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Research Trends in Wi-Fi Performance Improvement in Coexistence Networks with Machine Learning  

Kang, Young-myoung (성결대학교 컴퓨터공학과)
Publication Information
Journal of Platform Technology / v.10, no.3, 2022 , pp. 51-59 More about this Journal
Abstract
Machine learning, which has recently innovatively developed, has become an important technology that can solve various optimization problems. In this paper, we introduce the latest research papers that solve the problem of channel sharing in heterogeneous networks using machine learning, analyze the characteristics of mainstream approaches, and present a guide to future research directions. Existing studies have generally adopted Q-learning since it supports fast learning both on online and offline environment. On the contrary, conventional studies have either not considered various coexistence scenarios or lacked consideration for the location of machine learning controllers that can have a significant impact on network performance. One of the powerful ways to overcome these disadvantages is to selectively use a machine learning algorithm according to changes in network environment based on the logical network architecture for machine learning proposed by ITU.
Keywords
Coexistence; Wi-Fi; LTE-LAA; Performance; Machine Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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