International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
Divination and Estimation of Channel in Mobile
Multi-User OFDMA Systems
G.Shiva Kumar#1, Mrs. G.Narendar*2,
M.Tech1,M.Tech2,Assistant Professor2,
Electronics and Communication Engineering& Sreenidhi Institute of science and Technology
,JNTU University,Yamnampet,Hyderabad
Abstract—In this paper we have a tendency to project an
approach of Channel estimation and prediction algorithms are
developed and evaluated for use in adaptive OFDM uplinks with
overlapping pilots when multiple users are transmitting
simultaneously. Pilots are nothing but insinuation symbols used
between transmitter and receiver .Estimation of channel
performance is done by using NMSE which is calculated in
MMSE and KALMAN estimator, we present the MMSE and LS
estimators and a method for modifications compromising
between complexity and performance. The channel estimation is
a two dimensional problem both (time and frequency), similarly a
plot is drawn between NMSE and Wavelength to analyze the
characteristics when the user is mobile.
Keywords-MMSE (Minimum Mean Square Error),SNR(Signal
to Noise Ration),
SER(symbol
error rate) and
NMSE(Normalized Mean Square Error).
I.
INTRODUCTION
In
adaptive
systems
resources(time/frequency/antenna) are allocated
based on channel quality and based upon user
requirements. The resources are utilized well and
multiuser multiplicity gains. In the system based on
OFDMA/TDMA bins, the sub-carriers are allocated
for each and every user .
For mobile users, the SNR will vary between bins
both in frequency and in time. The feasibility of
adaptive transmission in the downlink, based on
channel prediction .This paper will consider the
problem of estimating and predicting channels in the
corresponding uplinks. The downlink scenario is
less concentrated where overlapping pilots are not
used and main focus is on uplinks.
II.
OFDM
The basic idea underlying OFDM systems is the
division of the available frequency spectrum into
many subcarriers. To obtain a high spectral potency,
the frequency responses of the subcarriers are
overlapping and orthogonal, thence the name
OFDM. This orthogonality are often fully
ISSN: 2231-5381
maintained with a tiny price in a loss in SNR, albeit
the signal passes through a time dispersive
attenuation channel, by introducing a cyclic prefix
(CP).
There are two main problems in designing
channel estimators for wireless OFDM systems.
The primary downside is that the arrangement of
pilot information, wherever pilot suggests that the
reference signal utilized by both transmitters and
receivers. The second downside is that the style of
an estimator with both low complexity and good
channel tracking ability. The two issues are
interconnected. In general, the attenuation channel
of OFDM systems will be viewed as a twodimensional (2D) signal (time and frequency). The
best channel estimator in terms of mean-square
error is based on 2D Wiener filter interpolation.
Unluckily, such a 2D estimator structure is just too
complex for sensible implementation.
The combination of high data rates and low bit
error rates in OFDM systems necessitates the use of
estimators that have both low complexity and high
precision, where the two limiting factors work
contrary to each other and a good trade-off is
needed. The one-dimensional (1D) channel
estimations are usually adopted in OFDM systems
to accomplish the trade-off between complexity and
accuracy . The pilots are inserted both the directions
(frequency and time) in block-type and comb-type
channel estimations, correspondingly. The blocktype pilot preparation can be based on least square
(LS), minimum mean-square error (MMSE), and
modified MMSE for the estimators. The estimation
for the comb-type pilot arrangement includes the LS
estimator with 1D interpolation.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
I/P
chann
el
codin
g
Guard
band
inserti
on
IDFT
CP
INSER
TION
respectively. The estimations for the block-type
pilot arrangement can be based on least square (LS),
minimum mean-square error (MMSE), &modified
MMSE.
D/A
In uplinks as multi-user gain increases the channel
prediction becomes harder. Different carriers are
used for uplinks and downlinks.
