Strength of Augmented Responses in Linear Overloaded MIMO Detection

In this paper, the appropriate strength of augmented responses is investigated in linear multi-input multioutput (MIMO) detection. The augmented responses is used to generate a square channel matrix for applying linear MIMO detection schemes in overloaded MIMO systems. It is originally proven that the strength of augmented responses should be as small as possible. If gradient descent direction random walk MIMO detection is applied, the augmented responses limits the step size of the gradient descent algorithm. Thus, the strength of the augmented responses effect on the convergence rate of the gradient descent algorithm and bit error performance. Numerical results obtained through computer simulation show that the optimum values of the augmented responses exists.

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Publication Details

Published
2025-06-17
DOI
https://doi.org/10.1109/vtc2025-spring65109.2025.11174494
Primary Topic
Distributed Sensor Networks and Detection Algorithms
Type
article
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article

Strength of Augmented Responses in Linear Overloaded MIMO Detection

Yukitoshi Sanada
Distributed Sensor Networks and Detection Algorithms
article

Strength of Augmented Responses in Linear Overloaded MIMO Detection

Yukitoshi Sanada
article en

Abstract

In this paper, the appropriate strength of augmented responses is investigated in linear multi-input multioutput (MIMO) detection. The augmented responses is used to generate a square channel matrix for applying linear MIMO detection schemes in overloaded MIMO systems. It is originally proven that the strength of augmented responses should be as small as possible. If gradient descent direction random walk MIMO detection is applied, the augmented responses limits the step size of the gradient descent algorithm. Thus, the strength of the augmented responses effect on the convergence rate of the gradient descent algorithm and bit error performance. Numerical results obtained through computer simulation show that the optimum values of the augmented responses exists.

Keio University (JP)
Openalex Percentile: Top 69%
Distributed Sensor Networks and Detection Algorithms
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