BER analysis of MIMO ZF receiver with imperfect channel estimation under correlated Rician fading channel

Eduru, Supraja; Nakkeeran, R.

doi:10.1016/j.aeue.2020.153401

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…MMSE is a linear processing method that minimizes the mean square error between estimated and transmitted symbols, achieving enhanced signal-to-noise ratio and mitigating interference [4]. ZF is another linear method that nullifies interference by forcing the received signals to be orthogonal to interference, leading to increased data rates and improved MIMO system capacity [5].…”

Section: Introductionmentioning

confidence: 99%

The application of large-scale MIMO detection algorithm in 5G and beyond 5G

Tang

2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

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In recent years, the rapid advancement of 5G technology has brought to the forefront the pivotal role of Multiple-Input Multiple-Output (MIMO) system algorithms. This paper delves into a comprehensive exploration of two distinct algorithmic approaches within the context of 5G applications for massive MIMO systems. These two approaches are matrix transformation and machine learning, and the following paragraphs will shed light on their respective attributes and intricacies. Matrix transformation is a fundamental technique in MIMO systems, which aims to optimize the transmission of signals by manipulating the channel matrices. This method, while established and reliable, exhibits certain limitations in accommodating the dynamic and complex nature of 5G environments. On the other hand, machine learning algorithms, with their adaptability and capacity for self-improvement, have gained prominence in recent years. They offer a promising avenue for addressing the challenges presented by 5G MIMO systems, such as handling interference and optimizing resource allocation. In this paper, we provide concrete examples to analyze the strengths and weaknesses of both matrix transformation and machine learning in the context of 5G applications. Furthermore, we explore potential directions for the application of these algorithms and propose areas for improvement, with the ultimate goal of enhancing the efficiency and performance of massive MIMO systems in the evolving landscape of 5G technology.

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Section: Introductionmentioning

confidence: 99%