Tutorial:Multiple-Input and Multiple-Output (MIMO) System
AnalysisR00943129
Outline:1. Abstract p.22. Introduction of MIMO System.p.33.
Types of MIMO System p.54. Function of MIMO System .p.75. MIMO
Channel Model ..p.116. Application of MIMO System p.157. Future
Work .p.198. Conclusion .p.199. Reference ..p.201. AbstractDigital
communication using multiple-input-multiple-output (MIMO) has been
regarded as one of the most significant technical breakthrough
modern communications. In this tutorial, the overview of recent
progress in the area of MIMO system is introduced. A key feature of
MIMO system is the ability to turn multi-path propagation,
traditionally a pitfall of wireless transmission, into a benefit
for the user. The first part of the tutorial introduced MIMO system
and analyzed why MIMO system. Followed, the section 3 used two
major classifications to determine types of MIMO. From single user
to multi users, open loop to close loop, there are literally
descriptions to figure out category of MIMO system. Beside, several
different open loop MIMO systems include Space Time Transmit
Diversity (STTD) MIMO, Spatial Multiplexing (SM) MIMO and Uplink
Collaborative MIMO are introduced. Coming to the function of MIMO
system, I separated it to three parts to illustrate. 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. At the end of this tutorial report I briefly
conclude the content of this report follow the section description.
I present the stat of the art in channel modeling and measurement,
leading to a better understanding of actual MIMO gains. Although
MIMO system does not related to my research topic, I do try my best
to survey the MIMO system knowledge and put all of the information
to this tutorial. Hoping it can take the reader to understanding
how MIMO system work.
2. Introduction of MIMO SystemBefore the explaining of Why MIMO
System, it is necessary to briefly talking about the definition of
MIMO. As the communication system included transmitter and receiver
with different antenna allocation, there are a simple category of
multi-antenna types:
Multi-antenna types
SISOSingle-input-single-output means that the transmitter and
receiver of the radio system have only one antenna.
SIMOSingle-input-multiple-output means that the receiver has
multiple antennas while the transmitter has one antenna.
MISOMultiple-input-single-output means that the transmitter has
multiple antennas while the receiver has one antenna.
MIMOMultiple-input-multiple-output means that the 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. So why need MIMO
system? The wireless system before MIMO is been constrained by
network capacity which is related with channel quality and
coverage. To see how problem occurred, we need to talk about the
transmission on a multipath channel. In wireless communication the
propagation channel is characterized by multipath propagation due
to scattering on different obstacle. The multipath problem is a
typical issue in communication system with time variations and time
spread. For time variations the channel is fading and caused SNR
variations. For time spread, it becomes important for suitable
frequency selectivity. In an urban environment, these signals will
bounce off trees, buildings, etc. and continue on their way to
their destination (the receiver) but in different directions. With
MIMO, the receiving end uses an algorithm or special signal
processing to sort out the multiple signals to produce one signal
that has the originally transmitted data. 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 space dimension 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. MIMO achieves this goal by spreading the
same total transmit power over the antennas to achieve anarray
gainthat improves the spectral efficiency (more bits per second per
hertz of bandwidth) or to achieve adiversity gainthat improves the
link reliability (reducedfading). 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:
3. Types of MIMO SystemThere are two major classifications to
determine types of MIMO:(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).
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 MIMOTextbook MIMO
configurations are represented as either "Open Loop" or "Closed
Loop". In application, the commonly used MIMO terminology has most
often been in reference to Open Loop MIMO techniques. Closed Loop
MIMO techniques, also known as Transmitter Adaptive Antenna (TX-AA)
techniques, are simply referred to by the industry as
"beamforming".
