A Low Complexity Precoding Algorithm Based on Parallel Conjugate Gradient for Massive MIMO Systems

Chen, Geng; Zeng, Qingtian; Xue, Xiaomei; Li, Zhengquan

doi:10.1109/access.2018.2870861

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Here, we assume that the channel matrices {H n } are perfectly known at the BS, which can be determined by exploiting the channel reciprocity [14], [15] of time division multiplexing (TDD) systems. It can be easily verified that the ZF has a high computational complexity [16], [17] since it needs to compute large-scale pseudo-inverse matrix.…”

Section: B Multi-user Precoding Schemementioning

confidence: 99%

PAPR-Aware Massive MIMO-OFDM Downlink

2019

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We investigate the peak-to-average power ratio (PAPR) reduction problem in orthogonal frequency-division multiplexing-based massive multiuser (MU) multiple-input multiple-output (MIMO) downlink systems. In this paper, we develop a downlink transmission scheme that performs jointly MU precoding and PAPR reduction (PP) by exploiting the excess degrees of freedom offered by equipping the BS by a large number of antennas. Specifically, the joint MU precoding and PAPR reduction scheme is formulated as a simple convex optimization problem solved via steepest gradient descent (GD) approach. Then, we develop a novel algorithm, referred to as MU-PP-GDm, to reduce the PAPR of the transmitted signals by exploiting the high-dimensional null space of the MIMO channel matrix while maintaining an excellent transmission quality. The simulation results show that the proposed MU-PP-GDm has low computational complexity and can achieve substantial PAPR performance with a fast convergence rate. INDEX TERMS 5G+, massive multiple-input multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM), peak-to-average power ratio (PAPR), multiuser (MU) precoding, convex optimization, gradient descent (GD). The associate editor coordinating the review of this manuscript and approving it for publication was Jiayi Zhang. In practice, broadband wireless communications, however, encounter large delay spread, and, therefore, suffer from frequency-selective fading. Orthogonal frequency-division multiplexing (OFDM), a scheme of encoding digital symbols on multiple orthogonal sub-carriers, is an efficient and well-established way to deal with frequency-selective channels. Therefore, massive MIMO-OFDM is a very promising combination to meet the ever growing demands for higher link readability and spectrum efficiency of next-generation wireless communication systems (5G and Beyond). However, massive MIMO precoders exhibit transmit signals with high peak-to-average power ratio (PAPR), regardless of whether single-carrier or OFDM transmission and of whether a low or a high modulation order is used [4]. Consequently, the nonlinearity of the radio frequency (RF) high-power amplifier (HPA), which is necessary in a transmission chain, yields in-band distortion and out-of-band radiation (OBR) which cause signal distortion, phase rotation and adjacent channel interference, respectively. To avoid such heavy distortions, the transmit signals require that the power amplifiers are backed-off and operated in their linear region (i.e., where their transfer characteristics are sufficiently linear). Nevertheless, operating at lower power levels reduces 25474 2169-3536 2019 IEEE. Translations and content mining are permitted for academic research only.

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Section: B Multi-user Precoding Schemementioning

confidence: 99%

PAPR-Aware Massive MIMO-OFDM Downlink

2019

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“…It aims to cancel out multi-user interference (MUI) between the legitimate receivers. The computational complexity of ZF precoding is discussed in [16], [17]. The adopted ZF precoding vector, F ∈ C N t ×N r , is can be written as…”

Section: System Modelmentioning

confidence: 99%

PAPR-Aware Artificial Noise for Secure Massive MIMO Downlink

et al. 2022

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This paper introduces a new approach to providing secure physical-layer massive multipleinput multiple-output (MIMO) based communications that can improve the energy efficiency of the system. This is achieved by synthesizing orthogonal artificial noise (AN) that has to be constrained to lie in the null space of the legitimate users' channels while it should lie in the range space of the eavesdropper's channel. In addition, this AN reduces the peak-to-average power ratio (PAPR) of the transmit signal. Indeed, low PAPR signals are preferable and more efficient for low-cost hardware, thus improving the energy consumption of massive MIMO systems. In this paper, we propose a new PAPR-aware precoding scheme based on the use of AN to enhance the secrecy performance of massive MIMO while reducing the PAPR of the transmit signal and guaranteeing excellent transmission quality for legitimate users. The scheme is formulated as a convex optimization problem that can be resolved via steepest gradient descent (GD). Accordingly, we developed a new iterative algorithm, referred to as PAPR-Aware-Secure-mMIMO, that makes use of instantaneous information to solve the optimization problem. Simulation results show the efficiency of our proposed algorithm in terms of PAPR reduction and secrecy, which is also studied with respect to power distribution between useful signal and AN, PAPR targets and the number of BS antennas.INDEX TERMS Artificial noise (AN), massive multiple-input multiple-output (MIMO), peak-to-average power ratio (PAPR), physical layer security (PLS), power allocation, steepest gradient descent (GD), zeroforcing (ZF) precoding.

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A Low-Complexity AEPDF-assisted Precoding Scheme for Massive MIMO Systems with Transmit Antenna Correlation

Lee

Yang

2020

J Sign Process Syst

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A Low Complexity Precoding Algorithm Based on Parallel Conjugate Gradient for Massive MIMO Systems

Cited by 6 publications

References 25 publications

PAPR-Aware Massive MIMO-OFDM Downlink

PAPR-Aware Massive MIMO-OFDM Downlink

PAPR-Aware Artificial Noise for Secure Massive MIMO Downlink

A Low-Complexity AEPDF-assisted Precoding Scheme for Massive MIMO Systems with Transmit Antenna Correlation

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