Evaluation of channel estimation combined with ICI self-cancellation scheme in doubly selective fading channel

International Journal of Computer Science and Software Engineering (IJCSSE), Volume 1, Issue 1, October 2014
ISSN (Online): 2409-4285 www.IJCSSE.org Page: 1-7
Evaluation of channel estimation combined with ICI self-cancellation
scheme in doubly selective fading channel
Lien Pham Hong1, Quang Nguyen Duc2, Dung Mac Duc3
1 University of Technical Education Ho Chi Minh City, Vietnam
2, 3 Ho Chi Minh City University of Technology, Vietnam
1quangnd@vms.com.vn, 2phamhonglien2005@yahoo.com, 3dungmac10150802@yahoo.com
ABSTRACT
Orthogonal Frequency Division Multiplexing (OFDM) is a
modulation scheme, which is used in several wireless systems for
transferring data at high rate. The multi path fading channel and
the frequency offset between the transmitted and received carrier
frequencies introduce ICI (Inter Carrier Interference). ICI effects
the OFDM symbols and degrades the system performance. This
paper proposes a solution: combine channel estimation and ICI
self-cancellation to combat against ICI in doubly selective fading
channel. The simulation results show the effect of this solution.
Keywords: OFDM, ICI, Wireless Systems, Inter Carrier
Interface.
1. INTRODUCTION
Orthogonal Frequency Division Multiplexing (OFDM) is a
modulation scheme, which is used for transferring data at
high rate by using a numerous sub-carrier orthogonal to
each other. With many advantages, OFDM is used in many
wireless communication systems nowadays. The main
disadvantage of this scheme is the inter-carrier interference
(ICI), caused by Doppler shift due to relative motion
between the transmitter and receiver when transferring
data in multi path fading channel , or by differences
between the frequencies of the local oscillators at the
transmitter and receiver.
Currently, there are many different methods for reducing
ICI including: pulse shaping, frequency domain
equalization [1], ICI self-cancellation [2], maximum
likelihood estimation [3]…The research of these methods
is applied in the Gaussian environment with the
normalized frequency offset. However the real
environment is not only Gaussian noise but also the effect
of the complicated multipath fading and the mismatch
between the transmitter and the receiver caused by the
movement of the transmitter or the receiver, we call this
the “doubly selective fading channel”. So we concentrate
on the performance of the OFDM system in this
environment and we propose the combination between
channel estimation and the method of reducing ICI. In this
paper, we use the channel estimation LS (Least Square)
combine with 2 methods of reducing ICI: Maximum
likelihood estimation and the ICI self-cancellation. The
results show that the combination between channel
estimation LS and ICI self-cancellation can reduce the
effect of ICI and make the OFDM perform better.
2. OFDM SYSTEM AND ICI
OFDM block diagram [4] [5] is shown in Figure 1.
Fig. 1. OFDM block diagram.
The main disadvantage of OFDM, however, is its
susceptibility to small differences in frequency at the
transmitter and receiver, normally referred to as frequency
offset. This frequency offset can be caused by Doppler
shift due to relative motion between the transmitter and
receiver, or by differences between the frequencies of the
local oscillators at the transmitter and receiver. In this
paper, the frequency offset is modeled as a multiplicative
factor introduced in the channel, as shown in Figure 2.
Fig. 2. Model for frequency offset.

2
L. P. Hong et al.
The received signal in time domain could be written as
() = ()

+ () (1)
Where is normalized frequency offset and = Δ ,
Δ is a frequency differences between the transmitted and
received carrier frequencies, is a subcarrier symbol
period. w(n) is the AWGN introduced by the channel.
The effect of this frequency offset on the received symbol
stream can be understood by considering the received
symbol () on the kth sub-carrier.
!
() = ()(0) + ()( − )
#$%
#'
+ '
= 0,1,2 … − 1 (2)
Where is the total number of subcarriers, () is the
transmitted symbol for the kth subcarrier, ' is the FFT of
() and ( − )are the complex coefficients for the ICI
components in the received signal.The ICI components are
the interfering signals transmitted on sub-carriers other
than the kth subcarrier. The complex coefficients are given
by
( − ) = ,-./
(#01!')2
,-.(
(#01!')/ ) exp 789 :1 −
; ( + − )
(3)
The desired received signal power can be represented as:
=[|@()| ] = =[|()(0)| ]
= =[|()| ]|(0)| (4)
The ICI power is represented as:
=[|B()| ] = =
⎣ ⎢ ⎢ ⎡
!
F ()( − )
#$%
#'
F

