空间时码多输入-多输出无线系统概述：从理论到实践

需积分: 9 195 浏览量更新于2024-08-01 收藏 928KB PDF 举报

本文档深入探讨了"From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems"。该论文是2003年4月发表在《IEEE Journal on Selected Areas in Communications》的一篇综述性文章，主要关注多输入-多输出（MIMO）空间-时间编码无线系统的最新进展。作者David Gesbert、Mansoor Shafi、Da-shan Shiu、Peter J. Smith和Ayman Naguib作为IEEE会员，共同为我们提供了关于MIMO技术革命性潜力的全面介绍。首先，论文回顾了MIMO无线链路研究的历史，强调了这一技术的巨大潜力，特别是在提高数据传输速率和抗衰落性能方面。MIMO系统通过同时利用多个天线进行通信，能够实现空间复用（Spatial Multiplexing），即在不增加频率资源的情况下增加数据流，从而显著提升频谱效率。其次，文章详细介绍了几种关键的MIMO空间-时间编码算法，这些算法通常是在理想独立衰落条件下设计和分析的。这些编码技术包括但不仅限于分集编码、空间多工（Spatial Division Multiple Access, SDMA）、以及诸如卷积码、Turbo码等高效的编码方案，它们旨在最大化信号的抗干扰能力和可靠性。然而，理论上的巨大增益能否在实际环境中得到体现，很大程度上取决于对信道模型的精确理解和建模。因此，论文着重讨论了当前的研究进展，包括如何更好地模拟和测量实际的多径衰落、多用户干扰和频率选择性衰落等无线环境因素，以便更准确地评估MIMO系统的性能。最后，文章针对MIMO技术在实际无线系统和标准中的应用提出了挑战和问题。这涉及到如何在有限的硬件资源、功率预算以及复杂度限制下，有效集成MIMO功能，确保其能够在现代通信网络中无缝工作，并与其他技术如OFDM（正交频分复用）协同，以实现最佳的系统性能。这篇论文为读者提供了一个从理论到实践的桥梁，帮助理解MIMO空间-时间编码技术的发展历程、核心原理及其在无线通信领域的实际应用前景。通过阅读这篇文章，研究人员和工程师可以更好地掌握MIMO技术的关键要素，并为未来的无线通信标准制定提供指导。

GESBERT et al.: FROM THEORY TO PRACTICE: AN OVERVIEW OF MIMO SPACE–TIME CODED WIRELESS SYSTEMS 285

or equivalent. Some results are available here [27] but they are

limited.

Some caution is necessary in interpreting the above equa-

tions. Capacity, as discussed here and in most MIMO work

[1], [3], is based on a “quasi-static” analysis where the channel

varies randomly from burst to burst. Within a burst the channel

is assumed fixed and it is also assumed that sufficient bits are

transmitted for the standard infinite time horizon of information

theory to be meaningful. A second note is that our discussion

will concentrate on single user MIMO systems but many results

also apply to multiuser systems with receive diversity. Finally,

the linear capacity growth is only valid under certain channel

conditions. It was originally derived for the independent and

identically distributed (i.i.d.) flat Rayleigh fading channel and

does not hold true for all cases. For example, if large numbers

of antennas are packed into small volumes, then the gains in

may become highly correlated and the linear relationship will

plateau out due to the effects of antenna correlation [28]–[30].

In contrast, other propagation effects not captured in (4) may

serve to reinforce the capacity gains of MIMO such as multi-

path delay spread. This was shown in particular in the case when

the transmit channel is known [4] but also in the case when it is

unknown [5].

More generally, the effect of the channel model is critical.

Environments can easily be chosen which give channels where

the MIMO capacities do not increase linearly with the numbers

of antennas. However, most measurements and models available

to date do give rise to channel capacities which are of the same

order of magnitude as the promised theory (see Section V). Also

the linear growth is usually a reasonable model for moderate

numbers of antennas which are not extremely close-packed.

