mm-Dalga Kanallarındaki Yol Kaybı Modelleri Üzerine Kısa Bir Derleme

Öz

Yüksek bant genişliğine ve buna bağlı olarak yüksek hızlı veri iletişimine olanak tanıması nedeniyle milimetre dalga (mm-dalga) haberleşmesinin 5. Nesil (5G) haberleşme sistemlerinde kullanılması planlanmaktadır. Yol kaybı, mm-dalga haberleşmesinde sistem başarımını etkileyen en önemli faktörlerden biridir. Bu nedenle etkin ve güvenilir mm-dalga haberleşme sistemini oluşturmak, yüksek veri hızları elde etmek için yol kaybı dikkate alınmalıdır. mm-dalga haberleşme kanalının yayılım özelliklerinin ve yol kaybı modellerinin doğru bir biçimde belirlenmesi 5G sistemleri için oldukça önemlidir. 5G sistemlerininde, yol kaybını yüksek doğruluk ve hassasiyetle tahmin etmek için literatürde birçok yöntem önerilmiştir. Bu derleme çalışmasında araştırmacılara, 5G mm-dalga haberleşme sistemlerinde yol kaybı hakkında bilgi sağlamak hedeflenmiştir. 2018-2021 yılları arasında yapılmış, makine öğrenmesi, derin öğrenme, sinir ağları ve yayılım ölçümü yaklaşımına dayanan birçok çalışma sunulmuş, CI, ABG veya FI gibi temel yol kaybı modellerini, 5G’de üç boyutlu ışın izleme yöntemlerini inceleyen çalışmalar açık ve anlaşılır bir biçimde özetlenmiştir.

Anahtar Kelimeler

• mm-dalga
• 5. Nesil
• Yol kaybı modelleri
• Yayılım ölçümleri

A Brief Review of Path Loss Models for mmWave Channels

Abstract

It is planned to use millimeter wave (mm-wave) communication in 5th Generation (5G) communication systems, as it allows high bandwidth and accordingly high speed data communication. Path loss is one of the most important factors affecting system performance in mm-wave communication. Therefore, path loss must be taken into account in order to create an efficient and reliable mm-wave communication system and to obtain high data rates. It is very important for 5G systems to accurately determine the propagation characteristics and path loss models of the mm-wave communication channel. Many methods have been proposed in the literature to predict path loss with high accuracy and precision in 5G systems. In this review, it is aimed to provide researchers a clear knowledge about path loss in 5G mm-wave communication systems. Papers published between 2018-2021 which based on machine learning, deep learning, neural networks and propagation measurement approach were presented, and the main results of researches related to main path loss models Close-in (CI), and Alpha, Beta, Gamma (ABG) or Floating Intercept (FI) and papers that discussed 3-D ray tracing method were summarized in clear and precise manner.

Keywords

• mmWave
• 5G
• Path loss models
• Propagation measurements

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