⎦ ⎥ ⎥ ⎤
= =[|()| ]Σ !|( − )|
#$%
#'
(5)
CIR is given by below equation:
@BK = LM|N(')|OP
L[|Q(')|O] = LM|R(')|OPLM|(%)|OP
L[|R(#)|O]Σ
ST|(#!')|O
UVW
UXY
(6)
In this paper, the desired signal is transmitted on subcarrier
“0”, the CIR expression in Eq. 6 can be derived as:
@BK = |(%)|O
Σ
ST|(#)|O
UVT
(7)
3. MAXIMUM LIKELIHOOD
ESTIMATION
A method for frequency offset correction is ML estimation
in OFDM systems was suggested by Moose [3].In this
approach, the frequency offset is first statistically
estimated using a maximum likelihood algorithm and then
cancelled at the receiver. This technique involves the
replication of an OFDM symbol before transmission and
comparison of the phases of each of the subcarriers
between the successive symbols.
Figure 3 shows the block diagram of the OFDM system
using this method.
Fig. 3. The OFDM system using channel estimation and maximum
likelihood estimation.
When an OFDM symbol of sequence length is
replicated, the receiver receives, in the absence of noise,
the 2 point sequence Z() is given by:
Z() =
1

[ ()()

('01)

]
'$!]
^
= 0,1, … , − 1, ≥ 2` + 1 (8)
Where () are the 2 + 1 complex modulation values
used to modulate 2 + 1 subcarriers. () is the channel
transfer function for the kth carrier and is the normalized
frequency offset of the channel.The first set of symbols
is demodulated using an point FFT to yield the sequence
K() and the second set is demodulated using another
point FFT to yield the sequence K () .The frequency
offset is the phase difference between K() and K (),
that is K () = K()
1.
Adding the AWGN yields:
() = K() + a()
() = K()
1 + a ()
= 0,1, … , − 1 (9)

3
L. P. Hong et al.
The maximum likelihood estimate of the normalized
frequency offset is given by:
̂ = :
∗(')] jYVSj 2

; tan! f/Σ Qg[hO(')hT
∗(')] jYVSj 2m (10)
/Σ kl[hO(')hT
This maximum likelihood estimate is a conditionally
unbiased estimate of the frequency offset and was
computed using the received data .Once the frequency
offset is known, the ICI distortion in the data symbols is
reduced by multiplying the received symbols with a
complex conjugate of the frequency shift and applying the
FFT.
n = oo p()!Oq

