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International Journal of Electronics and Computer Science Engineering
Available Online at www.ijecse.org
1686
ISSN- 2277-1956
Artificial Neural Network Channel Estimation for
OFDM System
1
Kanchan Sharma
, Shweta Varshney
2
1,2
1,2
Dept. of Electronics and Communication Technology
Guru Gobind Singh Indraprastha University,Delhi, India
1
Email- [email protected]
2
Email- [email protected]
Abstract- This paper uses Artificial Neural Network (ANN) for channel estimation based on Levenberg-Marquardt
training algorithm in OFDM systems over Rayleigh fading channels. This technique utilizes the learning property of
neural network. By using this feature, there is no need of any matrix computation and proposed technique is less complex.
This technique is useful to achieving the high data rate, transmission capability with high bandwidth, efficiency and its
robustness to multipath delay. In OFDM system, the Channel estimation is an essential problem so the Pilot-aided
channel estimation has been used; a good choice of the pilot pattern should match the channel behavior both in time and
frequency domains. In this arrangement, the performance of the channel estimation is analyzed with estimators based on
Least Square Algorithm is carried out through MATLAB Simulation. The performance of OFDM with ANN is evaluated
on the basis of Bit Error Rate (BER). The OFDM with ANN has been shown to perform much better than the OFDM
without ANN.
Keywords –OFDM. Channel Estimation. Artificial Neural Network. Least-Square Error
I. INTRODUCTION
OFDM is becoming a very popular multi-carrier modulation technique for transmission of signals over wireless
channels. Now OFDM is widely used for high-speed communications over frequency selective channels. OFDM
divides the high data rate stream into parallel lower data rate and hence prolongs the symbol duration, thus helping
to eliminate Inter Symbol Interference (ISI). It also allows the bandwidth of subcarriers to overlap without Inter
Carrier Interference (ICI) as long as the modulated carriers are orthogonal. Therefore OFDM is considered as an
efficient modulation technique for broadband access in a very dispersive environment.
The frequency selective fading, is caused by multipath could lead to carriers used, being heavily attenuated due to
destructive interference at the receiver. The result of this is the carriers being lost in the noise [1].To increase
performance of OFDM system under frequency selective channels; the channel estimation is required before
demodulation of OFDM signals [2]. The channel estimation is a process of characterizing the effect of the
transmission medium on the input signal.
In OFDM system there are several techniques for channel estimation [2-14].Among these techniques; Block type
Pilot based channel estimation technique is more popular. The Block type Pilot based estimation techniques can be
based on Least-Square (LS). The LS estimators have low complexity.
In this paper, we propose an artificial neural network (ANN) based on channel estimation technique as an alternative
to Block type pilot based channel estimation technique for OFDM systems over Rayleigh fading channels. The
Simulation results show that ANN based on channel estimator gives better results as compared to Block type pilot
based channel estimator for OFDM systems over the Rayleigh fading channel.
The organization of this paper is as follows. In section II, description of OFDM system model is given. ANN based
channel estimator is described in section III. Simulation results are offered in section IV and finally, section V
Concludes the paper.
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Artificial Neural Network Channel Estimation for OFDM System
II.SYSTEM OUTLINE
A. OFDM System Model –
A block diagram of the OFDM system is shown in Fig.1.Firstly the binary information is mapped using
baseband modulation schemes such as QAM.Then the serial-to-parallel conversion is applied to baseband
modulated signals. After modulation the symbol rate reduced to R=(R/log2M), where M is constellation is
applied to baseband modulated signals. This reduces data rate by N times where N is number of parallel
streams. Each of parallel streams constitutes tiny bandwidth in the spectrum. So these streams almost undergo flat
fading in the channel. This is the greatest advantage of OFDM. After inserting pilots either to all subcarriers with a
specific period of blocks or within a uniform period of frequency bins in all blocks, the serial-to-parallel converted
data is modulated using Inverse Fast Fourier Transform (IFFT). After IFFT, the time domain signal is given by
following equation:
s(n)=IFFT( S(k)),n=0,1,2..N-1
Where N is the length of FFT, s (k) is baseband data sequence. After IFFT, the guard interval called as cyclic Prefix
is inserted to prevent Inter-Symbol-Interference (ISI). This interval should be chosen to be larger than expected
delay spread of the multipath channel. The guard time includes the cyclically extended part of the OFDM symbol in
order to eliminate the Inter-Carrier-Interference (ICI). The symbol extended with the cyclic prefix is given as
follows:
Where Nc is the length of the cyclic prefix. The resultant signal st(n) will be transmitted over frequency selective
time varying fading channel with additive white Gaussian Noise (AWGN). The received signal is given by
following equation:
yt (n) = st (n) h (n) + w (n)
(3)
Where h(n) is the impulse response of the frequency selective channel and W(n) is AWGN.
At the receiving end, firstly the cyclic prefix is removed. Then the signal y(n) without cyclic prefix is applied to FFT
block in order to obtain following equation:
After FFT block, assuming there is no ISI demodulated signal is given by following equation:
Where H(k) is FFT[h(n)] and W(k) is FFT[w(n)].
