not aware of each other's state. The EH-RCA policy is seen to be better than EH-RQUCB policy as number of user increases.
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页数：5
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