CH
AN
NEL
O/P
Chann
el
Decodi
ng
GUAR
D
DELET
ION
DFT
CP
DELE
TION
A/
D
The estimators are based upon repetitive filtering
and decoding, estimators for the OFDM systems
with multiple transmit-and-receive antennas, and so
on. The block-type pilot channel estimation
schemes are more suitable for the slow fading
channels, and the comb-type pilot channel
estimation schemes are more suitable for the middle
and fast fading channels. In addition, block-type
pilot schemes are used over middle or fasting fading
channels, the channel estimation error may vary
considerably as a function of the location of the data
blocks with respect to the pilot block.
III.
ESTIM ATION TECHNIQUES
A. LS ESTIMATOR
This estimator minimizes the parameter (Y-XH)(YXH)H where (.)H represents conjugate transpose
operation.LS estimator of H is given by H^LS=X-1Y.
B. MMSE ESTIMATOR
It employs second order statistics to minimize the
mean square error .In this we make use of second
order auto-correlation and cross-correlation
functions. The mathematical equations used are
A. problem in channel estimators
There are two main problems in designing
channel estimators for wireless OFDM systems.
The pilot information arrangement is difficult,
where pilots can be used by both transmitters and
receivers. The pilots are inserted both the directions
(frequency and time) in block-type and comb-type
channel estimations, in which the pilots are inserted
in the both frequency and in time domains,
ISSN: 2231-5381
Y=DFTN(IDFTN( X)
g+ n)=
X
F g+ N
RHH = E{ H H H} = E{(F g ) (F g )H} = F Rgg FH
RgY = E{ g Y H}=E{ g(X F g + N)H} = Rgg FH XH
Rgg = Auto correlation of estimate of channel conditions( g)
2
RYY = E{ Y YH} = X F Rgg FH XH + σ N IN
-1
HH
ĝMMSE = RgY RYY Y
where
H=F
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g(F = DFT Matrix)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
C. KALMAN ESTIMATOR
Kalman predictor and corrector mathematical
equations, it calculates the next state and the output
based on the guesses.
The Equations used are : PREDICTOR
EQUATIONS
xapriori(j)=a*xaposteriori(j-1)
residual(j)=z(j)-h*xapriori(j)
dramatically reduces the calculation complexity of
matrices.
A. Comb-Type Pilot Channel Estimation
In comb-type pilot based channel estimation, for each
transmitted symbol, Np pilot signals are uniformly
inserted into X with S with subcarriers apart from each
other, where S=N/NP.
The receiver P=[PK]T,(K=0,1,----------NP-1)knows the
pilots locations , the pilot values XP=[XKP]T, and the
received signal Y. The LS estimates to the channel
conditions at the pilot subcarriers are calculated by
papriori(j)=a*a*paposteriori(j-1)+Q
k(j)=h*papriori(j)/(h*h*papriori(j)+R)
HPLS=[Y(P0)/X0 P,Y(P1)/X1P,--------------------Y(PNPP
T
1/X NP-1]
paposteriori(j)=papriori(j)*(1-h*k(j))
xaposteriori(j)=xapriori(j)+k(j)*residual(j)
In the above equations a and h are the system
explicit parameters Q and R are measurement and
performance noise.
D.Modified MMSE Estimator
At the data subcarriers (specified by H with length
N), given the LS estimates at pilot subcarriers ,
received signals Y, and maybe certain additional
knowledge of the channel statistics required for the
channel condition can be estimated.
IV.
Modified MMSE estimators are studied widely to
reduce complexity . Among them, an optimal lowrank MMSE (OLR-MMSE) estimator is proposed in
this paper, which combines the following three
simplification techniques:
1.The first simplification of MMSE estimator is to
replace the term (XXH)-1 with
E{(XXH)-1}=E{1/|XK|2}I
2.The second simplification is based on the low-rank
approximation. Most of the energy in is contained
in, or near, the first (L + 1) taps, where and N is the
DFT size. Therefore, we can only consider the taps
with significant energy, that is, the upper left corner
of the autocovariance matrix . In the IEEE Std.
802.11 and IEEE Std. 802.16, is chosen among
{1/32, 1/16, 1/8, 1/4}, so the effective size of matrix
is reduced dramatically after the low-rank
approximation is used.
