Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2022 / Cilt: 11 - Sayı: 4

Array gain analyses of MIMO systems in 5G communication systems

5G iletişim sistemlerinde ÇGÇÇ sistemlerin dizin kazanç analizleri

Pages
888–897
Publication Date
2022-10-21
DOI
—
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AI Summary & Key Takeaways

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

The results show that theoretical array gains can be achieved in end-to-end 5G NR downlink channels.

📌 3 Key Takeaways

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Objective & Hypothesis:In the last decade, Multiple-Input Multiple-Output (MIMO) algorithms have been developed for mobile communication networks in order to increase spectrum efficiency and reduce transmitted power, which are also two of the main Key Performance Indicators of Fifth Generation New Radio (5G NR).
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Key Findings & Data:Therefore, various MIMO algorithms are being researched for their adaptability to 5G NR specifications.
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Conclusion & Impact:The results show that theoretical array gains can be achieved in end-to-end 5G NR downlink channels.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Dizin kazanç · ÇGÇÇ · TGÇÇ

Abstract

In the last decade, Multiple-Input Multiple-Output (MIMO) algorithms have been developed for mobile communication networks in order to increase spectrum efficiency and reduce transmitted power, which are also two of the main Key Performance Indicators of Fifth Generation New Radio (5G NR). Therefore, various MIMO algorithms are being researched for their adaptability to 5G NR specifications. The objective of this study is to examine the array gains achieved with the deployment of multiple transmit antennas and multiple receive antennas in 5G NR Physical Downlink Shared Channel (PDSCH). The study first examines the array gains of Single-Input Multiple-Output (SIMO) and Multiple-Input Single-Output (MISO), then combines the transmitter and the receiver diversities in a MIMO system for 5G PDSCH. The array gains are achieved through precoding and combining vectors obtained by Singular Value Decomposition (SVD) of the channel coefficients matrix. The results show that theoretical array gains can be achieved in end-to-end 5G NR downlink channels.

Özet

Son on yılda, mobil iletişim ağlarında spektrum verimliliğini artırmak ve kullanılan iletim gücünü azaltmak için Çoklu Giriş Çoklu Çıkış (ÇGÇÇ) algoritmaları geliştirilmiştir. Spektrum verimliliğini artırmak ve kullanılan iletim gücünü azaltmak Beşinci Nesil Yeni Radyonun (5G YR) da Temel Performans Göstergelerinden olduğundan MIMO algoritmalarının 5G YR spesifikasyonlarına uygunluğu araştırılmaktadır. Bu çalışmanın amacı, 5G YR Fiziksel Aşağı Yönlü Bağlantı Paylaşımlı Kanalında (FAYBPK) çoklu verici antenler ve çoklu alıcı antenler kullanılarak elde edilen dizin kazançlarını incelemektir. Çalışma, Tekli Giriş Çoklu Çıkış (TGÇÇ) ve Çoklu Giriş Tekli Çıkış (ÇGTÇ) dizin kazanımlarını inceler, ardından 5G YR FAYBPK ÇGÇÇ sisteminde verici ve alıcı çeşitliliklerini birleştirir. Dizin kazanımları, Tekil Değer Ayrıştırma (TDA) ile elde edilen ön kodlama ve birleştirme vektörlerini kullanılarak elde edilir. Çalışma sonuçlara göre teorik dizi kazanımları baştan-uca 5G YR aşağı bağlantı kanallarında elde edilebilmektedir.

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Cite This Article

MUTLU, U., KABALCI, Y. (2022). Array gain analyses of MIMO systems in 5G communication systems. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 11 - Sayı: 4, 888–897. https://ajindex.com/articles/584398

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References (6)

  1. H. Kim, Design and Optimization for 5G Wireless Communications. Wiley-IEEE Press, 2020.
  2. A. Goldsmith, Wireless Communications. Cambridge: Cambridge University Press, 2005.
  3. D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press, 2005. IEEE SA, “IEEE 802.11n-2009.” https://standards.ie ee.org/ieee/802.11n/3952/. Accessed March 2022 IEEE SA, “IEEE 802.11ac-2013.” https://standards.ie ee.org/ieee/802.11ac/4473. Accessed March 2022 IEEE, “IEEE 802.16-2017.” https://standards.ieee.org/ ieee/802.16/6996/. Accessed March 2022 3GPP, “Release 14.” https://www.3gpp.org/release-14. Accessed March 2022 3GPP, “Release 15.” https://www.3gpp.org/release-15. Accessed March 2022 3GPP, “Release 7.” https://www.3gpp.org/specificatio ns/releases/73-release-7. Accessed March 2022 Y. Kabalci, 5G Mobile Communication Systems: Fundamentals, Challenges, and Key Technologies, Smart Grids and Their Communication Systems, Springer (2019), pp. 329-359
  4. A. Zaidi, F. Athley, J. Medbo, X. Chen, and G. Durisi, 5G physical layer: Principles, models and technology components, Academic Press, 2018.
  5. U. Mutlu, Y. Kabalci, Effects of Antenna Array on Throughput in 5G NR PDSCH, III. International Turkic World Congress on Science and Engineering (TURK-COSE), pp. 98-108, 2021
  6. E. Telatar, Capacity of multi-antenna Gaussian channels, Eur. Trans. Telecommun., vol. 10, no. 6, pp. 585–595, 1999, doi: 10.1002/ett.4460100604. M. A. Albreem, A. H. Al Habbash, A. M. Abu-Hudrouss, and S. S. Ikki, Overview of Precoding Techniques for Massive MIMO, IEEE Access, vol. 9, pp. 60764–60801, 2021, doi: 10.1109/ACCESS 2021. 3073325. T. Kebede, Y. Wondie, J. Steinbrunn, H. B. Kassa, and K. T. Kornegay, Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment, IEEE Access, vol. 10, pp. 16365–16387, 2022, doi: 10.1109/ACCESS 2022.314 9301. R. M. Asif, J. Arshad, M. Shakir, S. M. Noman, and A. U. Rehman, Energy Efficiency Augmentation in Massive MIMO Systems through Linear Precoding Schemes and Power Consumption Modeling, Wirel. Commun. Mob. Comput., vol. 2020, pp. 1–13, Sep. 2020, doi: 10.1155/2020/8839088. D. Kadhim, Promising Gains of 5G Networks with Enhancing Energy Efficiency Using Improved Linear Precoding Schemes, Int. J. Intell. Eng. Syst., vol. 14, no. 3, pp. 139–149, Jun. 2021, doi: 10.22266/ijies2021.0630.13. J. Kaur, O. R. Popoola, M. Ali Imran, Q. H. Abbasi, and H. T. Abbas, Improving Throughput For Mobile Receivers Using Adaptive Beamforming