Open Access
ARTICLE
A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning
Azka Amin1, Xihua Liu2, Imran Khan3, Peerapong Uthansakul4, *, Masoud Forsat5, Seyed Sajad Mirjavadi5
1 School of Business, Qingdao University, Qingdao, 266061, China.
2 School of Economics, Qingdao University, Qingdao, 266061, China.
3 Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan.
4 School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
5 Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, Qatar.
* Corresponding Author: Peerapong Uthansakul. Email: .
Computers, Materials & Continua 2020, 65(2), 1487-1505. https://doi.org/10.32604/cmc.2020.011749
Received 27 May 2020; Accepted 16 June 2020; Issue published 20 August 2020
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.
Keywords
Cite This Article
A. Amin, X. Liu, I. Khan, P. Uthansakul, M. Forsat
et al., "A robust resource allocation scheme for device-to-device communications based on q-learning,"
Computers, Materials & Continua, vol. 65, no.2, pp. 1487–1505, 2020. https://doi.org/10.32604/cmc.2020.011749
Citations