Presented by:

Y. Naveen Kumar,

E-mail: naveen4ever@gmail.com

B. Hari Prasad,

E-mail: hari_b115@yahoo.co.in

Pre-final Year, E.C.E.

MIMO Wireless Communication Systems

ABSTRACT

Multiple-Input Multiple-Output (MIMO) wireless systems provides

increased spectral efficiency and system capacity, link reliability, resistance

to interference, by utilizing antenna arrays at both the transmitter and

receiver. In this paper the importance of MIMO wireless communication

systems is explored by an explanation of how they work. Key technologies

that enable MIMO systems to be used practically are explained in Section

III. Next, MIMO capacity and channel phenomenology is presented along

with space time codes which are all crucial to the implementation of MIMO

systems. Finally, some misconceptions of MIMO systems in the real world

are analyzed.

Index Terms—multiple-input multiple-output (MIMO) systems,

orthogonal frequency division multiplexing (OFDM), prototyping, space

time codes

[1]

[2] INTRODUCTION

Multiple Input Multiple Output communication systems, often termed MIMO,

combines multiple data streams for the transmission of information through one radio

channel. This increases the amount of data which can be transmitted per channel in a

fixed amount of time. The maximum speed and efficiency of the MIMO system is

equivalent to the number of signal streams sent in the channel. Furthermore MIMO

communication links use multiple antenna arrays at both the transmitter and receiver to

take advantage of the total spatial dimension of the propagation channel. When properly

designed, MIMO systems not only multiply data streams in one channel, increase

throughput, system performance, and reliability but it also produces remarkable

reductions in channel vibrations.

Previous to the discovery of MIMO technology, multi-path propagation was seen

as a hindrance to wireless communication systems. In contrast, MIMO technology

accepts multi-path propagation as an opportunity to address the challenges of wireless

technology. MIMO systems serve as a solution to these challenges by providing an

increase in signal performance and reliability while handling restricted spectrum. For

example, wireless LAN products implementing MIMO have demonstrated in laboratory

tests, field test and commercial applications the ability to cover areas at least twice as

large as conventional wireless LAN products at comparable or better data rates with

comparable or better reliability.

Although there are many advantages to MIMO technology, designing and

evaluating MIMO communication systems in the practical environment is a very difficult

task. This is due to the behavior of the propagation channel and the complicated

electromagnetic relationship between the environment and antenna arrays. The specific

environmental factors which can affect MIMO capabilities include channel complexity,

external interference, and channel estimation error.

HOW MIMO SYSTEMS WORK

MIMO systems utilize multiple antenna arrays at both the transmitter and

receiver. They divide a data stream into multiple streams, each of which is modulated

and transmitted independently through different transmit antennas at the same time and in

the same frequency band. By taking advantage of the multi-path propagation

phenomenon of the signals, each received chain becomes a linear combination of the

multiple transmitted data streams. Since these data streams are transmitted in a parallel

fashion, the throughput is increased as antennas are added to the system. Using a set of

well-defined instructions to estimate all channels between each transmitter and receiver,

the data streams are separated at the receiver. By exploiting the spatial dimension, each

multi-path route can be thought of as a separate channel that creates multiple "virtual

wires" between the transmitter and receiver. To take advantage of these “virtual wires”

created by multi-path, MIMO systems makes use of multiple, spatially separated

antennas to transfer more data and increase throughput. As shown in Fig. 1, a MIMO

decoder uses antennas at the receiver. With the assumption of N receive antennas;

the signal received by each antenna can be represented as where

. (1)

Fig. 1. A Generic MIMO system.

Treating the channel as a matrix, we can recover the independent transmitted streams {xi}

from {rj} by estimating the individual channel weights hij to construct the channel matrix

H. As a result, the product of the vector r with H-1 gives the estimate of the transmitted

vector x.

Orthogonal Frequency Division Multiplexing

Orthogonal Frequency Division Multiplexing (OFDM) is a common modulation

scheme that divides a broadband channel into many parallel sub-channels. It has proven

to be a very efficient for the transmission in multi-path wireless channels. MIMO and

OFDM techniques combine to achieve high spectral efficiency and increased throughput.

A MIMO-OFDM system transmits independent OFDM modulated data from multiple

antennas at the same time. After OFDM demodulation, MIMO decoding extracts the

data from all the transmit antennas on all the sub-channels at the receiver. The major

processing blocks in a basic MIMO-OFDM transmitter include digital, mixed-signal, and

analog function as shown in Fig. 2.

