A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

Achieving high spectral efficiency in realistic massive multiple-input multiple-output (M-MIMO) systems entail a significant increase in implementation complexity, especially with respect to data detection. Linear minimum mean-squared error (LMMSE) can achieve near-optimal performance but involves c...

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Vydané v:IEEE access Ročník 7; s. 39341 - 39351
Hlavní autori: Khoso, Imran A., Dai, Xiaoming, Irshad, M. Nauman, Khan, Ali, Wang, Xiyuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Acceleration
Algorithms
Antennas
Chebyshev acceleration
Chebyshev approximation
Complexity
Complexity theory
Convergence
Data detection
Detectors
Inversions
Iterative methods
linear minimum mean square error (LMMSE)
low-complexity
massive multiple-input multiple-output (MIMO)
MIMO (control systems)
MIMO communication
Parameter sensitivity
relaxation parameter
Richardson iteration
Robustness (mathematics)
ISSN:2169-3536, 2169-3536
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Shrnutí:Achieving high spectral efficiency in realistic massive multiple-input multiple-output (M-MIMO) systems entail a significant increase in implementation complexity, especially with respect to data detection. Linear minimum mean-squared error (LMMSE) can achieve near-optimal performance but involves computationally expensive large-scale matrix inversions. This paper proposes a novel computationally efficient data detection algorithm based on the modified Richardson method. We first propose an antenna-dependent approach for the robust initialization of the Richardson method. It is shown that the proposed initializer outperforms the existing initialization schemes by a large margin. Then, the Chebyshev acceleration technique is proposed to overcome the sensitivity of the Richardson method to relaxation parameter while simultaneously enhancing its convergence rate. We demonstrate that the proposed algorithm mitigates multiuser interference and offers significant performance gains via the iterative cancellation of the bias term by prior estimation. Hence, each step of the iteration routine gives a new and better estimate of the solution. An asymptotic expression for the average convergence rate is also derived in this paper. The numerical results show that the proposed algorithm outperforms the existing methods and achieves near-LMMSE performance with a significantly reduced computational complexity.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2907366