Channel estimation for high data rate communication in mobile wi max system

International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
115
CHANNEL ESTIMATION FOR HIGH DATA RATE
COMMUNICATION IN MOBILE WI-MAX SYSTEM
Jagdish D. Kene1
, Dr. Kishor D. Kulat2
1
Ph D Scholar, Electronics Engineering Department, V.N.I.T., Nagpur, M.S., India
2
Professor, Electronics Engineering Department, V.N.I.T., Nagpur, M.S., India
ABSTRACT
High data rate is one of the main challenges in next generation wireless system in the
mobile environment. Mobile Wi-Max system based on IEEE 802.16e standard supports high
throughput by implementing Orthogonal Frequency Division Multiple Access (OFDMA). In
Mobile environment, channel estimation is the important key for studying the system
performance. In this paper, downlink channel estimation in mobile Wi-Max is evaluated by
exploiting pilot at data symbol. Using two interpolation schemes namely (i) Least Square
Error (LSE) and (ii) Minimum Mean Square Error (MMSE), the evaluation of downlink
channel estimation is carried out in frequency domain that provides high data rate
transmission with low hardware complexity. The characteristics of interpolation methods are
measure in terms of Bit Error Rate (BER) performance of the channel estimated over OFDM
symbols. Author has shown that, the MMSE outperform LSE technique for mobile Wi-Max
application with reference to ideal channel condition.
Keywords: Channel Estimation, LSE, MMSE, Mobile Wi-Max, OFDMA.
1. INTRODUCTION
The next generation wireless system demands high data rates to provide the services
to the applications like internet protocol Television (IPTV), video on demand (VOD), voice
over IP (VOIP) etc. Worldwide Interoperability for microwaves Access (Wi-Max) system is
design to support high mobility and bit rate greater than 5 M bits/sec and target to reach up to
100 M bits/sec[1]. Wi-Max is based upon IEEE802.16 & standard for wireless Metropolitan
Area Network (WMAN) [2]. It enables operators to offer diverse wireless services to fixed
and mobile users. The IEEE 802.16e standard was published in year 2004 for Fixed Wireless
INTERNATIONAL JOURNAL OF ELECTRONICS AND
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ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 3, May – June, 2013, pp. 115-123
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116
Access (FWA) applications using OFDM technology [5]. In December 2005, the IEEE
602.16e standard was published to support Mobile Wireless Access (MWA) is based on
Orthogonal Frequency Division Multiple Access (OFDMA) technology [6]. OFDM is
transmission technique of multi-carrier modulation scheme in which the wide transmission
bandwidth is divided into narrower bands and data is send in parallel on that narrow bands.
The basic principle behind OFDM is that a signal with long symbol duration time is less
sensitive to multipath fading than a signal with a short symbol time. Hence Wi-Max system
performance can be improved by sending several parallel symbols with a long symbol time
than sending them in a series with a shorter symbol time. This result in reducing the Inter
Symbol Interference (ISI).The remaining ISI effect is eliminated by cyclically extending the
signal [5][8].
OFDM is basically used for fixed Wi-Max. OFDMA is a multiuser version of OFDM
used for mobile Wi-Max. In addition mobile Wi-Max uses scalable OFDMA (SOFDMA) as a
transmission technique where band width is scalable between 1.25 to 20 MHz. (IEEE
802.16e). The scalability is achieved by making the FFT size flexible for constant subcarriers
spacing [3]. In the realization of promises made by Wi-Max system, the receiver algorithms
(Decoder/Demodulator) play a very important role. Channel estimation, which is one of the
key blocks in receiver section of Wi-Max system. It is one of most important elements of
wireless receivers that employ coherent demodulation [4].
Generally channel estimation is a challenging problem in any wireless system because
the radio channel is highly dynamic compare to other guided media. While travelling through
the radio channel, signal undergoes various noise effects that corrupt the signal and place the
limitation on the system performance. To realize the changes in received in signals, channel
estimation has been studied specifically for OFDM based Wi-Max system [7], [8]. Although
there are many channel estimation techniques for wireless system reported in the literature for
mobile Wi-Max system. It is important to employ estimation techniques that is specifically
design for Wi-Max pilots or preambles and offers low computational and hardware
complexities. In this paper, different channel estimation techniques are analyzed for downlink
transmission of Wi-Max system. For this, approaches based on
i) Least Square (LS) estimation
ii) Minimum Mean Square Error (MMSE)
The comparisons are made with reference to different channel parameters. It is observed that
MMSE estimator offers a good trade-off between the system performance and complexity.
The rest of the paper is organized as follows: In section 2 system model will be introduced.
Channel estimated algorithms will be explained in section 3, Simulation and results are
presented in section 4. Conclusions are drawn in section 5.
2. SYSTEM MODEL
The mobile Wi-Max system based on IEEE 802.16e is represented in block schematic
is shown in fig.1. The data bits are generated randomly, that are encoded and interleaved.
Then stream of data are map into QPSK or QAM signals. In the downlink channel,
subcarriers are divided into clusters or frames and sub-channelized by inserting the pilots. In
this one or more sub-channels are assigned to the different mobile users. Data and pilot
subcarriers are allocated within each frame, which is physically transmitted over the channel,
after the sub-channelization, frame is fed to a IFFT block that produces corresponding time
domain signal x (n) and cyclic prefix is added to each symbol before transmission. At the

