Dynamic resource allocations based on Q-learning for D2D communication in cellular networks

Author

Yong Luo ; Zhiping Shi ; Xin Zhou ; Qiaoyan Liu ; Qicong Yi

Author_Institution

Nat. Key Lab. of Sci. & Technol. on Commun., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2014

Firstpage

385

Lastpage

388

Abstract

In order to solve the problem of spectrum and power allocation for D2D communication in cellular networks when the prior knowledge is not available, a method based on machine learning is proposed in this paper. Q-learning, which is one of the most important algorithms in machine learning, is proposed to solve the radio resource management in underlay mode to find the optimal strategy in time series. In this mode, Q-learning is used to finish the channel assignment and the power allocation at the same time. And the simulation results show that greater system capacity can be achieved through the method proposed in this paper.

Keywords

cellular radio; learning (artificial intelligence); resource allocation; telecommunication computing; telecommunication network management; D2D communication; Q-learning; cellular networks; channel assignment; communication power allocation; communication spectrum; device-to-device communication; dynamic resource allocation; machine learning; radio resource management; time series; Channel allocation; Equations; Interference; Learning (artificial intelligence); Resource management; Simulation; Time series analysis; D2D; Q-Learning; resource allocation;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2014 11th International Computer Conference on

Print_ISBN

978-1-4799-7207-4

Type

conf

DOI

10.1109/ICCWAMTIP.2014.7073432

Filename

7073432