Mitigating Interference in 5G Heterogeneous Networks Through Machine Learning

Chiapin Wang; Chen Hao Kao

doi:10.1109/ISPACS62486.2024.10867992

Mitigating Interference in 5G Heterogeneous Networks Through Machine Learning

Chiapin Wang^*
, Chen Hao Kao

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper proposes a machine learning-based algorithm using Reinforcement Learning (RL) for mitigating interference in heterogeneous 5G networks. Via an appropriate design of the reward function, both Macrocell and Femtocell users can access wireless resources more fairly, while maintaining a certain level of total user capacity. Experimental simulation results demonstrate the effectiveness of our approach maintaining user access fairness and improving overall system capacity.

Original language	English
Title of host publication	ISPACS 2024 - International Symposium on Intelligent Signal Processing and Communication Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Edition	2024
ISBN (Electronic)	9798350389210
DOIs	https://doi.org/10.1109/ISPACS62486.2024.10867992
Publication status	Published - 2024
Event	2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024 - Kaohsiung, Taiwan Duration: 2024 Dec 10 → 2024 Dec 13

Conference

Conference	2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024
Country/Territory	Taiwan
City	Kaohsiung
Period	2024/12/10 → 2024/12/13

Keywords

5G
Femtocell
Interference Mitigation
Machine Learning
Q-learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Signal Processing

Access to Document

10.1109/ISPACS62486.2024.10867992

Cite this

Wang, C & Kao, CH 2024, Mitigating Interference in 5G Heterogeneous Networks Through Machine Learning. in ISPACS 2024 - International Symposium on Intelligent Signal Processing and Communication Systems. 2024 edn, Institute of Electrical and Electronics Engineers Inc., 2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024, Kaohsiung, Taiwan, 2024/12/10. https://doi.org/10.1109/ISPACS62486.2024.10867992

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abstract = "This paper proposes a machine learning-based algorithm using Reinforcement Learning (RL) for mitigating interference in heterogeneous 5G networks. Via an appropriate design of the reward function, both Macrocell and Femtocell users can access wireless resources more fairly, while maintaining a certain level of total user capacity. Experimental simulation results demonstrate the effectiveness of our approach maintaining user access fairness and improving overall system capacity.",

keywords = "5G, Femtocell, Interference Mitigation, Machine Learning, Q-learning",

author = "Chiapin Wang and Kao, \{Chen Hao\}",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024 ; Conference date: 10-12-2024 Through 13-12-2024",

year = "2024",

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AB - This paper proposes a machine learning-based algorithm using Reinforcement Learning (RL) for mitigating interference in heterogeneous 5G networks. Via an appropriate design of the reward function, both Macrocell and Femtocell users can access wireless resources more fairly, while maintaining a certain level of total user capacity. Experimental simulation results demonstrate the effectiveness of our approach maintaining user access fairness and improving overall system capacity.

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KW - Femtocell

KW - Interference Mitigation

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KW - Q-learning

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AN - SCOPUS:105001487252

BT - ISPACS 2024 - International Symposium on Intelligent Signal Processing and Communication Systems

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T2 - 2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

Y2 - 10 December 2024 through 13 December 2024

ER -

Mitigating Interference in 5G Heterogeneous Networks Through Machine Learning

Abstract

Conference

Keywords

ASJC Scopus subject areas

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