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receiving end, guard time is removed from received signal y (n) which constitute channel
impulse response h (n) and additive white Gaussian noise w (n) and then fed to FFT block
that convert signal into frequency domain. After the pilot extraction OFDMA symbols are
collected and rearranging the subcarriers. The original data is recovered by performing the
suitable decoding technique. The principle of OFDM technique is to convert serial data
stream into parallel blocks of size N number of symbols stream and each of which is
modulates using Inverse Fast Fourier Transform (IFFT). In the frequency domain, each
OFDM symbol is made by mapping the symbols on the subcarriers.
Figure 1: Block Diagram of Mobile Wi-Max (IEEE 802.16e OFDMA-PHY)
The OFDMA symbol used in mobile Wi-Max has three classes of subcarriers [8].
• Data Carriers: Used for data transmission.
• Pilot Carriers: Used for carrying pilot symbols. Since the magnitude and phase of
these carriers are known to the receiver and they are used for channel estimation.
• Null Carriers: These carriers include DC subcarriers and Guard subcarriers do not
have transmitted energy. These are used to enable the signal to naturally delay and
prevent leakage of energy into adjacent channels.
The typical frequency domain representation of Wi-Max OFDM symbol is shown in fig.2.
Figure 2: Frequency Domain Representation of OFDM Symbol

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The time domain samples of an OFDM symbol can be obtained from frequency domain data
symbols as
( ){ }( )x n IFFT X k=
( ) 2 /
1
e
N
J nk N
n
X k Π
=
= ∑ 0 1n N≤ ≤ − (1)
Where X(k) is transmitted data symbol at kth
subcarrier of the OFDM symbol.
N defines the size of Fast Fourier transform.
The cyclic prefix (CP) is added to the time domain OFDM symbol. After passing the signal
through Digital to Analog converter, signals are transmitted over the mobile radio frequency
channel. The impulse response of the channel is assumed to be constant over an OFDM
symbol.
At the receiving end, the signal is received with noise. Cyclic prefix is removed after
synchronization. On the basis of perfect synchronization between time and frequency
domain, the simple form of baseband model of the received samples can be formulated as
( ) ( ) ( )
0
( )
L
l
y n x n l h l w n
=
= − +∑ (2)
Where L is the number sample spaced channel taps.
w(n) is Additive White Gaussian Noise (AWGN) sample with zero mean and variance 2
wσ .
h(l) is channel impulse response for the current OFDM symbol and given as time invariant
linear filter.
In the receiver part, FFT of the received signal y(n) has been done. The samples in
frequency domain can be written as
( ) ( ){ }Y k FFT y n=
( ) ( ) ( )X k H k W k= + (3)
Where H(k) and W(k) are FFTs of h(n) and w(n) respectively.
3. CHANNEL ESTIMATION METHODS
In order to neutralize the effects of the channel, the channel estimation should be done
at receiver. The channel estimation shown in figure 1 for base band OFDM system can be
based on Least square and minimum mean square error estimates. The structure of OFDM
signaling permits a channel estimator that uses both times as well as frequency correlation.
However, such a two dimensional structure is too complex for practical implementation. The
low complexity estimators can be modeled by the use of frequency correlation only. In this
paper channel estimation of Wi-Max system can be test with following methods.
(i) Least Square Error (LSE) Channel Estimator.
(ii) Minimum Mean Square Error (MMSE) Channel Estimator.