OVERLAPPING PILOTS EXAMPLE
The comb-type pilot schemes can eliminate this
variation, and therefore all OFDM data symbols
experience a similar error rate. Because the error
rate of the comb-type pilot schemes is higher than
the lowest error rate that can be achieved by the
block-type pilot schemes, the block-type pilot
schemes provide the opportunity to protect the data
with high importance/priority by transmitting them
at the positions where the error rate is low.
Therefore, comb-type pilot schemes are more
suitable for generic data transmission, while the
3.The third simplification uses the singular value
decomposition (SVD). The SVD of is , where is a
unitary matrix containing the singular vectors and is
a diagonal matrix containing the singular values on
its diagonalλ0>=λ1>= ...>= λN1. The SVD also
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
block-type pilot schemes are more suitable for
transmission over slow fading channels or
transmission with unequal error protection (UEP).
reliable and robust while maintaining the high data
rate that digital communication demands.
ACKNOWLEDGMENT
One of the time-frequency bins of the investigated
system, containing twenty subcarriers with six
symbols each. Known 4-QAM pilot symbols
(black) and 4-QAM control data symbols (rings) are
placed on four pilot subcarriers. The modulation
format for the other (payload) symbols is adjusted
adaptively. Each bin is allocated to one out of K
users. All payload symbols within a bin use the
same modulation format
The author is grateful to Prof. G. Narendar for his
help.
RESULTS
1
PLOT OF SNR V/S SER OF AN OFDM SYSTEM WITH MMSE ESTIMATOR
10
V.
0
10
------S E R in D B
The available uplink bandwidth within a sector
(cell) is assumed to be partitioned into timefrequency bins of bandwidth Δfb and duration T.
We here assume T = 0.667 ms and Δfb = 200 kHz,
which is appropriate for stationary and vehicular
users in urban or suburban environments . We also
assume a subcarrier spacing of 10 kHz, a cyclical
prefix of duration 11 µs and an OFDM symbol
period (including cyclic prefix) of Ts = 111µs.
Thus, each bin of 0.667 ms × 200 kHz contains 120
symbols, with 6 symbols of duration 111µs on each
of the 20 10 kHz subcarriers. Of these 120 symbols,
four positions are booked for overlapping pilot
symbols, assumed to be 4-QAM symbols. Besides,
8 symbols are owed for regulate information, that
utilizes a fixed modulation (here assumed to be 4QAM), parting 108 payload symbols.
-1
10
CONCLUSION
-2
The purpose of this project was to give some
insight into power of the OFDM transmission
scheme. It has discussed not only the transmission
scheme itself, but also some of the problems that are
presented in mobile communications as well as the
techniques to correct them.
10
5
10
15
20
25
30
SNR in DB
Digital communications is a rapidly growing
industry and Orthogonal Frequency Division
Multiplexing is on the forefront of this
technology.OFDM will prove to revolutionize
mobile communications by allowing it to be more
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
PLOT OF SNR V/S SER FOR AN OFDM SYSTEM WITH KALMAN ESTIMATOR
REFERENCES
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[2].Edfors, O., Sandell, M., Van de Beek, J.-J., and Wilson, S. K.,
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1.2
10
----- S E R
[3].Strobach, P., “Low-Rank Adaptive Filters,” IEEE Transactions
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[4].K. B., Cheng, R. S., and Cao, Z., “Channel Estimation for OFDM
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[5].Li, Y., “Simplified Channel Estimation for OFDM Systems with
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1.1
10
[6].Auer, G., “Channel Estimation in Two Dimensions for OFDM
Systems with Multiple Transmit Antennas,” GLOBECOM, pp. 322–
326, 2003.
5
10
15
20
25
30
SNR in DB
Plot for NMSE vs Wavelength
1
10
0
-------> NM S E
10
-1
10
-2
10
0.1
0.15
0.2
0.25
0.3 0.35 0.4
------->Wavelength
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0.45
0.5
0.55
0.6
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