Low-complexity MMSE detector based on refinement Gauss-Seidel method for massive MIMO systems

Negrete, Juan Carlos; Almeida, Celso de

doi:10.1109/latincom.2017.8240166

Cited by 7 publications

(13 citation statements)

References 16 publications

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“…The PGS precoder can achieve improved convergence rate by reducing N or using ordering scheme. From (20) and (21), the error between the final solution vectorx PGS and approxi- PGS can be obtained as follows,…”

Section: Convergence Ratementioning

confidence: 99%

An Efficient Modified Gauss Seidel Precoder for Downlink Massive MIMO Systems

Lee

Jang

et al. 2020

IEEE Access

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Recently, as the demand for tremendous spectral efficiency has increased, the massive multiple-input multiple-output (MIMO) system has attracted attention in the wireless communication system. In massive MIMO, the zero forcing (ZF) precoder provides optimal performance. However, the complexity for process of matrix inversion is burden in terms of practical implementation. Therefore, many researches for approximate inversion of channel matrix have been performed in order to reduce the complexity. The typical linear precoder based on approximate matrix inversion is the Gauss Seidel (GS) precoder. The GS precoder provides the similar precoded signals to ZF precoder with low complexity. However, the GS precoder does not adopt parallel implementation because of inner structure. Consequently, precoder for the GS iterative method spends a lot of times in order to estimate precoded signal. Therefore, this problem makes the GS precoder impractical. In this paper, the punctured GS (PGS) is proposed in order to mitigate the problem of parallel operation by modifying inner structure for the GS precoder. However, the performance for the PGS precoder is degraded due to modified inner structure. Therefore, the ordering PGS precoder which performance degradation due to modified inner structure is mitigated is additionally introduced. As a result, although the delay when precoded signal for the PGS precoder is obtained decreases than the GS precoder, the BER performance for the PGS precoder is degraded than the GS precoder. In contrast, the ordering PGS precoder provides improved BER performance with decrease of delay compared with the GS precoder.

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Section: Convergence Ratementioning

confidence: 99%

An Efficient Modified Gauss Seidel Precoder for Downlink Massive MIMO Systems

Lee

Jang

et al. 2020

IEEE Access

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“…Among various schemes, ref [12] proved that the Gauss-Seidel (GS) requires very low transmit power for obtaining target error performance compared to other schemes such as the Neumann series (NS), the Jacobi method, the Richardson method, and optimized coordinate descent. In [14][15][16][17][18][19][20][21][22][23][24][25], the pre-coding and detection schemes based on the GS were studied in the massive MIMO systems. The GS has high error performance using the iterative method and requires O K 2 multiplications since an algorithm requires only matrix-vector multiplications.…”

Section: Introductionmentioning

confidence: 99%

“…For reducing the complexity and error propagation of the GS, the authors in [15] proposed a reduced signal dimension using an interference cancellation and effective decoding scheme using the grey region in the uplink system. For accelerating the convergence rate of the GS, the authors in [16][17][18][19] proposed the band matrix and the stair matrix as an initial matrix, respectively, in the uplink system. For better error performance of the GS, the authors in [20][21][22][23] applied the soft-output detector in the uplink system.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, for efficient hardware implementation of the GS, the authors in [24] proposed the low-complexity inversion of the triangular matrix in the uplink system, and the authors in [25] proposed the parallel decoding scheme in the uplink system. According to several surveys, the common problems in [14][15][16][17][18][19][20][21][22][23][24][25] are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the error performance for the GS is degraded when the number of iterations is lower than î, such as î − 1, î − 2, • • • , 1, and it causes an error floor where the error performance is not improved despite infinite transmit power. However, all studies for the GS in [14][15][16][17][18][19][20][21][22][23][24][25] did not consider a prior estimation of the number of iterations and obtained the number of optimal iterations by measuring error performances with Monte-Carlo simulations. In addition to problems of the prior estimation, the authors in [14,[16][17][18][19][20][21][22][23][24][25] considered the massive MIMO systems which have high α values.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Estimation of the Number of Optimal Iterations for GS Pre-coding in Downlink Massive MIMO Systems

Lee

Hwang

et al. 2020

Applied Sciences

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This paper proposes an estimation scheme of the number iterations for optimal Gauss–Seidel (GS) pre-coding in the downlink massive multiple input multiple output (MIMO) systems for the first time. The number of iterations in GS pre-coding is one of the key parameters and should be estimated accurately prior to signal transmission in the downlink systems. For efficient estimation without presentations of the closed-form solution for the GS pre-coding symbols, the proposed estimation scheme uses the relative method which calculates the normalized Euclidean distance (NED) between consecutive GS solutions by using the property of the monotonic decrease function of the GS solutions. Additionally, an efficient initial solution for the GS pre-coding is proposed as a two term Neumann series (NS) based on the stair matrix for improving the accuracy of estimation and accelerating the convergence rate of the GS solution. The evaluated estimation performances verify high accuracy in the downlink massive MIMO systems even in low loading factors. In addition, an additional complexity for estimating the number of the optimal iterations is nearly negligible.

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