Polynomial Expansion-Based MMSE Channel Estimation and Precoding for Massive MIMO-GFDM Systems

In this paper, low-complexity channel estimators and precoders are proposed for massive multiple-input multiple-output generalized frequency division multiplexing (MIMO-GFDM) systems. In order to combat the effect of non-orthogonality in GFDM, interference-free pilots are used in frequency-domain mi...

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Vydáno v:Wireless personal communications Ročník 128; číslo 1; s. 109 - 129
Hlavní autoři: Wang, Yanpeng, Fortier, Paul
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.01.2023
Springer Nature B.V
Témata:
Algorithms
Antennas
Communications Engineering
Complexity
Computer Communication Networks
Engineering
Estimators
Fourier transforms
Frequency division multiplexing
Lower bounds
Mean square errors
MIMO communication
Networks
Orthogonality
Pilots
Polynomials
Radiation
Receivers & amplifiers
Signal,Image and Speech Processing
Spectrum allocation
Wireless communications
GFDM
Precoding
Polynomial expansion
Channel estimation
MMSE
Massive MIMO
ISSN:0929-6212, 1572-834X
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Shrnutí:In this paper, low-complexity channel estimators and precoders are proposed for massive multiple-input multiple-output generalized frequency division multiplexing (MIMO-GFDM) systems. In order to combat the effect of non-orthogonality in GFDM, interference-free pilots are used in frequency-domain minimum mean square error (MMSE) channel estimation. Polynomial expansion is used to approximately compute the matrix inverses in the conventional MMSE estimation and precoding, consequently reducing the cubic computational complexity to square order. The degree of the matrix polynomial can be properly selected to get a required trade-off between complexity and estimation/precoding performance. Different weights can be assigned to the terms in the polynomial expansion and be optimized to achieve a minimal mean square error (MSE). Derived limits on the MSE of the proposed estimators can predict their performance in the high E s / N 0 region. Then, we derive a Cramér-Rao lower bound (CRLB) and use it as a benchmark for the estimators. In addition, the related computational complexity and the impacts of the polynomial degree are also investigated. Numerical results show the accuracy of the proposed channel estimators and precoders.
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ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-022-09943-0