the mimo system summary

7/27/2019 The MIMO System Summary

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The MIMO System

As the communication system included transmitter and receiver with different

antenna allocation, there are a simple category of multi-antenna types:

Multi-antenna types

SISO

Single-input-single-output means that

the transmitter and receiver of the radiosystem have only one antenna.

SIMO

Single-input-multiple-output means thatthe receiver has multiple antennas while

the transmitter has one antenna.

MISO

Multiple-input-single-output means that

the transmitter has multiple antennaswhile the receiver has one antenna.

MIMO

Multiple-input-multiple-output means thatthe both the transmitter and receiver have

multiple antennas.

MIMO is the use of multiple antennas at both the transmitter and

receiver to improve communication performance.

The simple overview of MIMO:

Multiple data streams transmitted in a single channel at the same time

Multiple radios collect multipath signals

Delivers simultaneous speed, coverage, and reliability improvements

MIMO

exploits the spacedimension to improve wireless systems capacity,

range and reliability.

It offers significant increases in data throughput and link range without

additional bandwidth or increased transmit power.


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As the number of antenna element increasing, the channel capacity is increased

too. Instead of logarithmic-increasing of channel capacity in SIMO and MISO system,

the MIMO system owned linear-increasing of channel capacity as antenna increased.

The improving of MIMO from SIMO and MISO is shown below:

1.Types of MIMO System(1)Single User MIMO (SU-MIMO) vs. Multi User MIMO (MU-MIMO)

(2)Open loop MIMO vs. Close loop MIMO

3.1 Single User MIMO (SU-MIMO) vs. Multi User MIMO (MU-MIMO)

Single User MIMO (SU-MIMO):

When the data rate is to be increased for a single UE, this is called Single User

MIMO (SU-MIMO).


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Multi User MIMO (MU-MIMO):

When the individual streams are assigned to various users, this is called Multi

User MIMO (MU-MIMO). This mode is particularly useful in the uplink because the

complexity on the UE side can be kept at a minimum by using only one transmit

antenna. This is also called 'collaborative MIMO'.

3.2 Open loop MIMO vs. Close loop MIMO

Open Loop MIMO techniques- the commonly used MIMO terminology

Closed Loop MIMO techniques- also known as Transmitter Adaptive

Antenna (TX-AA) techniques, referred to by the industry as"beamforming".

Space Time Transmit Diversity (STTD) MIMO

Space-time block coding based transmit diversity(STTD)-a method

oftransmit diversityused inUMTSSthird-generationcellular systems.


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Spatial Multiplexing (SM) MIMO

Spatial multiplexing- transmission techniques inMIMOwireless

communicationto transmit independent and separately encoded data

signals, so-calledstreams, from each of the multiple transmit antennas.

n

Uplink Collaborative MIMO

Collaborative Spatial Multiplexing (Collaborative MIMO)-comparable to regular

spatial multiplexing, where multiple data streams are transmitted from multiple

antennas on the same device.

an additional open-loop MIMO technique consider by WiMAX vendors

to increase the spectral efficiency and capacity of the uplink


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communications path.

Spatial Multiplexing MIMO: Uplink Collaborative MIMO:

Close loop MIMO:

Antenna technologies are the key in increasing network capacity. It started with

sectorized antennas. These antennas illuminate 60 or 120 degrees and operate as

one cell. In GSM, the capacity can be tripled, by 120 degree antennas. Adaptive

antenna arrays intensify spatial multiplexing using narrow beams. Smart antennas

belong to adaptive antenna arrays but differ in their smart direction of arrival (DoA)

estimation. Smart antennas can form a user-specific beam. Optional feedback can

reduce complexity of the array system.

Beamforming is the method used to create the radiation pattern of an antenna

array. It can be applied in all antenna array systems as well as MIMO systems.

Smart antennas are divided into two groups:

Phased array systems (switched beamforming) with a finite number of fixed

predefined patterns

Adaptive array systems (AAS) (adaptive beamforming) with an infinite number of

patterns adjusted to the scenario in real time

Switched Beamformer Adaptive Beamformer


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Switched beamformers-electrically calculate the DoA and switch on the fixed

beam.

Adaptive beamformer-deals with that problem and adjusts the beam in real-

time to the moving UE.

2.Function of MIMO SystemMIMO can be sub-divided into three main categories:

(1)Precoding

(2)Spatial multiplexing

(3)Diversity coding

Precoding:

Precodingis a generalization ofbeamformingto support multi-layer transmission

inmulti-antennawireless communications.

The Precoding can be separated by two classifications:

Precoding for Single User MIMO

Precoding for Multi User MIMO

Precoding for Single User MIMO

In single user multiple-input multiple-output (MIMO) systems, a transmitter

equipped with multiple antennas communicates with a receiver that has multiple

antennas.

