S-QR-LR：简化QR分解与晶格约简结合的多用户MIMO预编码

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"该文提出了一种基于简化QR分解和晶格约简的多用户多输入多输出（Multi-User Multiple-Input Multiple-Output, MU-MIMO）预编码方案，旨在解决在大天线数量下预编码计算复杂度高的问题。通过结合这两种技术，旨在提高无线通信系统的可实现速率，并同时服务大量用户。文章详细介绍了S-QR-LR预编码方案的设计思路、工作原理及其在降低计算复杂度和提升系统性能方面的优势。" 未来无线通信系统对发射端和接收端的天线数量需求越来越大，以提升可实现的数据传输速率。多用户MIMO（MU-MIMO）技术被视为一种有潜力的技术，能够同时服务于大量用户，进一步增加MIMO系统的可实现速率。然而，当发射端的天线数量增多时，预编码的计算复杂度成为MU-MIMO系统的一大瓶颈。该论文提出了一种名为S-QR-LR的新型预编码策略，它结合了简化QR分解（Simplified QR Decomposition）和晶格约简（Lattice Reduction）。简化QR分解是一种用于矩阵运算的有效方法，它可以将一个矩阵分解为一个正交矩阵和一个上三角矩阵，从而简化了预编码过程。而晶格约简则是一种优化技术，能改善信号在多维空间中的分布，减少干扰并提高信道容量。 S-QR-LR预编码方案的工作流程是，首先通过简化QR分解来处理传输矩阵，这降低了计算的复杂性，然后应用晶格约简来进一步优化预编码向量，以增强信号的传输效率和抗干扰能力。这种联合策略不仅减少了计算负担，还能保持甚至提升系统性能，特别是在大规模MIMO系统中。文章详细阐述了S-QR-LR预编码方案的数学模型和实现步骤，并通过仿真结果验证了其在降低复杂度和提高系统吞吐量方面的有效性。此外，论文还讨论了该方案可能面临的挑战和潜在的应用场景，为后续研究提供了有价值的参考。这篇研究论文对无线通信领域的预编码技术进行了深入探索，提出的S-QR-LR方案为解决大规模MU-MIMO系统中的计算复杂性问题提供了一个创新且实用的解决方案。

Simplified QR-decomposition based and

lattice reduction-assisted multi-user

multiple-input–multiple-output

precoding scheme

ISSN 1751-8628

Received on 16th July 2015

Revised on 11th November 2015

Accepted on 21st December 2015

doi: 10.1049/iet-com.2015.0643

www.ietdl.org

Shu Fang

✉

, Jian Wu, Chengyi Lu, Zong-di Yue, Yuan-chao Han

Department of National key Laboratory on Communications, University of Electronic Science and Technology of China, Chengdu 611731,

People’s Republic of China

✉ E-mail: fangshu@uestc.edu.cn

Abstract: Future wireless communication sy stems require more and more antennas at the transceiver to improve the

achievable rates. Multi-user multiple-input multiple-output (MU-MIMO) technique is r egarded as a potential technique

to serve large number of users simultaneously to further increase the achievable rates in MIMO systems. If

the number of antennas at the transceiv er is large, the computational complexity of precoding becomes the b ottleneck

and a big challenge in MU-MIMO system. In this paper, a simplified QR decomposition with lattice reduction assisted

MU-MIMO precoding scheme named S-QR-LR is proposed as a low-compl e xity MU-MIMO transm issio n s cheme . T he

simplified QR decomposition method is delicately designed and operated twice for the proposed precoding scheme not

only to achieve good performance but also reduce the computational complexity significantly. The proposed S-QR-LR

scheme first uses the simplified QR decomposition operation to balance the multi-user interference and the noise.

Then, the proposed S-QR-LR precoding scheme utilizes the simplified QR decomposition metho d agai n with the assist

of lattice reduction to obtain the precoding gain to further improve the performance with low computational

complexity. Analytical and simulation results sho w that the prop osed S-QR-L R precoding scheme achieves best

performance among the existing precoding schemes, but requires the lowest computational complexity.

