TY - JOUR
T1 - A Cooperative Spectrum Sensing with Multi-Agent Reinforcement Learning Approach in Cognitive Radio Networks
AU - Gao, Ang
AU - Du, Chengyuan
AU - Ng, Soon Xin
AU - Liang, Wei
N1 - Publisher Copyright:
© 1997-2012 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Cognitive radio networks (CRNs) can greatly improve the temporal and spatial spectrum utilization by identifying and exploring spectrum holes of the licensed primary users (PUs). However, since the occupation of primary channels changes dynamically, a swift and accurate spectrum sensing is crucial especially in the multi-channel multi-secondary users (SUs) environment, where the number of channels is much larger than that of SUs. To improve the sensing accuracy, a cooperative sensing algorithm is proposed in this letter, where multiple SUs can share their spectrum detection results for a more effective spectrum holes search. This letter further employs multi-agent deep deterministic policy gradient (MADDPG) algorithm with the feature of centralized training and decentralized execution to reduce the synchronization and communication overhead caused by the sensing cooperation of SUs. The numerical simulation demonstrates that with the combination of cooperative sensing and multi-agent reinforcement learning, the proposed algorithm can greatly enhance the sensing accuracy in comparison to other non-cooperative learning or centralized learning approaches.
AB - Cognitive radio networks (CRNs) can greatly improve the temporal and spatial spectrum utilization by identifying and exploring spectrum holes of the licensed primary users (PUs). However, since the occupation of primary channels changes dynamically, a swift and accurate spectrum sensing is crucial especially in the multi-channel multi-secondary users (SUs) environment, where the number of channels is much larger than that of SUs. To improve the sensing accuracy, a cooperative sensing algorithm is proposed in this letter, where multiple SUs can share their spectrum detection results for a more effective spectrum holes search. This letter further employs multi-agent deep deterministic policy gradient (MADDPG) algorithm with the feature of centralized training and decentralized execution to reduce the synchronization and communication overhead caused by the sensing cooperation of SUs. The numerical simulation demonstrates that with the combination of cooperative sensing and multi-agent reinforcement learning, the proposed algorithm can greatly enhance the sensing accuracy in comparison to other non-cooperative learning or centralized learning approaches.
KW - Cognitive radio networks
KW - cooperative spectrum sensing
KW - deep reinforcement learning
KW - multi-agent deep deterministic policy gradient
UR - http://www.scopus.com/inward/record.url?scp=85105886061&partnerID=8YFLogxK
U2 - 10.1109/LCOMM.2021.3078442
DO - 10.1109/LCOMM.2021.3078442
M3 - 文章
AN - SCOPUS:85105886061
SN - 1089-7798
VL - 25
SP - 2604
EP - 2608
JO - IEEE Communications Letters
JF - IEEE Communications Letters
IS - 8
M1 - 9426930
ER -