International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 13 - Apr 2014
A Review on Precoding MIMO Technique
Shashi Shukla#1, Saurabh MitraSecond Author*2
#1
Mtech Scholar Electronics and Communication Engg.Dr C V Raman University Bilaspur *2Saurabh Mitra Assistant Professor
Electronics and Communication Engg Dr C V Raman University Bilaspur India
Abstract— With the integration of Internet and multimedia
applications in next generation wireless communications, the
demand for wide-band high data rate communication services is
growing. In recent years, multiple transmitter and receiver
antennas are employed in the wireless communications systems
to adapt various demands of high speed wireless links and
improved signal to noise ratio . In order to takefull advantage of
the Multi Input Multi Output (MIMO) systems, precoding
technique is needed at the transmitter side. Many precoding
techniques exist in literature. In this paper a brief review of
previous work which done with precoding for mimo system is
disscussed.
Keywords—Channel
state information, MIMO System
,precoding, space time block coding,diversity.
I.
Introduction
The Multi Input Multi Output (MIMO) systems used in a rich
scattering environment for wireless communications improve
the reliability or the data rate of transmissions significantly in
comparison with Single Input Single Output (SISO) systems.
These systems achieve large capacity and diversity gains in
comparison with single transmitter and single receiver
systems [1]. Through a feedback link, the channel knowledge
can be made available at the transmitter and precoding
techniques can be used to adapt the information signal to the
channel and significantly improve the performance of MIMO
communication. If full channel state information is considered
at the transmitters, linear precoder can be designed according
to various criteria, such as ergodic capacity, received signal to
noise ratio (SNR) or error probability [2].
The benefits of using multiple antennas at both the
transmitter and the receiver in a wireless system are well
established. Multiple-input multiple-output (MIMO) systems
enable a growth in transmission rate linear in the minimum
number of antennas at either end. MIMO techniques also
enhance link reliability and improve coverage.While the
benefits of MIMO are realizable when the receiver alone
knows the communication channel, these can be further
improved by transmitting the channel information at
transmitter. The importance of transmit channel knowledge
can be significant. For instance, in a four-transmit two-receive
antenna system with independent identically distributed (i.i.d.)
Rayleigh flat-fading,transmit channel knowledge can more
than double the capacity at−5dB (SNR) signal-to-noise ratio
and add 1.5 b/s/Hz additional capacity at 5 dB SNR. Such
SNR choices are common in practical systems such as WiFi
and WiMax applications. In a non-i.i.d.channel, channel
knowledge at the transmitter offers even greater leverage in
ISSN: 2231-5381
performance.So, exploiting transmit channel side information
is of great practical interest in MIMO wireless.
Precoding is a processing method that exploits CSIT(channel
state information) by operating on the signal before
transmission.
II.
MIMO and Precoding
A standard 2 × 2 MIMO spatial multiplexing scheme, shown
in Figure 1 (a), assumes the wireless channel will provide four
separate connections between transmit and receive antennas.
Each channel connection, shown as an arrow in the figure,
represents a unique combination of all transmission paths
including the direct Line of Sight (LOS) path, should one exist,
and the numerous multi-paths created by reflection, scattering
and diffraction from the surrounding environment.Depending
on the resulting channel conditions,the MIMO system may not
be able to properly recover the transmitted data streams(layers)
if the SNR is too low at any of the receive antennas.With the
addition of Precoding,as shown in Figure 1(b), the transmitter,
Figure.1simplified block diagram showing the differences between
(a)MIMO without precoding(b)MIMO with precoding
Having knowledge of the current channel conditions, can
effectively combine the layers before transmission with the
goal of equalizing the signal reception across the multiple
receive antennas. Precoding schemes have been specified for
spatially-multiplexed and transmit-diversity applications [5].
Precoding is based on transmit beam-forming concepts with
the provision of allowing multiple beams to be simultaneously
transmitted in the MIMO system. The LTE specification
defines a set of complex weighting matrices for combining the
layers before transmission using up to 4 × 4antenna
configurations [5]. For a2 × 2 configuration, the weighting
matrix, W, is multiplied by the input layers to generate the
precoded signals to be transmitted.
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(2.1)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 13 - Apr 2014
Here,
are the input layers prior to precoding (q = 0, 1)
and
are the precoded signals applied to each transmit
antenna. The simplest precoding matrix maps each layer to a
single antenna dedicated to transmitting that layer, without
any coupling to other antennas. In this case,the weighting
matrix, defined with codebook index 0,
(2.2)
resulting in the following transmitted data as
codes with the smallest memory sizes and asynchronous full
cooperative diversity [9].
B. Weighted MMSE criteria
H. Sampath, P. Stoica, and A. Paulraj [3] proposed designing
jointly optimum linear precoder and decoder for a MIMO
channel with and without delay-spread, using the weighted
minimum mean-squared error (MMSE) criterion subject to a
transmit power constraint. The optimum linear precoder and
decoder maximizes information rate or minimizes the sum of
the output symbol estimation errors.
(2.3)
A second precoding matrix,defined with codebook index 1,
provides linear combination of the sums and differences of
the two input layers respectively. The weighting matrix for
codebook 1 is
Fig 2.MIMO Communication System
In this method he input bit streams are first coded and
modulated. The latter are than passed through the linear
precoder which is optimized for a fixed and known channel.
resulting in the following transmitted data
The precoder is a matrix with complex elements and adds
redundancy to the input symbol streams to improve system
performance. The precoder output is launched into the MIMO
channels through MT transmit antennas. The signals are
(2.5) received by MR receive antennas and processed by linear
decoder, which is optimized for the fixed and known channel.
The linear decoder also operates in the complex field and
III.
Literature Review
removes any redundancy that has been introduced by the
precoder.
