Q-learning based power control algorithm for D2D communication

2016 ◽  
Author(s):  
Shiwen Nie ◽  
Zhiqiang Fan ◽  
Ming Zhao ◽  
Xinyu Gu ◽  
Lin Zhang
Keyword(s):  
Power Control ◽  
Control Algorithm ◽  
D2d Communication ◽  
Q Learning ◽  
Power Control Algorithm

A Q-Learning-Based Downlink Power Control Algorithm for Energy Efficiency in LTE Femtocells

2014 ◽  
Vol 556-562 ◽  
pp. 1766-1769 ◽  
Author(s):  
Lian Fen Huang ◽  
Bin Wen ◽  
Zhi Bin Gao ◽  
Hong Xiang Cai ◽  
Yu Jie Li
Keyword(s):  
Energy Efficiency ◽  
Power Control ◽  
Control Algorithm ◽  
Interference Management ◽  
Smart Power ◽  
Q Learning ◽  
Downlink Power Control ◽  
Indoor Coverage ◽  
Power Control Algorithm ◽  
Management Issues

Femtocell is introduced to improve indoor coverage, which is beneficial for both users and operators. But it will also inevitably produce interference management issues in the heterogeneous network which consists of femtocells and macrocells. In this paper, a decentralized Q-learning-based power control strategy is proposed, comparing with homogenous power allocation and smart power control (SPC) algorithm. Simulation results have shown that Q-learning-based power control algorithm can implement the compromise of capacity between macrocells and femtocells, and greatly enhance energy efficiency of the whole network.

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