Most classic precoding results assumenarrowband,slowly fadingchannels,

meaning that the channel for a certain period of time can be described by a single

channel matrix which does not change faster.

The precoding strategy that maximizes the throughput, calledchannel capacity,

depends on thechannel state informationavailable in the system.

Precoding for Multi User MIMO

Inmulti-user MIMO, a multi-antenna transmitter communicates simultaneously


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with multiple receivers (each having one or multiple antennas). This is known

asspace-division multiple access(SDMA).

From an implementation perspective, precoding algorithms for SDMA systems

can be sub-divided into linear and nonlinear precoding types.

The capacity achieving algorithms are nonlinear, but linear precoding approaches

usually achieve reasonable performance with much lower complexity.

Nonlinear precoding is designed based on the concept ofdirty paper

coding(DPC), which shows that any known interference at the transmitter can be

subtracted without the penalty of radio resources if the optimal precoding scheme

can be applied on the transmit signal.

Spatial multiplexing:

Spatial multiplexingrequires MIMO antenna configuration. In spatial

multiplexing, a high rate signal is split into multiple lower rate streams and each

stream is transmitted from a different transmit antenna in the same frequency

channel.

Diversity coding:

Diversity Codingtechniques are used when there is nochannel knowledgeat

the transmitter. In diversity methods, a single stream (unlike multiple streams inspatial multiplexing) is transmitted, but the signal is coded using techniques called

space-time coding.

MIMO Channel Model

Diagram of a MIMO wireless transmission system is shown below:

Here is a MIMO system model:


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There are detail explains for denoted symbols:

r is the Mx1 received signal vector as there are M antennas in receiver.

H represented channel matrix

s is the Nx1 transmitted signal vector as there are N antennas in transmitter

n is an Mx1 vector of additive noise term

Let Qdenote the covariance matrix of x, then the capacity of the system

described by information theory as below:

This is optimal when is unknown at the transmitter and the input distribution

maximizing the mutual information is the Gaussian distribution.

It is important to note that can be rewritten as:

Where 1 , 2 , , m are the nonzero eigenvalues ofW, m=min(M,N), and

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 singular value decomposition (SVD) ofH. Let the SVD be

given by


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Then U and V are unitary and D=diag( , , , , 0 , , 0). Hence theMIMO signal model can be rewritten as:

When the channel is known at the transmitter (and at the receiver), then H is

known in above equation and we optimize the capacity over Qsubject to the power

constraint tr(Q). Fortunately, the optimal Qin this case is well known and is

called a water filling solution.

There is a simple algorithm to find the solution and the resulting capacity is

given by

Where is chosen to satisfy

If the transmitter has only statisticalchannel state information, then the

ergodicchannel capacitywill decrease as the signal covarianceQcan only be

optimized in terms of the averagemutual informationas

The spatial correlation of the channel has a strong impact on the ergodic channel

capacity with statistical information.

If the transmitter has no channel state information it can select the signal

covariance Qto maximize channel capacity under worst-case statistics, which

means Q=(1/Nt )*I and accordingly

Additional information: Fundamental Capacity theorem
http://en.wikipedia.org/wiki/Spatial_Correlationhttp://en.wikipedia.org/wiki/Channel_capacityhttp://en.wikipedia.org/wiki/Channel_capacityhttp://en.wikipedia.org/wiki/Channel_state_informationhttp://en.wikipedia.org/wiki/Channel_state_informationhttp://en.wikipedia.org/wiki/Channel_capacityhttp://en.wikipedia.org/wiki/Channel_capacityhttp://en.wikipedia.org/wiki/Spatial_Correlation


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For a SISO system the capacity is given by

Where h is the normalized complex gain of a fixed wireless channel or that of a

particular realization of a random channel. is the SNR at any RX antenna. As we

deploy more RX antennas the statistics of capacity improve and with M RX antennas,

we have a SIMO system with capacity given by

Similarly, if we opt for transmit diversity, in the common case, where the transmitter

does not have channel knowledge, we have a MIMO system with N TX antennas and

the capacity is given by

For N TX and M RX antennas, we have the now famous capacity equation:

where (*) means transpose-conjugate and is the channel matrix.

3.Application of MIMO SystemThe 3GPP mobile radio standard (UMTS) has undergone numerous phases of

development. Starting with WCDMA, various data acceleration methods have been

introduced, including HSDPA and HSUPA. The newest releases cover HSPA+ and Long

Term Evolution (LTE).


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HSPA+ (3GPP Release 7/8):

A transmit diversity mode had already been introduced in Release 99 (WCDMA).

Release 7 of the 3GPP specification (HSPA+) expanded this approach to MIMO and again

increased the data rate with respect to Release 6 (HSDPA).