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3.1. Least Square Error (LSE) Estimator
The least square estimator is very basic and simple channel estimator. It can easily be
implemented without knowing the channel statistics compare to other techniques. It works on
the principle of dividing the received signal by the symbols that have actually been sent, i.e.
the symbols that are supposed to known [7]. The channel frequency response (CFR) is
written as
LS
YH
X
=
W
H
X
= + (4)
The division is indicative of an element wise division of Y by X. it is a simplistic
model with only one division per carrier. In this conventional channel estimation method, the
estimation of pilot signals is susceptible to AWGN and Inter Carrier Interference (ISI). Since
the channel responses of data subcarriers are obtained by interpolating the channel
characteristics of pilot subcarriers that result in poor performance of OFDM based Wi-Max
system [8], [13].
3.2. Minimum Mean Square Error (MMSE) Estimator
In this estimator technique, the mean squared error between the actual and estimated
channels is minimized. Therefore it is known as minimum mean square error estimator.
MMSE uses additional information like the operating signal to noise ratio and the channel
statistics. MMSE provides smooth interpolation and hence it is widely used for channel
estimation of OFDM based system with pilot subcarriers in downlink channel of
IEEE802.16e standard. In statistics, the mean squared error (MSE) of an estimator is the
expected value of the square of an error. The error is the amount by which the estimator
differs from the quantity to be estimated. The difference occurs because the estimator doesn’t
account for information that could produce a more accurate estimate [12]. However the
computational complexity of MMSE is very high due to extra information such as the
correlation between subcarriers and noise variance. The MMSE of the variable X (i.e.
Diagonal matrix that contains transmitted symbols) for given linear time invariant system
model is define as
1
Y X Y YX R R Y
∧
−
= (5)
Where RYX is the cross correlation between variables x and y. when the above equation is
applied to the OFDM channel estimation, then CFR of MMSE estimator is written as
( )( )
112
P P P
H
MMSE HH H H LSH R R XX Hωσ
−∧ −
= + (6)
Where Hp is the CFR at the pilot subcarriers, RHHP is the cross correlation between all the
subcarriers and the pilot subcarriers, σω represent the channel noise variances [8], [12], [13].

120
4. SIMULATIONS AND RESULTS
In this paper, channel estimation method is based on clusters or frames of transmitted
symbols. The mobile Wi-Max standard has implemented very unique permutation method
called Downlink Partially Used Sub channelization (DL-PUSC) that helps to minimize the
effect of channel fading [7]. The channel estimation methods which are mentioned in
previous section are evaluated based on IEEE 802.16e OFDMA physical layer. For
simulation, we consider the Wi-Max system operating in the 2.3 GHz frequency band and
downlink PUSC sub channel allocation. The various system parameters used in the
simulation are indicated in Table 1. The parameters are considered with the assumption that
the channel frequency response is constant over an OFDM symbol, but the time varying
within an OFDMA frame for low and medium mobility [9]. The estimator performance is
measured in terms of Bit Error Rate vs. SNR as shown in fig. 3. The BER performance of
MMSE is smoother than LS. i.e. MMSE estimation method performs better than LSE
method. The BER performance of these estimator also compare with reference to estimation
of ideal channel. i.e. the channel conditions are perfectly known to the receiver section also
shown in fig. 3. The scatter plot of received signal and equalized signal with QPSK
modulation based on LSE and MMSE estimation are shown in fig. 4 and Fig. 5 respectively.
We observed that the MMSE estimated symbols are comparatively less scattered than LS
estimation. It means that MMSE method is more robust against noise and helps Wi-Max
system to get better recovery of corrupted symbols.
Table 1: Parameters considered for simulation Mobile WiMax system
Parameters Value
System Bandwidth 8.75 MHz
Sampling frequency 10 MHz
FFT size 1024
Channel coding Convolution coding (CC)
Code rate ½
Modulation QPSK
Cyclic prefix ratio 1/8
Guard interval 128
Channel AWGN
Total number of subcarriers
(Data and Pilot)
841
Number of Guard subcarriers
183
(92 on left and 91on right)

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Figure 3: BER Performance of LSE and MMSE Channel Estimator w. r. t. Ideal channel
condition
Figure 4: Scatter Plot of LSE Channel Figure 5: Scatter Plot of MMSE Channel
Estimator Estimator

122
5. CONCLUSION
In this paper, two different channel estimation algorithms for downlink mobile Wi-
Max are analyzed and compare the BER performance under different system parameters. The
channel response of pilot subcarriers are estimated by LSE and MMSE estimator based on
pilot at symbol data in frequency. From the simulation results, we conclude that, the proposed
MMSE channel estimation method with fixed coefficients achieves better BER performance
compare to conventional LSE estimation method. Thus by fixing the parameter coefficient,
MMSE helps to mitigate the practical hardware and computational complexity. Owing to this
MMSE channel estimation scheme is effectively useful than LSE estimation for high data
rate applications of downlink mobile Wi-Max (IEEE 802.16e standard) system.
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