TY - JOUR
T1 - Cooperative Communications with Relay Selection Based on Deep Reinforcement Learning in Wireless Sensor Networks
AU - Su, Yuhan
AU - Lu, Xiaozhen
AU - Zhao, Yifeng
AU - Huang, Lianfen
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2019/10/15
Y1 - 2019/10/15
N2 - Cooperative communication technology has become a research hotspot in wireless sensor networks (WSNs) in recent years, and will become one of the key technologies for improving spectrum utilization in wireless communication systems in the future. It leverages cooperation among multiple relay nodes in the wireless network to realize path transmission sharing, thereby improving the system throughput. In this paper, we model the process of cooperative communications with relay selection in WSNs as a Markov decision process and propose DQ-RSS, a deep-reinforcement-learning-based relay selection scheme, in WSNs. In DQ-RSS, a deep-Q-network (DQN) is trained according to the outage probability and mutual information, and the optimal relay is selected from a plurality of relay nodes without the need for a network model or prior data. More specifically, we use DQN to process high-dimensional state spaces and accelerate the learning rate. We compare DQ-RSS with the Q-learning-based relay selection scheme and evaluate the network performance on the basis of three aspects: outage probability, system capacity, and energy consumption. Simulation results indicate that DQ-RSS can achieve better performance on these elements and save the convergence time compared with existing schemes.
AB - Cooperative communication technology has become a research hotspot in wireless sensor networks (WSNs) in recent years, and will become one of the key technologies for improving spectrum utilization in wireless communication systems in the future. It leverages cooperation among multiple relay nodes in the wireless network to realize path transmission sharing, thereby improving the system throughput. In this paper, we model the process of cooperative communications with relay selection in WSNs as a Markov decision process and propose DQ-RSS, a deep-reinforcement-learning-based relay selection scheme, in WSNs. In DQ-RSS, a deep-Q-network (DQN) is trained according to the outage probability and mutual information, and the optimal relay is selected from a plurality of relay nodes without the need for a network model or prior data. More specifically, we use DQN to process high-dimensional state spaces and accelerate the learning rate. We compare DQ-RSS with the Q-learning-based relay selection scheme and evaluate the network performance on the basis of three aspects: outage probability, system capacity, and energy consumption. Simulation results indicate that DQ-RSS can achieve better performance on these elements and save the convergence time compared with existing schemes.
KW - Wireless sensor networks
KW - cooperative communications
KW - deep reinforcement learning
KW - outage probability
KW - relay selection
UR - http://www.scopus.com/inward/record.url?scp=85072534348&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072534348&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2019.2925719
DO - 10.1109/JSEN.2019.2925719
M3 - Article
AN - SCOPUS:85072534348
SN - 1530-437X
VL - 19
SP - 9561
EP - 9569
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 20
M1 - 8750861
ER -