MIMO Wireless Communication System

Wireless communication using multiple-input multiple-output (MIMO)

systems enables increased spectral efficiency for a given total transmit power.

Increased capacity is achieved by introducing additional spatial channels that are

exploited by using space-time coding. In this article, we survey the environmental

factors that affect MIMO capacity. These factors include channel complexity,

external interference, and channel estimation error. We discuss examples of

space-time codes, including space-time low-density parity-check codes and spacetime

turbo codes, and we investigate receiver approaches, including multichannel

multiuser detection (MCMUD). The ‘multichannel’ term indicates that the

receiver incorporates multiple antennas by using space-time-frequency adaptive

processing. The article reports the experimental performance of these codes and

receivers.

Mmultiple-output (MIMO) systems

are a natural extension of developments

in antenna array communication. While the

advantages of multiple receive antennas, such as gain

and spatial diversity, have been known and exploited for

some time [1, 2, 3], the use of transmit diversity has

only been investigated recently [4, 5]. The advantages

of MIMO communication, which exploits the physical

channel between many transmit and receive antennas,

are currently receiving significant attention [6–9].

While the channel can be so nonstationary that it cannot

be estimated in any useful sense [10], in this article

we assume the channel is quasistatic.

MIMO systems provide a number of advantages

over single-antenna-to-single-antenna communication.

Sensitivity to fading is reduced by the spatial diversity

provided by multiple spatial paths. Under certain environmental

conditions, the power requirements associated

with high spectral-efficiency communication can

be significantly reduced by avoiding the compressive region

of the information-theoretic capacity bound. Here,

spectral efficiency is defined as the total number of information

bits per second per Hertz transmitted from

one array to the other.

After an introductory section, we describe the concept

of MIMO information-theoretic capacity bounds.

Because the phenomenology of the channel is important

for capacity, we discuss this phenomenology and

associated parameterization techniques, followed by examples

of space-time codes and their respective receivers

and decoders. We performed experiments to investigate

channel phenomenology and to test coding and receiver

techniques.

Capacity

We discuss MIMO information-theoretic performance

bounds in more detail in the next section. Capacity increases

linearly with signal-to-noise ratio (SNR) at low

SNR, but increases logarithmically with SNR at high

SNR. In a MIMO system, a given total transmit power

can be divided among multiple spatial paths (or modes),

driving the capacity closer to the linear regime for each

mode, thus increasing the aggregate spectral efficiency.

As seen in Figure 1, which assumes an optimal high

spectral-efficiency MIMO channel (a channel matrix

with a flat singular-value distribution), MIMO systems

enable high spectral efficiency at much lower required

energy per information bit.

The information-theoretic bound on the spectral efficiency

is a function of the total transmit power and

the channel phenomenology. In implementing MIMO

systems, we must decide whether channel estimation

information will be fed back to the transmitter so that

the transmitter can adapt. Most MIMO communication

research has focused on systems without feedback.

A MIMO system with an uninformed transmitter

(without feedback) is simpler to implement, and at high

SNR its spectral-efficiency bound approaches that of an

informed transmitter (with feedback).

One of the environmental issues with which communication

systems must contend is interference, either

unintentional or intentional. Because MIMO systems

use antenna arrays, localized interference can be

mitigated naturally. The benefits extend beyond those

achieved by single-input multiple-output systems, that

is, a single transmitter and a multiple-antenna receiver,

because the transmit diversity nearly guarantees that

nulling an interferer cannot unintentionally null a large

fraction of the transmit signal energy.

Phenomenology

We discuss channel phenomenology and channel parameterization

techniques in more detail in a later section.

Aspects of the channel that affect MIMO system

capacity, namely, channel complexity and channel stationarity,

are addressed in this paper. The first aspect,

channel complexity, is a function of the richness of scatterers.

In general, capacity at high spectral efficiency

increases as the singular values of the channel matrix

increase. The distribution of singular values is a measure

of the relative usefulness of various spatial paths

through the channel.

Space-Time Coding and Receivers

In order to implement a MIMO communication system,

we must first select a particular coding scheme.

Most space-time coding schemes have a strong connection

to well-known single-input single-output (SISO)

coding approaches and assume an uninformed transmitter

(UT). Later in the article we discuss space-time

low-density parity-check codes, space-time turbo codes,

and their respective receivers. Space-time coding can

exploit the MIMO degrees of freedom to increase redundancy,

spectral efficiency, or some combination

of these characteristics [11]. Preliminary ideas are discussed

elsewhere [6].

A simple and elegant solution that maximizes diversity

and enables simple decoupled detection is proposed

in Reference 12. More generally, orthogonal space-time

block codes are discussed in References 13 and 14. A

general discussion of distributing data across transmitters

(linear dispersive codes) is given in Reference 15.

High SNR design criteria and specific examples are given

for space-time trellis codes in Reference 16. Unitary

codes optimized for operation in Rayleigh fading are

presented in Reference 17. Space-time coding without

the requirement of channel estimation is also a common

topic in the literature. Many differential coding

schemes have been proposed [18]. Under various constraints

at the transmitter and receiver, informationtheoretic

capacity can be evaluated without conditioning

on knowledge of the propagation channel [19, 20].

More recently, MIMO extensions of turbo coding have

been suggested [21, 22]. Finally, coding techniques for

informed transmitter systems have received some interest

[23, 24].

Experimental Results

Because information-theoretic capacity and practical

performance are dependent upon the channel phenomenology,

a variety of experiments were performed. Both

channel phenomenology and experimental procedures

are discussed in later sections. Experiments were per-

Posted in: Telecomunication