Open loop MIMO:With Open Loop MIMO, the communications channel
does not utilize explicit information regarding the propagation
channel. Common Open Loop MIMO techniques include Space Time
Transmit Diversity (STTD), Spatial Multiplexing (SM) and
Collaborative Uplink MIMO.Space Time Transmit Diversity (STTD)
MIMOSpace-time block coding based transmit diversity(STTD) is a
method oftransmit diversityused inUMTSSthird-generationcellular
systems. STTD is optional in theUTRANNair interface but mandatory
for user equipment. STTD utilizesspace-time block code(STBC) in
order to exploit redundancy in multiply transmitted versions of a
signal. The same data is coded and transmitted through different
antennas, which effectively doubles the power in the channel. This
improves Signal Noise Ratio (SNR) for cell edge performance.
Spatial Multiplexing (SM) MIMOSpatial multiplexingis
transmission techniques inMIMOwireless communicationto transmit
independent and separately encoded data signals, so-calledstreams,
from each of the multiple transmit antennas. Therefore, the space
dimension is reused, ormultiplexed, more than one time. SM delivers
parallel streams of data to CPE by exploiting multi-path. It can
double (2x2 MIMO) or quadruple (4x4) capacity and throughput. SM
gives higher capacity when RF conditions are favorable and users
are closer to the BTS.
Short Summary: STTD vs. SMSTTD outperforms SM when SNR is weak
whereas when SNR is higher SM is well suited. STTD improves the SNR
for cell edge users while SM provided higher capacity when user are
in good RF condition and are closer to the radio tower. An ideal
wireless system employing MIMO techniques will support both STTD
and SM. The system will calculate an optimal switching point and
dynamically shift between the two approaches to offer the necessary
coverage or capacity gain demanded from the network at any given
time or location.
Uplink Collaborative MIMOCollaborative Spatial Multiplexing
(Collaborative MIMO) is comparable to regular spatial multiplexing,
where multiple data streams are transmitted from multiple antennas
on the same device. It is an additional open-loop MIMO technique
consider by WiMAX vendors to increase the spectral efficiency and
capacity of the uplink communications path. A practical realization
of this technique would allow for two separate end-users 'WiMAX'
devices, each having a single transmit lineup, to utilize the same
frequency allocation to communicate with the dual-antenna WiMAX
base station. With this technique two devices (having only
transmitted antenna each) can collaboratively transmit on the same
sub-channel which can increase the uplink capacity. Spatial
Multiplexing MIMO: Uplink Collaborative MIMO:
Close loop MIMO:Antenna technologies are the key in increasing
network capacity. It started withsectorized 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
timeSwitched BeamformerAdaptive Beamformer
Switched beamformers electrically calculate the DoA and switch
on the fixed beam. The user only has the optimum signal strength
along the center of the beam. The adaptive beamformer deals with
that problem and adjusts the beam in realtime to the moving UE. The
complexity and the cost of such a system is higher than the first
type.
4. 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. In conventional single-layer beamforming, the same
signal is emitted from each of the transmit antennas with
appropriate weighting such that the signal power is maximized at
the receiver output. When the receiver has multiple antennas,
single-layer beamforming cannot simultaneously maximize the signal
level at all of the receive antennas. Thus, in order to maximize
the throughput in multiple receive antenna systems, multi-layer
beamforming is required. The benefits of beamforming are to
increase the received signal gain, by making signals emitted from
different antennas add up constructively, and to reduce the
multipath fading effect. The Precoding can be separated by two
classifications: Precoding for Single User MIMO Precoding for Multi
User MIMOPrecoding for Single User MIMOIn 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. In practice, such channels can
be achieved, for example, throughOFDM. The precoding strategy that
maximizes the throughput, calledchannel capacity, depends on
thechannel state informationavailable in the system.Precoding for
Multi User MIMOInmulti-user MIMO, a multi-antenna transmitter
communicates simultaneously 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. Linear precoding
strategies include MMSE precoding and the
simplifiedzero-forcing(ZF) precoding. There are also precoding
strategies tailored for low-ratefeedback ofchannel state
information, for example random beamforming. 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. If
these signals arrive at the receiver antenna array with
sufficiently different spatial signatures, the receiver can
separate these streams into (almost) parallel channels. Spatial
multiplexing is a very powerful technique for increasing channel
capacity at higher signal-to-noise ratios (SNR). The maximum number
of spatial streams is limited by the lesser of the number of
antennas at the transmitter or receiver. Spatial multiplexing can
be used with or without transmit channel knowledge. Spatial
multiplexing can also be used for simultaneous transmission to
multiple receivers, known asspace-division multiple accessing. The
scheduling of receivers with different spatial signatures allows
good separability.Diversity coding:Diversity Codingtechniques are
used when there is nochannel knowledgeat the transmitter. In
diversity methods, a single stream (unlike multiple streams in
spatial multiplexing) is transmitted, but the signal is coded using
techniques called space-time coding. The signal is emitted from
each of the transmit antennas with full or near orthogonal coding.