B. Information Theoretic MIMO Capacity

1) Background: Since feedback is an important component

of wireless design (although not a necessary one), it is useful to

generalize the capacity discussion to cases that can encompass

transmitters having some a priori knowledge of channel. To this

end, we now define some central concepts, beginning with the

MIMO signal model

(5)

In (5),

is the received signal vector, is the

transmitted signal vector and is an vector of additive

noise terms, assumed i.i.d. complex Gaussian with each element

havinga varianceequal to

. For convenience we normalize the

noise power so that

in the remainder of this section. Note

that the system equation represents a single MIMO user com-

municating over a fading channel with additive white Gaussian

noise (AWGN). The only interference present is self-interfer-

ence between the input streams to the MIMO system. Some au-

thors have considered more general systems but most informa-

tion theoretic results can be discussed in this simple context, so

we use (5) as the basic system equation.

Let

denote the covariance matrix of , then the capacity of

the system described by (5) is given by [3], [21]

b/s/Hz (6)

where

holds to provide a global power constraint.

Note that for equal power uncorrelated sources

and (6) collapses to (4). This is optimal when is unknown at

the transmitter and the input distribution maximizing the mutual

information is the Gaussian distribution [3], [21]. With channel

feedback

may be known at the transmitter and the optimal

is not proportional to the identity matrix but is constructed from

a waterfilling argument as discussed later.

The form of equation (6) gives rise to two practical questions

of key importance. First, what is the effect of

? If we compare

the capacity achieved by

(equal power transmis-

sion or no feedback) and the optimal

based on perfect channel

estimation and feedback, then we can evaluate a maximum ca-

pacity gain due to feedback. The second question concerns the

effect of the

matrix. For the i.i.d. Rayleigh fading case we

have the impressive linear capacity growth discussed above. For

a wider range of channel models including, for example, corre-

lated fading and specular components, we must ask whether this

behavior still holds. Below we report a variety of work on the

effects of feedback and different channel models.

It is important to note that (4) can be rewritten as [3]

b/s/Hz (7)

where

are the nonzero eigenvalues of ,

, and

(8)

This formulation can be easily obtained from the direct use

of eigenvalue properties. Alternatively, we can decompose the

MIMO channel into m equivalent parallel SISO channels by

performing a singular value decomposition (SVD) of

[3],

[21]. Let the SVD be given by

, then and

are unitary and is diagonal with entries specified by

. Hence (5) can

be rewritten as

(9)

where

, and . Equation (9) repre-

sents the system as m equivalent parallel SISO eigen-channels

with signal powers given by the eigenvalues

Hence, the capacity can be rewritten in terms of the eigen-

values of the sample covariance matrix

. In the i.i.d. Rayleigh

fading case,

is also called a Wishart matrix. Wishart matrices

have been studied since the 1920s and a considerable amount is

known about them. For general

matrices a wide range of

limiting results are known [22], [31]–[34] as

or or both

tend to infinity. In the particular case of Wishart matrices, many

exact results are also available [31], [35]. There is not a great

deal of information about intermediate results (neither limiting

nor Wishart), but we are helped by the remarkable accuracy of

some asymptotic results even for small values of

, [36].

We now give a brief overview of exact capacity results,

broken down into the two main scenarios, where the channel is

either known or unknown at the transmitter. We focus on the

Authorized licensed use limited to: Harbin Institute of Technology. Downloaded on August 15,2010 at 07:50:36 UTC from IEEE Xplore. Restrictions apply.

剩余21页未读，继续阅读

FFtserAph

粉丝: 17
资源: 22

空间时码多输入-多输出无线系统概述：从理论到实践

SPACE-TIME CODING: THEORY AND PRACTICE

An Overview of MIMO Space-Time Coded Wireless Systems

ey-overview-of-china-outbound-investment-of-2020.pdf

Overview-and-Setup-Chinese.pdf

mc-ds-solutions-overview-a4-zh-CN.pdf

mc-so-supplier-excellence-overview-a4-zh-CN.pdf

leverage-open-source-benefits-with-assurance-of-hitachi-overview.pdf

ASAM_XCP_Part1-Overview_V1-1-0.pdf

An Overview of Ultra-Wide-Band Systems With MIMO

datasheet-firewall-feature-overview-zh-CN-2013-3-19.pdf

最新资源