Analysis and Design of Tomlinson-Harashima Precoding for Multiuser MIMO Systems

Chen, Xiang; Huang, Ming Zhao Min; Zhou, Shidong; Wang, Jing

doi:10.5772/14123

Cited by 1 publication

(4 citation statements)

References 26 publications

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“…• It has a promising performance, by employing SIC of the data streams at the transmitter, where it overcomes other linear precoding algorithms performance [208], [209]. • It has a practical implementation in contrast with the DPC algorithm [98].…”

Section: Thmentioning

confidence: 99%

Overview of Precoding Techniques for Massive MIMO

et al. 2021

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Massive multiple-input multiple-output (MIMO) is playing a crucial role in the fifth generation (5G) and beyond 5G (B5G) communication systems. Unfortunately, the complexity of massive MIMO systems is tremendously increased when a large number of antennas and radio frequency chains (RF) are utilized. Therefore, a plethora of research efforts has been conducted to find the optimal precoding algorithm with lowest complexity. The main aim of this paper is to provide insights on such precoding algorithms to a generalist of wireless communications. The added value of this paper is that the classification of massive MIMO precoding algorithms is provided with easily distinguishable classes of precoding solutions. This paper covers linear precoding algorithms starting with precoders based on approximate matrix inversion methods such as the truncated polynomial expansion (TPE), the Neumann series approximation (NSA), the Newton iteration (NI), and the Chebyshev iteration (CI) algorithms. The paper also presents the fixed-point iteration-based linear precoding algorithms such as the Gauss-Seidel (GS) algorithm, the successive over relaxation (SOR) algorithm, the conjugate gradient (CG) algorithm, and the Jacobi iteration (JI) algorithm. In addition, the paper reviews the direct matrix decomposition based linear precoding algorithms such as the QR decomposition and Cholesky decomposition (CD). The non-linear precoders are also presented which include the dirty-paper coding (DPC), Tomlinson-Harashima (TH), vector perturbation (VP), and lattice reduction aided (LR) algorithms. Due to the necessity to deal with a high consuming power by the base station (BS) with a large number of antennas in massive MIMO systems, a special subsection is included to describe the characteristics of the peak-to-average power ratio precoding (PAPR) algorithms such as the constant envelope (CE) algorithm, approximate message passing (AMP), and quantized precoding (QP) algorithms. This paper also reviews the machine learning role in precoding techniques. Although many precoding techniques are essentially proposed for a small-scale MIMO, they have been exploited in massive MIMO networks. Therefore, this paper presents the application of small-scale MIMO precoding techniques for massive MIMO. This paper demonstrates the precoding schemes in promising multiple antenna technologies such as the cell-free massive MIMO (CF-M-MIMO), beamspace massive MIMO, and intelligent reflecting surfaces (IRSs). In-depth discussion on the pros and cons, performance-complexity profile, and implementation solidity is provided. This paper also provides a discussion on the channel estimation and energy efficiency. This paper also presents potential future directions in massive MIMO precoding algorithms.

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Section: Thmentioning

confidence: 99%

Overview of Precoding Techniques for Massive MIMO

et al. 2021

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“…, where U i , Σ i and V i are left singular vector matrix, singular value matrix, right singular vector matrix. The F MU is obtained by calculating Equation (23). According to Appendix A, the obtained F MU is as follows,…”

Section: Modified Gbd Precodermentioning

confidence: 99%

“…Figures 6 and 7 represent normalized throughput performance for the modified GBD precoder, RBD precoder and GBD precoder according to SNR. Also, the throughput performances for the proposed scheme are compared with Tomlinson-Harashima precoding (THP), which is well known as a suboptimal precoder in the MU-MIMO system [23] to show the superiority of the proposed scheme. Both figures show that peak value for normalized throughput is enhanced as the number of data streams increases.…”

Section: Mgbd Precodermentioning

confidence: 99%

See 1 more Smart Citation

An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems

Lee

You

et al. 2020

Applied Sciences

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Recently, as the demand for data rate of users has increased, wireless communication systems have aimed to offer high throughput. For this reason, various techniques which guarantee high performance have been invented, such as massive multiple-input multiple-output (MIMO). However, the implementation of huge base station (BS) antenna array and decrease of reliability as the number of users increases are chief obstacles. In order to mitigate these problems, this paper proposes an adaptive precoder which provides high throughput and bit error rate (BER) performances to achieve the desired data rate in multi user (MU) MIMO downlink systems which have a practical BS antenna array (up to 16). The proposed scheme is optimized with a modified minimum mean square error (MMSE) criterion in order to improve BER gain and reduce data streams in order to obtain diversity gain at low signal to noise ratio (SNR). It is shown that the BER and throughput performances of the proposed scheme are improved.

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Analysis and Design of Tomlinson-Harashima Precoding for Multiuser MIMO Systems

Cited by 1 publication

References 26 publications

Overview of Precoding Techniques for Massive MIMO

Overview of Precoding Techniques for Massive MIMO

An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems

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