MIMO技术在移动通信中的关键进展与数据速率优化

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MIMO（Multiple-Input Multiple-Output）技术在移动通信中的应用已经成为无线通信领域的关键技术之一。这篇论文，发表于《国际通信、网络与系统科学》(Int.J_Communications,NetworkandSystemSciences) 2010年第三期，由Farhan Khalid和Joachim Speidel两位作者自德国斯图加特大学电信研究所共同撰写，其DOI为10.4236/ijcns.2010.33031，于2010年3月在线发布。论文首先概述了MIMO技术的核心进展，重点讨论了单用户MIMO(SU-MIMO)和多用户MIMO(MU-MIMO)这两种关键模式。SU-MIMO主要关注的是利用多个天线进行数据传输，通过空间分集提高接收信号的质量，减少干扰，并探讨了开环和闭环系统的区别，特别是强调了如何通过优化算法实现数据速率的最大化。作者深入分析了SU-MIMO系统的理论基础和实际应用策略，解释了这些技术如何通过独立的数据流在多个天线之间进行高效的信道编码和解码。接着，论文详细地审视了MU-MIMO的多种上行链路和下行链路技术，包括空间复用、多用户检测、波束形成等，旨在澄清这些技术背后的原理，强调了MU-MIMO对于提升网络容量、频谱效率以及降低干扰的重要性。 MU-MIMO尤其在多终端环境中表现出色，它允许同时服务于多个用户，显著提高了系统吞吐量，使得网络能够更好地支持高清视频流、在线游戏和其他高数据需求服务。此外，MU-MIMO还涉及协作多点传输（CoMP）和非协作多点传输（CoMP）的概念，前者通过基站间的协同操作来改善用户的性能，后者则通过分布式处理来简化网络结构。总结来说，该篇综述性文章全面阐述了MIMO技术在移动通信中的最新发展，为读者提供了理解SU-MIMO和MU-MIMO技术的关键概念、优势以及它们在设计现代无线通信系统中的重要角色。这对于从事无线通信研究、工程实践以及标准制定的人来说，是一份宝贵的参考资料。

F. KHALID ET AL.

219

efficiencies of 2  2 SM-OFDM systems and 4  2

CDA-SM-OFDM systems for a low user mobility indoor

WLAN scenario. Here it can be seen that the 4  2

CDA-SM-OFDM systems provide much higher spectral

efficiencies at low SNR values.

3.3. Singular Value Decomposition Based MIMO

Precoding

Singular value decomposition (SVD) based MIMO pre-

coding is a closed-loop MIMO scheme where the pre-

coding filter at the transmitter is designed by taking the

SVD of the MIMO channel matrix H.

[36] provides an analysis of the classical SVD based

MIMO precoding scheme, SVD based precoding with

ZF equalization, SVD based precoding with MMSE

equalization and also an improved SVD based precoding

technique. All of these schemes are analyzed with realis-

tic channel knowledge at the transmitter. Figure 6 shows

the block diagram of the SVD based MIMO-OFDM

transmitter and receiver.

3.3.1. Classical SVD Precoding and Equalization

In SVD based techniques, the channel matrix of a

system with transmit antennas and

receive an

tennas, is decomposed as

HUDV

(5)

where and are unitary matrices

while

NN

U 

NN

V 



D 

is a diagonal matrix consisting of the

ordered singular values .

The classical

SVD approach utilizes matrix for

ecoding at the transmitter. The columns of matrix

are the eigenvectors of

. The received signal is

given by



rHVsn (6)

where is a vector of information symbols

and

is the noise vector corresponding to an additive white

Gaussian noise

(AWGN) process with variance



for

each element. At the receiver, matrix

U is employed

for equalization and the detected signal vector is given

HH H

 



Ur UHVs Un

UUDVVs Un

yDsUn

(7)

Each individual received signal can be written as

iii

ydsn





 (8)

(a)

(b)

Figure 6. SVD based MIMO-OFDM system (a) Transmitter and (b) Receiver [36].

F. KHALID ET AL.

220

where represents the i-th element of

Un. The

corresponding SISO SNR values are then given by

SNR d



 (9)

Equations (8) and (9) show that the singular values

represent the MIMO processing gain for each of the ei-

genmodes. Therefore, SVD based MIMO precoding re-

quires adaptive modulation and bit loading techniques

for capacity maximization [36].

3.3.2. SVD Precoding with ZF Equalization

Linear ZF equalization can also be used at the receiver

which is based on the inversion of the estimated MIMO

channel matrix. ZF equalization requires the estimation

of the product of at the receiver. Assuming ideal

channel knowledge

at the receiver, the detected signal

can then be given by



 







yHVr

HV HVs HV n



(10)



1

ys UDn

(11)

where represents the pseudo inverse.







3.3.

3. SVD Precoding with MMSE Equalization

MMSE equalization is based on minimizing the mean

square error (MSE) between the transmitted and detected

symbols. The minimum mean square error is given by







min

ess



(12)

where is the transmitted symbol and represents

the received symbol. The detected signal for SVD based

MIMO MMSE equalization is given by













IHV HV HVr

(13)

with

N utilized eigenmodes. As seen from Equa-

tion (13), MMSE MIMO equalization also requires the

precoding matrix at the receiver.

3.3.4. Impr

oved SVD Prec

oding Technique with

Realistic Channel Knowledge

An improved SVD based MIMO precoding technique is

also proposed in [36] which maximizes MIMO capacity

while considering realistic channel knowledge at the

transmitter rather than the ideal one. The MIMO capacity

for realistic channel knowledge is given by

log 1





















(14)

where represent the singular values for the case of

realistic channel knowledge. The improved technique

considers the



strongest eigenmodes for transmission

with



if the following statement is fulfilled.

log 1





 

 

 

 

 





(15)

The remaining eigenmodes which correspond to the



unused eigenvectors of the precoding matrix

are not utilized.



3.3.5. Perform

nce Comparison

Figure 7

shows the BER performance comparison of the

classical SVD, ZF and MMSE equalization schemes for

a 4  4 MIMO-OFDM system. A curve for ideal ZF

equalization is also provided for reference. It is clear

from the comparison that MMSE equalization provides

the best results with realistic channel estimation.

Figure 8 shows the performance comparison of the

Figure 7. BER performance of uncoded 4  4 SVD based

MIMO systems [36].

Figure 8. BER performance of an uncoded 4  4 SVD based

MIMO system with MMSE equalization [36].

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