[3] TECHNOLOGIES THAT ENABLE MIMO SYSTEMS

There are a number of significant technologies that allow MIMO systems to be used

realistically and to their maximum potential in applications.

Fig. 2. Major processing blocks in a basic MIMO-OFDM transmitter.

Fig. 3. MIMO mobile ad hoc network [3].

MIMO Wireless Cellular Networks

Today, there is a substantial interest in the study of MIMO wireless cellular networks.

Although MIMO communication networks have become very comprehensive, MIMO

cellular systems are not. This is mainly due to interference, multiple-access, mobility, &

resource management.

MIMO Mobile Ad-Hoc Networks

In contrast to wireless cellular applications, MIMO mobile ad-hoc networks as shown in

Fig. 3 provide the flexibility that is necessary for some wireless environments. This

occurs in networking frameworks that communicate between nodes directly without

utilizing an access point (peer-to-peer relationship). In MIMO communication, the

spatial degrees of freedom can be enhanced to support point-to-multipoint and

multipoint-to-point transmission which can be manipulated in physical and higher layer

protocols in ad hoc networks. Due to fluctuations in the wireless environment, the nodes

are able to organize themselves to the variations in the network topology. Currently,

mobile ad hoc networks have been receiving much attention primarily in military

applications.

Limited Feedback

In implementing MIMO systems, one must decide whether the channel estimation

information will be fed back to the transmitter to allow adaptation. Most MIMO

communication research has focused on systems without feedback because they are

simpler to implement. MIMO communication systems with limited feedback, allows

data to be easily achieved with lower complexity and error. A MIMO channel assures

multiplicative gain in capacity along with corresponding gains in channel reliability.

When the channel is known at the transmitter, information can be customized to the

subspace structure of the matrix channel. Fig. 4 demonstrates the concept of limited

feedback. This model estimates the critical parameters of the channel at the receiver,

perform quantization methods, and sends the parameters to the transmitter via a feedback

channel.

Fig. 4. Limited Feedback w/ MIMO

MIMO System Prototyping

MIMO System Prototyping provides access to actual propagation channels. Not to

mention it enhances simulations impairments i.e. timing jitters, gain/phase offset,

quantization effects, and etc… Prototyping is crucial to the comprehension of practical

wireless system operations.

Antenna Theory & Design

MIMO technology is allowed through the use of multiple transmit and receive antennas

in the communication link. Furthermore, these antenna arrays provide spatial diversity

from the propagation channel along with algorithms that can adapt to the various changes

of the channel. Arrays are designed through the use of conventional metrics from

antenna theory. In contrast, algorithms are built under basic assumptions about the

channel. Generally, the highest performance in MIMO communication systems requires

spacing the antennas far apart at multiples of the wavelength. However, some

applications require placing the antennas closer together e.g. cell phone and notebook

computers. Nevertheless, it is still possible to accomplish good quality performance by

adjusting the antennas so that they have minimum overlap between their patterns. At the

University of Texas, an experiment was performed to optimize antenna theory

performance metrics and communication theoretic metrics [3]. To simulate arbitrary

array geometries in MIMO channels, a computational electromagnetic (CEM)

performance simulator was developed. After interfacing the simulator with EM software

tools, measurements of the performance degradation of the design due to near-field

effects across the array elements was accomplished. The result of the experiment is

shown in Fig. 5. As a result, the circular patch array (CPA) produces significant

polarization/pattern diversity gains along with reduction in the physical size of the array

as compared to the conventional uniform linear array (ULA).

[4]MIMO CAPACITY

In a MIMO system, the total transmit power can be divided among multiple modes to

drive the capacity closer to the liner regime for each spatial path. As a result, the

collective spectral efficiency is increased. Capacity increases linearly at low SNR but

logarithmically at high SNR. Fig. 6 compares four different MIMO channel

matrices with flat singular-value distributions. The distribution of singular values is a

measure of the virtual effectiveness of different spatial paths through the channel. Fig. 6

shows that MIMO systems facilitate high spectral efficiency at a much lower required

energy per information bit.

[5]CHANNEL PHENOMENOLOGY

The phenomenology of the channel is important for capacity. One main aspect of the

channel that affects MIMO system capacity is channel complexity (a function of the

intensity of scatterers). Generally, capacity at high spectral efficiency improves as the

singular values of the channel matrix increase. To simulate the channel matrix, a simple

approach can be utilized by performing analytic calculations. This approach assumes that

all entries in the channel matrix are sampled from complex Gaussians H ~ G. Although

this approach is simple, it can provide a channel eigenvalue distribution that is too flat.