A. Trellis code Method
This design method does not consider the coding and
modulation, but instead focuses only on the design of the
Y.Shang and X.-G. Xia,July 2006 introducedTo achieve full linear precoder and decoder. The generalized jointly optimum
cooperative diversity in a relay network, most of the existing linear precoder and decoder minimizes any weighted sum of
space-time coding schemes require the synchronization symbol estimation errors assuming total transmit power
between terminals. A family of space-time trellis codes that constraint across all transmit antennas.
The main goal of this method involves the design
achieve full cooperative diversity order without the
assumption of synchronization has been recently proposed. It ofprecoder (F) and decoder (G) matrices. In this paper, the
has been shown that the construction of such a family is design of equal-error and MMSEsystems are also compared.
equivalent to the construction of binary matrices that have full These two designs have similar total average BERs. The main
row rank no matter how their rows are shifted, where a row drawback of this system is that it is suitable for limited range
corresponds to a terminal (or transmit antenna) and its length of the bit streams only.
corresponds to the memory size of the trellis code on that
terminal. Such matrices are known as shift-full-rank (SFR) C. Space-Time Linear Precoder
matrices. A family of SFR matrices has been also constructed, A. Scaglione, P. Stoica, S. Barbarossa, G. Giannakis, and H.
but the memory sizes of the corresponding space-time trellis Sampath [5] introduced a new model for the design of
codes (the number of columns of SFR matrices) grow transmitter space-time coding that is referred to as linear
exponentially in terms of the number of terminals (the number precoding. The design model is based on an optimal pair of
of rows of SFR matrices), which may cause a high decoding linear transformations precoder (F) and decoder of blocks of
complexity when the number of terminals is not small. This the transmit symbols and receive samples, respectively. This
paper construct SFR matrices of any sizes for any number of operates jointly and linearly on the time and space
terminals. Furthermore, the author construct shortest (square) dimensions. The design targets different criteria of optimality
SFR (SSFR) matrices that correspond to space-time trellis and constraints, assuming that the channel is known both at
(2.4)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 13 - Apr 2014
the transmitter and receiver ends. Channel State Information
(CSI) can be acquired at the transmitter either if a feedback
channel is present or when the transmitter and receiver operate
in time division duplex (TDD) so that the timeinvariant
MIMO channel transfer functions is the same in both
ways.The specific design targets minimization of the symbol
mean square error and the approximate maximization of the
minimum distance between symbol hypotheses under average
and peak power constraints. The first design corresponds to
the MMSE criterion, whereas the second one is proved to be
equivalent to the maximization of the mutual information
between transmitter and receiver. The optimal design allow us
to define space-time modulation design that takes advantage
of the channel state information and offers simple closed-form
solutions, scalable with respect to the number of antennas,
size of the coding block, and transmit average/peak power.
D. Alamouti OFDM
Y.Li and X.-G.Xia Sep.4-9 2005,preposed an Alamouti coded
orthogonal frequency-division multiplexing (OFDM) scheme
for a cooperative communication system robust to both timing
errors and frequency offsets. OFDM with cyclic prefix (CP) is
used to combat timing errors. In order to mitigate the inter
carrier interference (ICI) caused by multiple frequency offsets
in the cooperative system, an ICI-self cancellation scheme is
constructed, which can suppress ICI effectively. Moreover, in
this scheme, if the channels are real-valued fading channels,
the received signals at the destination node have the Alamouti
code structure on each subcarrier and thus it has the fast
symbol-wise ML decoding and when frequency offsets are not
large, the new scheme can achieve diversity order 2 [13].
E. Zero Forcing Technique
Y.Mei,Y.Hua,A.Swami,and,B.Daneshrad,March preposed that
for Successful collaboration in cooperative networks require
accurate estimation of multiple timing offsets. When
combined with signal processing algorithms the estimated
timing offsets can be applied to mitigate the resulting intersymbol interference (ISI). This paper seeks to address timing
synchronization in distributed multi-relay amplify-andforward (AF) and decode-and-forward (DF) relaying
networks, where timing offset estimation using a training
sequence is analysed. First, training sequence design
guidelines are presented that are shown to result in improved
estimation performance. Next, two iterative estimators are
derived that can determine multiple timing offsets at the
destination. These estimators have a considerably lower
computational complexity [12].
Since the base station has no influence on the noise at the
user terminals, the most intuitive approach for precoding is a
zero forcing filter (ZF) which eliminates all interference at the
user terminals. ZF precoding for single antenna receivers
was investigated extensively in the literature [5], [6].
F. Minimum mean-square-error precoding
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The ZF precoder completely eliminates multi-user
interference at the expense of noise enhancement. The
minimum mean-square-error (MMSE) precoder balances the
multiuser interference mitigation with noise enhancement and
minimizes the total error. Unlikethe ZF precoder, the MMSE
precoder cannot be designed in such a straight forward way.A
key to design of the MMSE precoder is to scale the transmit
vector such that the total transmit power has the predefined
level [7]
CONCLUSION
This paper presents a brief review of recent research findings
concerning the prcoding and their design for MIMO
systemsThus a detailed survey of various precoding schemes
present in the literature for multiple input- multiple output
(MIMO) systems for transmission diversity.
ACKNOWLEDGMENT
Apart from my efforts, the success of my work depends
largely on the encouragement and guidelines of many others. I
take this opportunity to express my gratitude to the people
who has been instrumental in the successful completion of this
work. I would like to extend my sincere thanks to all of them.
I owe a sincere prayer to the LORD ALMIGHTY for his kind
blessing and giving me full support to do this work, without
which this would have not been possible. I wish to take this
opportunity to express my gratitude to all, who helped me
directly or indirectly to complete this work.
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