r

s (11)
4. ICI SELF CANCELLATION
ICI self-cancellation [2] is a scheme that was
introduced by Zhao and Sven-Gustav in 2001 to
combat and suppress ICI in OFDM. Succinctly, the
main idea is to modulate the input data symbol onto a
group of subcarriers with predefined coefficients
such that the generated ICI signals within that group
cancel each other, hence the name “self-cancellation”.
It is seen that the difference between
the ICI co-efficient of two consecutive sub-carriers
are very small. This makes the basis of ICI self-cancellation.
Here one data symbol is not modulated in to one sub-carrier,
rather at least in to two consecutive sub-carriers. If
the data symbol “a” is modulated in to the 1st subcarrier
then “–a” is modulated in to the 2nd sub-carrier. Hence the
ICI generated between the two sub-carriers almost
mutually cancels each other. This method is suitable for
multipath fading channels as here no channel estimation is
required. Because in multipath case channel estimation
fails as the channel changes randomly. This method is also
suitable for flat channels. The method is simple, less
complex and effective. The major drawback of this method
is the reduction in bandwidth efficiency as same symbol
occupies two sub-carrier.
Figure 4 shows the block diagram of the OFDM system
using this method.
Fig. 4. The OFDM using channel estimation and ICI self-cancellation.
4.1 Modulation
Assuming the transmitted symbols are such that:
(1) = −(0), (3) = −(2), … , ( − 1) =
−( − 2) (12)
Then the received signal on subcarrier becomes:
!
u() = ()( − )
#$%
+ '
= Σ ! ()u( − )
#$%
# lvl
+ ' (13)
In such a case, the ICI coefficient is denoted as:
u( − ) = ( − ) − ( + 1 − ) (14)
Similarly the received signal on subcarrier + 1 becomes:
u( + 1) = Σ ! ()u( − 1 − )
#$%
# lvl
+ '0(15)
4.2 Demodulation
To further reduce ICI, ICI cancelling demodulation is
done. The demodulation is suggested to work in such a
way that each signal at the + 1 th subcarrier (now k
denotes even number) is multiplied by “-1” and then
summed with the one at the kth subcarrier. Then the
resultant data sequence is used for making symbol
decision. It can be represented as:
uu() = u() − u( + 1)
= Σ ! ()[−( − 1 − ) + 2( − ) −
#$%
# wxẵ
( + 1 − )] + ' − '0 (16)
The corresponding ICI coefficient then becomes:
uu( − ) = −( − 1 − ) + 2( − ) − ( + 1 − )
Thus, the ICI signals become smaller when applying ICI
cancelling modulation. On the other hand, the ICI
cancelling demodulation can further reduce the residual
ICI in the received signals. This combined ICI cancelling
modulation and demodulation method is called the ICI
self-cancellation scheme.
4.3 Carrier to Interference ratio (CIR)
In equation (16), the average power can be represented as:
=[|@()| ]
= =[|()| |(−1) + 2(0) + (1)| ]
= =[|()| ]|(−1) + 2(0) + (1)| (18)
The average power could be represented as:

4
L. P. Hong et al.
=[|B()| ] = =[|()| ]Σ ! |( − 1 − ) +
#$%
#'
# lvl
2( − ) − ( + 1 − )| (19)
According to the definition of CIR, the CIR can be
represented as:
@BK =
=[|@()| ]
=[|B()| ]
= LM|R(')|OP|(!)0 (%)0()|O
L[|R(#)|O]Σ
ST |(#!!')0 (#!')!(#0!')|O
UVW
UXY
U z{ẵr
(20)
In this paper, the desired signal is transmitted on subcarrier
“0”, the CIR expression can be derived as:
@BK = |(!)0 (%)0()|O
Σ
ST |(#!!')0 (#!')!(#0!')|O
UVW
UXY
U |}|r
(21)
Due to the repetition coding, the bandwidth efficiency
of the ICI self-cancellation scheme is reduced by half.
To fulfill the demanded bandwidth efficiency, it is natural
to use a larger signal alphabet size. For example the
OFDM using modulation scheme 4PSK with ICI self-cancellation
has the same bandwidth efficiency as the
standard OFDM (using BPSK).
5. COMBINE CHANNEL ESTIMATION LS
WITH METHOD OF REDUCING ICI IN
DOUBLY SELECTIVE FADING
CHANNEL
The real transmitted channel is the doubly selective fading
so using channel estimation is necessary. The channel
estimation helps reducing the effect of the channel on the
signal. In this paper we use the algorithm LS (Least
Square) to estimate the channel. This algorithm does not
need to know the parameters of the channel so this one is
not complex but have the high variance. The response of
the channel is estimated using the known pilot data and the
pilot received at the receiver. Based on the estimated
response, the signal after that is represented as:
~
() = h(')
€(') , = 0,1, … , − 1 (22)
Where €
()is the estimated response.
The combination between channel estimation and the
method of reducing ICI is proposed to ensure the quality
of the system when we transmit the data through the real
channel.
6. SIMULATION RESULTS
6.1 Simulation Parameters
In this paper we simulate 3 systems: the standard OFDM,
the OFDM system using ML estimation and the OFDM
system using ICI self-cancellation. The block diagram of 3
systems was introduced in previous section.
All 3 OFDM systems use 1024 carriers with 840 of that
carrying data. The CP was added to reduce the effect of
ISI and has the length of 256. The OFDM system operates
at frequency 2.5 GHz with the bandwidth 20 MHz
The simulation channel is the fading channel of ITU-R
standard [6] [7].This is the standard for WiMAX system.
With this standard, we have 3 type of channel: indoor,
pedestrian and vehicular according to the speed between
the transmitter and the receiver. The parameters of 3
channel is shown in the tables below.
Table 1: Parameters of Indoor, Pedestrian and vehicular channel.
Tap Indoor Pedestrian Vehicular
Delay
(ns)
Power
(dB)
Delay
(ns)
Power
(dB)
Delay
(ns)
Power
(dB)
1 0 0 0 0 0 0
2 100 -3.6 200 -0.9 0.8 -1
3 200 -7.2 800 -4.9 1.6 -9
4 300 -10.8 1200 -8 2.2 -10
5 500 -18 2300 -7.8 3.6 -15
6 700 -25.2 3700 -23.9 5.2 -20
6.2 Results
To analyze the effect of ICI on the received signal, we
consider a system with = 1024 carriers. The frequency
offset values used are 0.1, 0.2 and 0.3 and is taken as 0.
So we are analyzing the signal received at the sub-carrier
with index 0.The complex ICI coefficients ( − ) are
plotted in figure 5.This figure shows that for a larger , the
weight of the desired signal component (0) decreases,
while the weights of the ICI components increases.

