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Journals / EMO Bilimsel Dergi / 2023 / Cilt: 13 Sayı: 2

Performance of Machine Learning-based Water-Filling Algorithm for Energy Efficient Data Transmission over Different Fading Channels in Wireless Networks

Kablosuz Ağlarda Farklı Gölgelenen Kanallar Üzerinden Enerji-Verimli Veri İletimi için Makine Öğrenmesi-temelli Su-Doldurma Algoritması

Ömer Melih GÜL
Pages
81–89
Publication Date
2023-07-19
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The numerical results demonstrate these online waterfilling algorithms shows close performance to offline waterfilling algorithms especially for longer time horizons.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, we tackle a resource allocation problem over multiple fading channels in wireless networks.
📊
Key Findings & Data:Differing from previous studies, the data transmission rate can take a value out of a discrete set of data transmission rates in this work.
💡
Conclusion & Impact:The numerical results demonstrate these online waterfilling algorithms shows close performance to offline waterfilling algorithms especially for longer time horizons.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Su-Doldurma · Makine Öğrenmesi · Çevrimiçi Politika

Abstract

In this paper, we tackle a resource allocation problem over multiple fading channels in wireless networks. Differing from previous studies, the data transmission rate can take a value out of a discrete set of data transmission rates in this work. We propose machine-learning-based online waterfilling algorithms for this problem. The relative performance of the online and optimal offline policies are evaluated for various types of fading channels (Rayleigh, Rician, Nakagami, Weibull) over various time horizons. The numerical results demonstrate these online waterfilling algorithms shows close performance to offline waterfilling algorithms especially for longer time horizons.

Özet

Bu çalışmada, kablosuz ağlarda çoklu gölgelenen kanallar üzerinden bir radio kaynak tahsisi problem ele alınmaktadır. Bu çalışmada önceki çalışmalardan farklı olarak, anlık veri iletim hızı sadece ayrık bir kümedeki veri iletim hızı değerlerinden birini alabilir. Bu probleme makine öğrenmesi-temelli çevrimiçi su doldurma algoritmaları önerilmiştir. Çevrimiçi ve eniyi çevrimdışı politikaların göreli performansı, çeşitli tiplerde (Rayleigh, Rician, Nakagami, Weibull) gölgelenen kanallar için çeşitli zaman ufuklarında değerlendirilmektedir. Sayısal sonuçlar, özellikle daha uzun zaman ufukları için, bu çevrimiçi su doldurma algoritmalarının çevrimdışı su doldurma algoritmalarına yakın performansı olduğunu göstermektedir.

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Cite This Article

GÜL, Ö. M. (2023). Performance of Machine Learning-based Water-Filling Algorithm for Energy Efficient Data Transmission over Different Fading Channels in Wireless Networks. EMO Bilimsel Dergi, Cilt: 13 Sayı: 2, 81–89. https://ajindex.com/articles/955266

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