Reinforcement Learning-Based Cooperative Spectrum Sensing

2019 ◽  
pp. 149-161
Author(s):  
Wenli Ning ◽  
Xiaoyan Huang ◽  
Fan Wu ◽  
Supeng Leng ◽  
Lixiang Ma
Keyword(s):  
Reinforcement Learning ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing

scholarly journals Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks

2020 ◽  
Vol 22 (1) ◽  
pp. 12-22
Author(s):  
Wenli Ning ◽  
Xiaoyan Huang ◽  
Kun Yang ◽  
Fan Wu ◽  
Supeng Leng
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks

scholarly journals Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1884
Author(s):  
Shuai Liu ◽  
Jing He ◽  
Jiayun Wu
Keyword(s):  
Reinforcement Learning ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Prior Information ◽  
Cooperative Spectrum Sensing ◽  
Spectrum Access ◽  
Spectrum Utilization ◽  
Channel Strategy ◽  
Large State Space ◽  
Network Utility

Dynamic spectrum access (DSA) has been considered as a promising technology to address spectrum scarcity and improve spectrum utilization. Normally, the channels are related to each other. Meanwhile, collisions will be inevitably caused by communicating between multiple PUs or multiple SUs in a real DSA environment. Considering these factors, the deep multi-user reinforcement learning (DMRL) is proposed by introducing the cooperative strategy into dueling deep Q network (DDQN). With no demand of prior information about the system dynamics, DDQN can efficiently learn the correlations between channels, and reduce the computational complexity in the large state space of the multi-user environment. To reduce the conflicts and further maximize the network utility, cooperative channel strategy is explored by utilizing the acknowledge (ACK) signals without exchanging spectrum information. In each time slot, each user selects a channel and transmits a packet with a certain probability. After sending, ACK signals are utilized to judge whether the transmission is successful or not. Compared with other popular models, the simulation results show that the proposed DMRL can achieve better performance on effectively enhancing spectrum utilization and reducing conflict rate in the dynamic cooperative spectrum sensing.

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