Diversity coding exploits the independent fading in the multiple
antenna links to enhance signal diversity. Because there is no
channel knowledge, there is no beamforming orarray gainfrom
diversity coding.5. MIMO Channel ModelDiagram of a MIMO wireless
transmission system is shown below:
The transmitter and receiver are equipped with multiple antenna
elements. The transmit stream go through a matrix channel which
consists of multiple receive antennas at the receiver. Then the
receiver gets the received signal vectors by the multiple receive
antennas and decodes the received signal vectors into the original
information. Here is a MIMO system model:
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 termLet Q denote the covariance matrix of x, then
the capacity of the system described byinformation theory as
below:
This is optimal when is unknown at the transmitter and the input
distribution maximizing the mutual information is the Gaussian
distribution. With channel feedback may be known at the transmitter
and the optimal is not proportional to the identity matrix but is
constructed from a water filling argument as discussed later. The
form of equation gives rise to two practical questions of key
importance. First, what is the effect of Q? If we compare the
capacity achieved by and the optimal Q based on perfect channel
estimation and feedback, then we can evaluate a maximum capacity
gain due to feedback. The second question concerns the effect of
the H 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, correlated 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 can be
rewritten as:
Where 1 , 2 , , m are the nonzero eigenvalues of W, 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) of H. Let the SVD be given
by
Then U and V are unitary and D=diag( , , , , 0 , , 0). Hence the
MIMO signal model can be rewritten as:
The above equation represents the system as m equivalent
parallel SISO eigen- channels with signal powers given by the
eigenvalues 1 , 2 , , m. Hence, the capacity can be rewritten in
terms of the eigenvalues of the sample covariance matrix W. For
general W matrices a wide range of limiting results are known as or
both tend to infinity. In the particular case of Wishart matrices,
many exact results are also available. 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 two key questions posed above; what is the effect of
feedback and what is the impact of the channel?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 Q subject to the
power constraint tr(Q). Fortunately, the optimal Q in 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
+ denotes taking only those terms which are positive. Since is a
complicated nonlinear function of 1 , 2 , , m, the distribution of
WCF appears intractable, even in the Wishart case when the joint
distribution of 1 , 2 , , m is known. 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
Thespatial correlationof the channel has a strong impact on the
ergodicchannel capacitywith statistical information.If the
transmitter has nochannel state informationit can select the signal
covarianceQto maximize channel capacity under worst-case
statistics, which meansQ=(1/Nt )*Iand accordingly
Additional information: Fundamental Capacity theoremFor 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
Where hi is the gain for RX antenna i. Note the crucial feature
of above equation in that increasing the value of M only results in
a logarithmic increase in average capacity. 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 byWhere the normalization by N
ensures a fix total transmitter power and shows the absence of
array gain in that case. Again, note that capacity has a
logarithmic relationship with N. Now, we consider the use of
diversity at both transmitter and receiver giving rise to a MIMO
system. For N TX and M RX antennas, we have the now famous capacity
equation:
where (*) means transpose-conjugate and is the channel
matrix.
6. 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).