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IJECSE,Volume1,Number 3
Kanchan Sharma and Shweta Varshney
Fig1 Block diagram of base band model of general OFDM system
From Eq.5 that before demodulation, the channel estimation should be done at the receiver side, in order to
compensate the effects of the channel on the received signal
B. ArtificialNeuralNetworks(ANNs) –
Artificial Intelligence is a branch of study, which enhances the capability of computers by giving them human-like
intelligence. The brain architecture has been extensively studied and attempts have been made to simulate it.
‘Artificial Neural Networks’ (ANN) represent an area of artificial intelligence (AI). They are basically an attempt to
simulate the brain. An artificial neuron model consists of a linear combination followed by an activation function.
This network utilized the different type of activation functions; the common ones, which are the sufficient for most
applications, are the sigmoidal and hyperbolic tangent functions.
B.I Multi-layer Feed-Forward (FF) ANN
First, consider the single layer FF network. A layer is a set of neurons or computational nodes at the same level. A
set of inputs can be applied to this single layer of neurons. This would then become a single layer FF network. This
single layer could be called the “output layer” (OL). Each node in the OL is called an output neuron. This structure
can be extended to a multi-layer FF ANN by adding one or more layers to the existing network. These additional
layers thenbecome “hidden layers” (HL). Each node in the HL is called a hidden neuron. They appear as
intermediate neurons between the input and the output layers.
In Figure 2, the three layers of an ANN are shown. The first layer is the input layer where the input datavector is
passed into the network.the input is a two dimensional vector. Following that is the hidden layer containing 3 hidden
neurons.
Figure 2 Multilayer feed forward network
B.II. ANN Based OFDM
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Artificial Neural Network Channel Estimation for OFDM System
In this paper the ANN based channel estimation is designed using MATLAB.We have simulated BER in 16-QAM
modulated OFDM signal.
The system model comprises of the blocks as shown in Figure 3.
Figure 3 OFDM with ANN
For ANN the symbols after parallel to serial converter block, which is QAM modulated are taken as the training
data. The data after passing through the channel are taken as target. With these training data and target data we have
trained the network for varying the SNR. After that we have tested the network with different OFDM signals. Now
these tested data are demodulated and with these we calculate the BER. The parameters of the proposed technique
are given in table1.
Table -1Parameters of artificial neural networks
value
parameter
Number of inputs
Number of hidden layers
Number of neurons
Epoch number
Training Function
Transfer Function
Performance
III.
2
2
10,10
500
Levenberg-Marquart
Tansig
Mean Aquare Error
EXPERIMENT AND RESULT
The BER performance has been observed for the OFDM signal for 16-QAM modulated signals in Rayleigh faded
channel in fig4. In AWGN as well as faded channel.
0
10
OFDM with LS estimation
-1
BER
10
-2
10
-3
10
0
2
4
6
8
SNR
10
12
Figure4. BER for LS estimators to OFDM Systems.
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Kanchan Sharma and Shweta Varshney
The ANN is trained with signal input from the transmitter side. For 16-QAM, the training sample set is a (1x8640)
complex type matrix. The target sample set is presented to the ANN in the form of a matrix of size (1x8640)
complex type matrix. The learning of the ANN is done in the training phase during which the ANN adjusts its
weights according to the specific coding logic applied at the transmitter end. The ANN is trained for 1000 epochs
and it performance is measured by Mean Square Error. During this phase, on an average, the ANN reaches this MSE
goal with an accuracy of nearly 100%.this is confirmed by over twenty trials.
During the simulation, severely faded data, mixed with AWGN is decoded by trained ANN to test its effectiveness
and confirm its feasibility in that role. This test also assesses its accuracy of performance. Fig 5 shows the BER
versus SNR of the LS with ANN channel estimation algorithms of OFDM system. It is seen that ANN based OFDM
exhibits better performance in terms of lower Bit Error Rate (BER).
-1
10
BER
ANN OFDM
-2
10
-3
10
0
2
4
6
8
SNR dB
10
12
14
16
Fig5 BER plot for LS estimator with ANN to OFDM systems.
0
10
ANN OFDM
Theoretical OFDM
OFDM with LS estimation
-1
BER
10
-2
10
-3
10
0
2
4
6
8
SNR dB
10
12
14
16
Fig6 Bit Error Rate of the OFDM system with and without the neural network
Finally we can compare the BER plots of OFDM with LS estimation, theoretical OFDM and ANN OFDM.From the
figure we can say that the best performance is obtained with the proposed ANN based technique for QAM. As
shown in Figs.6, the best results are obtained with ANN based estimation technique.
These results are very promising and show that neural networks can be efficiently used in an OFDM system.
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IV.CONCLUSION
In this paper an artificial neural network channel estimation technique based on levenbergmarquardtTraining algorithm has been proposed for OFDM systems over Rayleigh fading channel. BER
analysis of the ANN based estimator is obtained and compared with the LS estimation techniques. The
application of ANN for Rayleigh multipath fading channel in modulated environment may come to an
effective way to improve the BER probability in OFDM system. It shows the ANN transforms to be a
suitable tool which makes channel estimation better as well as received data performance is significantly
good in wireless communication. ANN specially feed-forward networks. With better configuration of the
ANN and optimization conditions of training and testing, the ANN can be used as the efficient method for
recovery of symbols at different fading condition.
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