@article{MAD3CC4F7,
title = "A Study on Interference Management and Resource Allocation Optimization Using Reinforcement Learning",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2024",
issn = "1226-4717",
doi = "10.7840/kics.2024.49.10.1368",
author = "Geon-a Park, Kae-won Choi",
keywords = "Wireless Communication, Resource Allocation, Reinforcement Learning, PPO Algorithm",
abstract = "In the wireless communication environment, the introduction of Ultra-Dense Networks (UDN) is essential to meet the increasing demands of users. UDNs are characterized by a high density of base stations, which enhance network capacity and improve signal strength and reliability, thereby improving service quality.
However, such densification leads to inter-base station interference that adversely affects network performance.
Consequently, effective interference management is crucial for optimizing resource allocation. This paper proposes the application of the Proximal Policy Optimization (PPO) algorithm to address interference management and resource allocation optimization challenges by simulating real network environments. The proposed method demonstrates the potential to enhance the overall efficiency and performance of wireless communication systems through resource allocation optimization that considers channel conditions and network fairness. This research is expected to provide practical solutions for resource allocation and interference management strategies in UDN environments, contributing to the enhancement of wireless communication system performance."
}