5
L. P. Hong et al.
Fig. 5. Amplitude of ( − ) with = 1024.
10-1
10-2
10-3
10-4
Figure 6 shows the amplitude comparison of|( − )|,
|u( − )| and |uu( − )| with = 1024 and = 0.2 .
For the majority of ( − ) values, |u( − )| is much
smaller than |( − )| and |uu( − )| is even smaller
than|u( − )|.
Thus, the ICI signals become smaller when applying ICI
cancelling modulation. On the other hand, the ICI
cancelling demodulation can further reduce the residual
ICI in the received signals. This combined ICI cancelling
modulation and demodulation method is called the ICI
self-cancellation scheme. Until now, three types of ICI
coefficients are obtained: ( − ) for the standard OFDM
system, u( − ) for ICI cancelling modulation and
uu( − ) for combined ICI cancelling modulation and
demodulation.
50
0
-50
-100
-150
-200
Fig. 6. Compare|( − )|,|u( − )| and|uu( − )|.
Figure 7 shows the theoretical CIR curve calculated by
above CIR equation together with simulation results. As a
reference, the CIR of a standard OFDM system is also
shown. Such an ICI cancellation scheme gives more than
15 dB CIR improvement in the range 0 ≤ ≤
0.5.Especially for small to medium frequency offsets
in the range 0 ≤ ≤ 0.2 the CIR improvement can
reach 17 dB.
40
35
30
25
20
15
10
5
Fig. 7. CIR compare between standard OFDM and the OFDM using ICI
self-cancellation.
Figure 8 and 9 is the results of BER (Bit Error Rate)
simulation in the indoor channel with 2 different frequency
offset. The relative speed between the transmitter and the
receiver is 1 km/h. The OFDM using channel estimation
LS with ICI self-cancellation has the best performance
than the other systems. It show that this combination
makes the OFDM system combat against the ICI in this
environment. Channel estimation with ML estimation is
effective in reducing the effect of ICI (The OFDM using
this scheme ha better performance than the standard
OFDM system). When we increase the frequency offset
from 0.2 to 0.3, the effect of ICI is increasing but the
combination is still good at reducing the ICI.
100
10-1
10-2
10-3
Fig. 8. BER graph in indoor channel with frequency offset Ɛ= 0.2 .
0 200 400 600 800 1000 1200
100
Subcarrier k
ICI coefficient
offset=0.1
offset=0.2
offset=0.3
0 200 400 600 800 1000 1200
-250
Subcarrier k
dB
|S(l-k)|
|S`(l-k)|
|S``(l-k)|
0.05 0.1 0.15 0.2 0.25
Normal Frequency Offset
CIR
Standard OFDM system
OFDM system using ICI self cancelation scheme
0 2 4 6 8 10 12 14 16 18 20
10-4
SNR
BER
OFDM standard
OFDM using ICI self cancellation
OFDM using frequency offset estimation

Evaluation of channel estimation combined with ICI self-cancellation scheme in doubly selective fading channel

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Evaluation of channel estimation combined with ICI self-cancellation scheme in doubly selective fading channel