Please use this identifier to cite or link to this item:
Title: Cellular base station downlink power allocation using model-free reinforcement learning
Authors: Liu, Hao
Keywords: Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 2020
Publisher: Nanyang Technological University
Abstract: With the maturity of 5G and IoT technologies, the number of base stations(BS) and end-user equipment(UE) is expected to increase dramatically. Therefore, the problem of an optimal solution for complex resource and power allocation in cellular networks has become a research topic. This is especially under the tremendous pressure of increasing demand. The issue that quality of service(QoS) is getting tougher to satisfy the user needs. Hence, in this dissertation, the author using reinforcement learning(RL) techniques to optimize the power allocation of Base station downlink, further improve the throughput and reduce the inter-cell interference is the topic of this dissertation. Our results examine two mainstream approaches in RL i.e. Q-learning and Deep Q Network(DQN) will be used to model the system. Since the DQN is a data-driven and model-free RL technique, it can be more consistent with the issue of BS power allocation.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
  Restricted Access
A dissertation submitted in partial fulfilment of The requirements for the degree of Master of science in communications engineering3.54 MBAdobe PDFView/Open

Page view(s)

Updated on Jan 29, 2023


Updated on Jan 29, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.