@Article{cmc.2020.011749,
AUTHOR = {Azka Amin, Xihua Liu, Imran Khan, Peerapong Uthansakul, Masoud Forsat, Seyed Sajad Mirjavadi},
TITLE = {A Robust Resource Allocation Scheme for Device-to-Device  Communications Based on Q-Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1487--1505},
URL = {http://www.techscience.com/cmc/v65n2/39889},
ISSN = {1546-2226},
ABSTRACT = {One of the most effective technology for the 5G mobile communications is 
Device-to-device (D2D) communication which is also called terminal pass-through 
technology. It can directly communicate between devices under the control of a base 
station and does not require a base station to forward it. The advantages of applying D2D 
communication technology to cellular networks are: It can increase the communication 
system capacity, improve the system spectrum efficiency, increase the data transmission 
rate, and reduce the base station load. Aiming at the problem of co-channel interference 
between the D2D and cellular users, this paper proposes an efficient algorithm for 
resource allocation based on the idea of Q-learning, which creates multi-agent learners 
from multiple D2D users, and the system throughput is determined from the 
corresponding state-learning of the Q value list and the maximum Q action is obtained 
through dynamic power for control for D2D users. The mutual interference between the 
D2D users and base stations and exact channel state information is not required during 
the Q-learning process and symmetric data transmission mechanism is adopted. The 
proposed algorithm maximizes the system throughput by controlling the power of D2D 
users while guaranteeing the quality-of-service of the cellular users. Simulation results 
show that the proposed algorithm effectively improves system performance as compared 
with existing algorithms.},
DOI = {10.32604/cmc.2020.011749}
}