1 Introduction

Multi-user multiple-input–multiple-output (MU-MIMO) technique,

a potential method to improve data rate and achieve high capacity

has been extensively studied in recent years [1, 2]. The vertical

Bell laboratories layered space–time (VBLAST) MIMO technique

is ﬁrst designed in [3] for high data rate transmission. VBLAST

architecture utilises the MIMO conﬁguration to provide parallel

layered transmission in spatial domain to improve the spectrum

efﬁciency. The drawback of VBLAST is that more receive

antennas are required than the transmitter, which is always not

valid in downlink cellular systems. In consequence, the

MU-MIMO precoding [4] technique for downlink MIMO systems

that requires channel state information (CSI) at the transmitter, is

developed to solve the problem. The spatial division multiple

access-based MU-MIMO technique improves the achievable rate

by transmitting the data of multiple users simultaneously and

pre-cancelling the MU interference (MUI) at the transmitter with

the CSI. To meet the continuous growing demands for high data

rates, MU-MIMO systems [5] with more transmit antennas have

attracted much attention for the future wireless communication

systems. The conﬁguration of up to eight transmit antennas at base

station (BS) in LTE-advanced [6] and IEEE 802.11ac [7]is

suggested. Massive MIMO (also known as large-scale antenna

systems, very large MIMO and hyper MIMO) is an emerging

technology that uses a few hundred antennas simultaneously to

serve tens of terminals in the same time–frequency resource [8, 9].

Therefore, MU-MIMO is regarded as a potential technique to

serve large number of users simultaneously in future massive

MIMO systems. Thus, the computational complexity of precoding

becomes the bottleneck and a big challenge in MU-MIMO

systems with large antennas at the transceiver.

In MU-MIMO systems, the MUI is the major problem that affects

its performance severely. The objective of MU-MIMO techniques is

to cancel the MUI via precoding at BS before transmission. The

zero-forcing channel-inversion (ZF-CI) precoding is ﬁrst proposed

in [10], which introduces a ZF equaliser at the transmitter instead

of the receiver to pre-cancel the MUI. However, ZF-CI precoding

results in low performance due to the reason that the precoding

vectors after ZF operation is non-unitary that will amplify the

noise, especially when the channel is ill-conditioned. The

well-known block diagonalisation (BD) precoding scheme that is

regarded as a generalisation of the ZF-CI precoding is proposed in

[11, 12]. Two singular value decomposition (SVD) operations are

implemented for each user in BD precoding algorithm. The MUI

from other users is eliminated completely by adopting the ﬁrst

SVD operation to MUI MIMO channel. Thus, the MU-MIMO

broadcast channel is decomposed into multiple parallel single-user

MIMO (SU-MIMO) channels. Then, the second SVD operation is

implemented to parallelise each user’s streams and obtain

maximum precoding gain for each sub-stream to further improve

the performance. BD precoding provides better performance than

ZF-CI precoding due to the reason that the unitary precoding

vectors for BD will not amplify the noise after precoding.

However, the computational costs of BD precoding scheme is very

heavy because of two SVD operations for each user.

Recent researches for MU-MIMO in [13, 14] focus on designing

the BD-type precoding schemes with less computational complexity.

By replacing the ﬁrst SVD operation with a less complex solution to

mitigate the MUI, a QR-decomposition-based BD (QR-BD)

precoding scheme is presented in [13] for MU-MIMO systems.

QR-BD utilises a QR-decomposition to the MUI MIMO channel

to obtain the null space of MUI. Therefore, the complexity of

SVD operation in BD precoding is reduced by QR operation in

QR-BD precoding. Similar to the QR-BD precoding scheme, a

generalised ZF-CI (GZI) precoding method is developed in [14],

where the MUI MIMO channel is operated ﬁrst by pseudo

inversion and then QR-decomposition to mitigate the MUI. Both

IET Communications

Research Article

IET Commun., 2016, Vol. 10, Iss. 5, pp. 586–593

586 This is an open access article published by the IET under the Creative Commons Attribution License

(http://creativecommons.org/licenses/by/3.0/)

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