Low-Complexity Precoding Design for Massive Multiuser MIMO Systems Using Approximate Message Passing

Chen, Jung-Chieh; Wang, Chang-Jen; Wong, Kai-Kit; Wen, Chao-Kai

doi:10.1109/tvt.2015.2457450

Cited by 38 publications

(38 citation statements)

References 15 publications

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“…So far most existing PAPR reduction methods (e.g. [15]- [17]) for large-scale MU MIMO-OFDM systems reduce the PAPR through directly devising the precoded signals {w n }.…”

Section: B Peak-to-average Power Ratio (Papr)mentioning

confidence: 99%

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An ADMM Approach for PAPR Reduction for Large-Scale MIMO-OFDM Systems

Bao

Fang

Wan

et al. 2018

IEEE Trans. Veh. Technol.

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We consider the problem of peak-to-average power ratio (PAPR) reduction for orthogonal frequency-division multiplexing (OFDM) based large-scale multiple-input multipleoutput (MIMO) systems. A novel perturbation-assisted scheme is developed to reduce the PAPRs of the transmitted signals by exploiting the redundant degrees-of-freedom (DoFs) inherent in the large-scale antenna array. Specifically, we introduce artificial perturbation signals to the frequency-domain precoded signals, with the aim of reducing the PAPRs of their time-domain counterpart signals. Meanwhile, the additive perturbation signal associated with each tone is constrained to lie in the null-space of its associated channel matrix, such that it does not cause any multi-user inference or out-of-band radiations. Such a problem is formulated as a convex optimization problem, and an efficient algorithm is developed by resorting to the variable splitting and alterative direction method of multipliers (ADMM) techniques. Simulation results show that the proposed method has a fast convergence rate and achieves substantial PAPR reduction within only tens of iterations. In addition, unlike other precoding-based PAPR reduction methods, our proposed method which introduces perturbation signals to the precoded signals is independent of the precoding stage and thus could be more suitable for practical systems.

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“…So far most existing PAPR reduction methods (e.g. [15]- [17]) for large-scale MU MIMO-OFDM systems reduce the PAPR through directly devising the precoded signals {w n }.…”

Section: B Peak-to-average Power Ratio (Papr)mentioning

confidence: 99%

“…• Most PAPR reduction schemes for large-scale MIMO-OFDM systems are precoding-based, e.g. [15]- [17]. These methods reduce the PAPR through designing the precoding matrices or devising the precoded signals directly.…”

Section: Introductionmentioning

confidence: 99%

An ADMM Approach for PAPR Reduction for Large-Scale MIMO-OFDM Systems

Bao

Fang

Wan

et al. 2018

IEEE Trans. Veh. Technol.

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“…One may refer to the method [23] to improve the stability of the GAMP against the ill-condition of the matrix A. Nevertheless, GAMP is expected to perform well in wireless communication scenarios since indoor and urban outdoor environments are typically rich in scattering and entries of MIMO channel matrices are usually assumed to be i.i.d Gaussian [15], [24].…”

Section: Aji Sjmentioning

confidence: 99%

“…We see that ln q(α 1 ) also has a factorized form ln q(α 1 ) = i ln q(α i1 ). Note that η i1 (defined in (15)) is a function of α i1 , which makes the inference of q(α i1 ) difficult. To address this difficulty, we use the latest computed value to replace η i1 i.e.…”

Section: E-step: Update Of Hidden Variablesmentioning

confidence: 99%

An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

Bao

Fang

Chen

et al. 2016

IEEE Trans. Wireless Commun.

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Abstract-We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems. Specifically, given a set of symbol vectors to be transmitted to K users, the problem is to find an OFDM-modulated signal that has a low PAPR and meanwhile enables multiuser interference (MUI) cancelation. Unlike previous works that tackled the problem using convex optimization, we take a Bayesian approach and develop an efficient PAPR reduction method by exploiting the redundant degrees-of-freedom of the transmit array. The sought-after signal is treated as a random vector with a hierarchical truncated Gaussian mixture prior, which has the potential to encourage a low PAPR signal with most of its samples concentrated on the boundaries. A variational expectation-maximization (EM) strategy is developed to obtain estimates of the hyperparameters associated with the prior model, along with the signal. In addition, the generalized approximate message passing (GAMP) is embedded into the variational EM framework, which results in a significant reduction in computational complexity of the proposed algorithm. Simulation results show our proposed algorithm achieves a substantial performance improvement over existing methods in terms of both the PAPR reduction and computational complexity.

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“…Generally, precoding approaches can be classified into nonlinear precoding and linear precoding. The optimal precoding is the nonlinear dirty paper precoding (DPC) [9], which can effectively eliminate the interference between different users and achieve optimal performance. However, nonlinear precoding schemes usually suffer from high complexity which makes them unpractical due to the hundreds of antennas in massive MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

Linear precoding design for massive MIMO based on the minimum mean square error algorithm

Zhang

2017

J Embedded Systems

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Compared with the traditional multiple-input multiple-output (MIMO) systems, the large number of the transmit antennas of massive MIMO makes it more dependent on the limited feedback in practical systems. In this paper, we study the problem of precoding design for a massive MIMO system with limited feedback via minimizing mean square error (MSE). The feedback from mobile users to the base station (BS) is firstly considered; the BS can obtain the quantized information regarding the direction of the channels. Then, the precoding is designed by considering the effect of both noise term and quantization error under transmit power constraint. Simulation results show that the proposed scheme is robust to the channel uncertainties caused by quantization errors.

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Low-Complexity Precoding Design for Massive Multiuser MIMO Systems Using Approximate Message Passing

Cited by 38 publications

References 15 publications

An ADMM Approach for PAPR Reduction for Large-Scale MIMO-OFDM Systems

An ADMM Approach for PAPR Reduction for Large-Scale MIMO-OFDM Systems

An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

Linear precoding design for massive MIMO based on the minimum mean square error algorithm

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