Link Adaptation Techniques for Massive MIMO Communication Systems

Huge MIMO communication structures are a promising technology for future Wi-Fi networks that can obtain significantly higher information costs and progress spectral efficiency in comparison to traditional structures. But, to completely take advantage of the ability of today's big MIMO, green and reliable hyperlink version strategies are required. Link model refers to the choice of trendy suitable transmission parameters, consisting of modulation and coding scheme, to evolve to changing channel conditions, a good way to optimize the machine's overall performance. In large MIMO structures, the massive number of trendy antennas at both the transmitter and receiver permits extra accurate channel estimation, which can be applied for greater superior hyperlink edition strategies. This technical summary provides a complete evaluation of the link model strategies for large MIMO systems. These techniques include preceding-based totally adaptive modulation and coding, adaptive beam forming, and closed-loop spatial multiplexing. The advantages and demanding situations of cutting-edge approaches are mentioned, and their overall performance is evaluated using theoretical analysis and simulation consequences. Furthermore, the impact cutting-edge large MIMO channel traits, along with correlation, fading, and interference, at the effectiveness of these strategies are likewise investigated. This research aims to offer insights into the potential modern-day hyperlink model for huge MIMO structures and guide the layout of cutting-edge green and reliable hyperlink model algorithms for destiny Wi-Fi networks.

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Published
2025-03-04
DOI
https://doi.org/10.1109/autocom64127.2025.10957563
Primary Topic
Advanced MIMO Systems Optimization
Type
article
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Link Adaptation Techniques for Massive MIMO Communication Systems

Prativa Das, Girija Shankar Sahoo, Divya Sharma, Lovish Dhingra et al.
Advanced MIMO Systems Optimization
article

Link Adaptation Techniques for Massive MIMO Communication Systems

Prativa Das, Girija Shankar Sahoo, Divya Sharma, Lovish Dhingra, Roopa Traisa, Veera Nagaiah Maddikayala
article en

Abstract

Huge MIMO communication structures are a promising technology for future Wi-Fi networks that can obtain significantly higher information costs and progress spectral efficiency in comparison to traditional structures. But, to completely take advantage of the ability of today's big MIMO, green and reliable hyperlink version strategies are required. Link model refers to the choice of trendy suitable transmission parameters, consisting of modulation and coding scheme, to evolve to changing channel conditions, a good way to optimize the machine's overall performance. In large MIMO structures, the massive number of trendy antennas at both the transmitter and receiver permits extra accurate channel estimation, which can be applied for greater superior hyperlink edition strategies. This technical summary provides a complete evaluation of the link model strategies for large MIMO systems. These techniques include preceding-based totally adaptive modulation and coding, adaptive beam forming, and closed-loop spatial multiplexing. The advantages and demanding situations of cutting-edge approaches are mentioned, and their overall performance is evaluated using theoretical analysis and simulation consequences. Furthermore, the impact cutting-edge large MIMO channel traits, along with correlation, fading, and interference, at the effectiveness of these strategies are likewise investigated. This research aims to offer insights into the potential modern-day hyperlink model for huge MIMO structures and guide the layout of cutting-edge green and reliable hyperlink model algorithms for destiny Wi-Fi networks.

Jain University (IN), Presidency University (IN), Siksha O Anusandhan University (IN), Maharishi University of Management and Technology (IN), Chitkara University (IN)
Openalex Percentile: Top 94%
Advanced MIMO Systems Optimization
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Link Adaptation Techniques for Massive MIMO Communication Systems — Prativa Das, Girija Shankar Sahoo, et al. · (2025) | TGRS Research Map | TGRS