Affiliation:
1. Faculty of Electronics and Telecommunications, University of Engineering and Technology, Vietnam National University
of Science, Ha Noi, Vietnam
2. Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi,
Vietnam
Abstract
Abstract:
This paper defines, analyzes, and improves the performance of the worst-case user in ultradense
networks
Background:
In Ultra-Dense Networks (UDNs), where the Base Stations are distributed with a very
high density, the users are possibly near the cells’ intersection. These users are called the Worst-Case
Users (WCU) and usually experience very low performance.
Objectives:
Thus, improving the WCU performance is an urgent problem to secure the service requirement
of future cellular networks.
Methods:
In this paper, the performance of the WCU is analyzed in UDNs with a maximum power
algorithm and under the wireless environment with Stretched Path Loss model and Rayleigh fading.
To improve the WCU data rate, the Deep Q Networks with and without Multi-Input-Multi-output
(MIMO) are utilized in this paper.
Results:
The simulation results show that a system–based Deep Q Learning can dramatically improve
the WCU performance compared to the system with the maximum power algorithm. In addition, the
deployment of the MIMO technique in a system–based Deep Q-learning only has benefits in bad
channel conditions.
Conclusion:
In any channel condition, utilization of Deep Q Learning is a suitable solution to
improve the WCU performance. Furthermore, if the user experiences a good channel condition, the
MIMO technique can be used with Deep Q Learning to obtain further performance improvement.
Publisher
Bentham Science Publishers Ltd.
Subject
Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Computer Science Applications