LTE (3GPP Release 8):

UMTS Long Term Evolution (LTE) was introduced in 3GPP Release 8. The

objective is a high data rate, low latency and packet optimized radio access

technology. LTE is also referred to as E-UTRA (Evolved UMTS Terrestrial Radio

Access) or E-UTRAN (Evolved UMTS Terrestrial Radio Access Network).


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Downlink

In LTE, one or two code words are mapped to one to four layers ("layer mapper"

block). To achieve multiplexing, a precoding is carried out ("precoding" block).

Table 1 shows the code book for spatial multiplexing with two antennas as an

example. Code books for four antennas are also defined.

LTE precoding matrix for a maximum of two layers:

Uplink

In order to keep the complexity low at the UE end, MU-MIMO is used in the uplink. To do

this, multiple UEs, each with only one Tx antenna, use the same channel.

WiMAXTM

(802.16e-2005):

WiMAXTM

promises a peak data rate of 74 Mbps at a bandwidth of up to 20

MHz. Modulation types are QPSK, 16QAM, and 64QAM.

Downlink

The WiMAXTM 802.16e-2005 standard specifies MIMO in WirelessMAN-OFDMA


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mode. This standard defines a large number of different matrices for coding and

distributing to antennas.

Uplink

In Uplink-MIMO only different pilot patterns are used. Coding and mapping is

the same like in non-MIMO case.

WLAN (802.11n):

WLAN as defined by the 802.11n standard promises a peak data rate of up to 600 Mbps

at a bandwidth of 40 MHz. Modulation types are BPSK, QPSK, 16QAM, and 64QAM.

WLAN differentiates between spatial streams (SS) and space-time streams

(STS). If NSS < NSTS, then a space-time block encoder ("STBC") distributes the SS to the

STS and adds transmit diversity by means of coding.

Conclusion

The presentation introduces the major feature of MIMO links for use in wireless

network. MIMO exploits the spacedimension to improve wireless systems capacity,range and reliability. It offers significant increases in data throughput and link range

without additional bandwidth or increased transmit power.

After introduced why MIMO system, we classified MIMO system into two major

categories: (1) Single User MIMO (SU-MIMO) vs. Multi User MIMO (MU-MIMO)

(2)Open loop MIMO vs. Close loop MIMO. Under open loop MIMO, three MIMO

system is provided: (1) Space Time Transmit Diversity (STTD) MIMO (2)Spatial

Multiplexing (SM) MIMO (3)Uplink Collaborative MIMO.


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Followed, we introduce the functions of MIMO system included (1) Precoding (2)

Spatial multiplexing (3)Diversity coding. Precoding is a generalization

ofbeamformingto support multi-layer transmission inmulti-antennawireless

communications. In spatial multiplexing, a high rate signal is split into multiple lower

rate streams and each stream is transmitted from a different transmit antenna in the

same frequency channel. Diversity Codingtechniques are used when there is

nochannel knowledgeat the transmitter.

Then a strict mathematics model of MIMO system is provided. While the MIMO

system is regarded as narrow flat fading channel, we modeled the MIMO system by

referring toinformation theory. Then we derived the channel capacity in mathematical

description.

In section 6, current applications of MIMO technique is written. Under 3GPP

mobile radio standard, there are several application included: (1) HSPA+ (2)LTE (3)

WiMAXTM

(4)WLAN.

At last, Future standards with using of MIMO technology is provided include LTE

Advanced, 1xEV-DO Rev. C and WiMAXTM

802.16m.

4.Reference[1] Wikipedia: MIMO, Precoding, Spatial multiplexing, Diversity Coding,

WiMAX MIMO, information theory, channel capacity.

*2+ ROHDE&SCHWARZ, Introduction to MIMO: Application Note

*3+ D. Gesbert, M. Shafi, D. S. Shiu, P. Smith, and A. Naguib, From theory to practice:

An overview of MIMO space-tim coded wireless systems, IEEE J. Select. Areas

Commun. Special Issue on MIMO Systems, pt. I, vol. 21, pp. 281302, Apr. 2003.

[4] A. J. Paulraj et al., An Overview of MIMO Communications a Key to Gigabit

Wireless, Proc. IEEE, vol. 92, no. 2, Feb. 2004, pp. 198218.

[5] Q. Li, G. Li, W. Lee, M. il Lee, D. Mazzarese, B. Clerckx, and Z. Li, MIMO

techniques in WiMAX and LTE: a feature overview, IEEE Commun. Magazine,

vol. 48, no. 5, pp. 8692, May. 2010.

[6] G. Bauch, MIMO Technologies for the Wireless Future, Proc. International


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symposium on Personal Indoor and Mobile Radio Communications, Cannes

France, Sept., 2008

[7] PPT slide: Dr. Jacob Sharony, Introduction to Wireless MIMO Theory and

Applications, IEEE LI, November 15, 2006

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