However, a diversity order characterization of the channel generated by spatial

correlation can be used to indicate an effective cut-off in the eigenvalue distribution. One

approach that examine spatial correlations uses the form

(2)

which results in a link-

by-link covariance matrix of the

Kronecker product form

(MLML†) (MRMR

†)*

(3)

Fig. 5. Measurements of performance degradation.

for the entries in the channel matrix H. Several conditions must be realized…

§ Scatterers are concerted around the transmitter and receiver.

§ Propagations are subjugated to multiple scattering of a specific kind.

§ Scatterers are adequately separated in angle when examined by its array.

[6] SPACE TIME CODES

To improve the performance of MIMO systems, space time codes that are suitable for

wireless communication systems are receiving major attention. These codes are used in

mapping the transmit signals to the multiple antennas. One approach to space-time

coding will be analyzed in which the transmitter is informed of the propagation channel

by the receiver to adjust its code appropriately. This approach offers large information-

theoretic capacity.

Block Orthogonal Codes

According to a specific format, the information bits are encoded in matrices that are

forced to lie in the class S. This class is defined by the property SS† that is proportional

to the identity matrix with a fixed proportionality constant [5]. The maximum-likelihood

decision for S is based on finding

(ZS†H†) (4)

which involves a linear function in the entries of S [5]. Where data is Z, channel matrix is

H, and S represents a set of matrix symbols. Linearity of the likelihood function

decouples decisions on the data symbols for some classes, e.g.

(5)

The information symbols s1 and s2 are sent needlessly over both channels and the

likelihood function is linear in each si, decoupling demodulation decisions [5].

[7] MISCONCEPTION OF MIMO SYSTEMS

The term “MIMO,” as described by engineers and researchers, refers to the use of

multiple, simultaneous signals in a frequency channel to develop multi-path propagation

and increase spectral efficiency [4]. Conversely, some manufacturers that claim MIMO

benefits are not using the term as described. Instead they use techniques that are often

confused with MIMO. Transmitter beam forming and receiver diversity can improve the

range for typical one-dimensional signals. As compared to MIMO, they are unsuccessful

in increasing capacity and data rated. In addition, beam forming techniques can create

hidden nodes and limits power consumption. Data compression and channel bonding are

more techniques that can be mistaken for MIMO. In contrast to MIMO communication

systems, data compression fails to substantially increase data throughput in many

practical networking applications. Moreover channel bonding interferes with other

devices in the same or nearby wireless networks. MIMO achieves substantially high data

rates without interference.

[8]

[9]CONCLUSION

Our Introduction to Communication System course focused on two separate areas of

communication systems. First, how they work. Second, how they behave in the presence

of noise. Specifically, we dealt with analog and digital communication systems. After

being introduced to Fourier analysis and probabilities, we studied amplitude angle

modulation for both linear and non-linear systems. We also studied sampling, pulse code

modulation, transmission of digital data, hybrid circuits, and recent developments in

communication systems.

These various topics that were studied have enhanced my knowledge of

communication systems as a whole. This course also served as a foundation for the study

of advanced systems and technology e.g. MIMO communication systems. MIMO has

shown tremendous development in the wireless industry. As a result, it is believed to be

the most valuable wireless development in this present era!

REFERENCES

[1] University of California The Non-Engineer’s Introduction to MIMO and MIMO-

OFMD [Online]. Available: http://www.mimo.ucla.edu

[2] Dave Borison, Airgo Networks. (2005). How MIMO multiplies Wireless Capacity

[Online]. Available: http://www.techbuilder.org/views/164302148

[3] Dr. Robert W. Health Jr. (2003). MIMO Wireless Inc. [Online]. Available:

http://www.mimowireless.com/

[4] Datacomm Research Company. (February 2005). MIMO Technology… Not All

Claims are Accurate [Online]. Available: www.datacommresearch.com

[5] Daniel W. Bliss, Keith W. Forsythe, Amanda M. Chan. (November 2005). MIMO

Wireless Communication [Online]. Volume (15). Available: http://www.ll.mit.edu/

[6] Santosh Kothamasu, “A wavelet based multiscale run-by-run controller form multiple

input multiple output (MIMO) processes,” 2004.

[7] Mohinder Jankiraman, “Space-time codes and MIMO systems,” 2004.