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). The
introduction of 64QAM modulation and MIMO in the downlink makes a
peak data rate of 28 Mbps (Rel. 7) possible. In Rel. 7 MIMO and
64QAM can not be used simultaneously. Since Rel. 8 the simultaneous
use is possible which leads to peak data rates up to 42 Mbps.
Uplink MIMO is not provided. MIMO was introduced in the form of a
double transmit antenna array (D-TxAA) for the high speed downlink
shared channel (HS-DSCH).
With D-TxAA, two independent data streams can be transmitted
simultaneously over the radio channel using the same WCDMA
channelization codes. The two data streams are indicated with blue
and green color in Figure 11. After spreading and scrambling,
precoding based on weight factors is applied to optimize the signal
for transmission over the mobile radio channel. Four precoding
weights w1 to w4 are available. The first stream is multiplied with
w1 and w2, the second stream is multiplied with w3 and w4. The
weights can take the following values:
Note that w1 is always fixed, and only w2 can be selected by the
base station. Weights w3 and w4 are automatically derived from w1
and w2, because they have to be orthogonal. The base station
selects the optimum weight factors based on proposals reported by
the UE in the uplink. In addition to the use of MIMO in HS-DSCH,
the weight information must be transmitted to the UE via the
HS-SCCH control channel. Although MIMO is not provided in the
uplink, MIMO-relevant information still does have to be transmitted
in the uplink. The UE sends a precoding control indication (PCI)
and a channel quality indication (CQI) in the HS-DPCCH, which
allows the base station to adapt the modulation, coding scheme, and
precoding weight to the channel conditions.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). The basic concept for LTE in downlink is
OFDMA (Uplink: SC-FDMA), while MIMO technologies are an integral
part of LTE. Modulation modes are QPSK, 16QAM, and 64QAM. Peak data
rates of up to 300 Mbps (4x4 MIMO) and up to 150 Mbps (2x2 MIMO) in
the downlink and up to 75 Mbps in the uplink are
specified.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). In this process, the layers are
multiplied by a precoding matrix W from a defined code book and
distributed to the various antennas. This precoding is known to
both the transmitter and the receiver. In the specification, code
books are defined for one, two, and four antennas, as well as for
spatial multiplexing (with and without CDD) and transmit diversity.
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:
UplinkIn 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.DownlinkThe WiMAXTM 802.16e-2005 standard
specifies MIMO in WirelessMAN-OFDMA mode. This standard defines a
large number of different matrices for coding and distributing to
antennas. In principle, two, three or four TX antennas are
possible. For all modes, the matrices A, B, and C are available. In
the "STC encoder" block, the streams are multiplied by the selected
matrix and mapped to the antennas.
UplinkIn Uplink-MIMO only different pilot patterns are used.
Coding and mapping is the same like in non-MIMO case. In addition
to single user MIMO (SU-MIMO) two different user can use the same
channel (collaborative MIMO, MU-MIMO).
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. It is backward
compatible with the previous standards 802.11 a/b/g. With up to
four streams, it supports up to a maximum of four antennas.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.
7. Future WorkFuture standards will continue to use MIMO
technology. At present, the following standards with MIMO are being
worked on: LTE Advanced :The goal is to provide 1 Gbps at 100 MHz
bandwidth in downlink direction. 1xEV-DO Rev. C:The goal is to
provide 18 Mbps at 1.25 MHz bandwidth in forward link. WiMAXTM
802.16m:The goal is to provide 300 Mbps at 20 MHz bandwidth in
downlink direction.8. ConclusionThis tutorial introduces the major
feature of MIMO links for use in wireless network. MIMO exploits
the space dimension 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.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.
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multiplexing, Diversity Coding, WiMAX MIMO, information theory,
channel capacity.[2] ROHDE&SCHWARZ, Introduction to MIMO:
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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.
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[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 symposium on Personal Indoor and Mobile Radio
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Sharony, Introduction to Wireless MIMO Theory and Applications,
IEEE LI, November 15, 2006