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An Eigen-Based Approach for Enhancing Matrix Inversion Approximation in Massive MIMO Systems
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DOI：10.1109/TVT.2016.2622010.png)
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Abstract
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This correspondence presents a new matrix inversion approximation (MIA) method for massive multiple-input multiple-output systems (MIMO). In contrast to the existing methods that are mostly derived from the Neumann series expansion framework, additional coefficients have been introduced in our proposed method to enhance the precision of approximation. We propose an efficient algorithm for the coefficient design, which consists of an eigenvalue estimation procedure derived from random matrix theory, and a least-squares fitting procedure that solves a low-dimensional overdetermined system of linear equations. Complexity analysis and simulation results show that our eigen-based MIA method exhibits practically comparable computational complexity, while achieving substantial performance enhancement compared to other benchmark methods.
Keywords:
Marcenko-Pastur law
massive multiple-input multiple-output (MIMO)
matrix inversion
Neumann series (NS)
random matrix
zero-forcing (ZF)
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