Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning

Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning

This paper studies the problem of joint power allocation and user association in wireless heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based approach. This is a challenging problem since the action space is hybrid, consisting of continuous actions (power allocation) and...

Full description

Bibliographic Details
Main Authors: Chi-Kai Hsieh, Kun-Lin Chan, Feng-Tsun Chien
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Applied Sciences
Subjects:
energy efficiency
power allocation
user clustering
reinforcement learning
Online Access:https://www.mdpi.com/2076-3417/11/9/4135

Internet

https://www.mdpi.com/2076-3417/11/9/4135

Similar Items