Farklı Gölgelenen Kanallar Üzerinden Enerji-Verimli Veri İletimi için Geçmiş-temelli Su-Doldurma Algoritması

Year 2022, , 118 - 125, 30.11.2022

Omer Melih Gul

https://doi.org/10.31590/ejosat.1112389

Abstract

Bu çalışmada, kablosuz ağlarda çoklu gölgelenen kanallar üzerinden bir radio kaynak tahsisi problem ele alınmaktadır. Bu problem iki yönlü incelenmektedir. İlk olarak, problemi tüm gölgelenen kanalları düşünerek ele alınmaktadır. Çevrimdışı su-doldurma algoritmasını düşünerek bu problemin en iyi çözümünü sunulmuştur. Daha sonra bu probleme geçmiş-temelli çevrimiçi su doldurma algoritmaları önerilmiştir. Bu çevrimiçi algoritma, geçmişin bir kısmına bağlı bir su-doldurma seviyesine karar vermek amacıyla geçmişi kısmı olarak kullanmaktadır. Daha sonra, bu çevrimiçi algoritma problemin zaman ufkunda veri iletmek için bu geçmiş-temelli su-doldurma seviyesini uygulamaktadır. Çevrimiçi ve 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 ve daha uzun geçmişin daha uzun kısımlarını kullanıldığında, bu çevrimiçi su doldurma algoritmalarının çevrimdışı su doldurma algoritmalarına yakın performansı olduğunu göstermektedir.

Keywords

Su-Doldurma, Takviyeli Öğrenme, Çoklu Erişim Haberleşmesi, Çevrimiçi Politika

Performance of History-based Water-Filling Algorithm for Energy-Efficient Data Transmission over Different Fading Channels

Abstract

In this paper, we tackle a resource allocation problem over multiple fading channels in wireless networks. This problem is investigated in two ways. First, we consider the problem over the whole multiple fading channels altogether with no power constraint. We look for an optimal solution for this problem by considering an offline waterfillling algorithm. Then, we also propose history-based online waterfilling algorithms for this problem. This online algorithm uses the history partially in order to determine a waterfilling level based on that part of history. Then, the online policy applies this history-based determined waterfilling level to transmit data over the time horizon of the problem. The relative performance of the online and offline policies is 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 and by using larger portions of history.

Keywords

Water-Filling, Reinforcement Learning, Multi-access Communications, Online Policy

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