www.ijecs.in
International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 4 Issue 3 March 2015, Page No. 11088-11094
An High Eqipped SC FDMA Communication Model Based
On Advanced Wavelet Mechanism For Wireless Systems
A.Navya Lakshmi#1 V.Vamsi Sudheera#2
PG scholar, Dept of ECE, Sir CRR College of Engineering,
Eluru, AP, India.
navyaalluri9@gmail.com
Assistant Professor, Dept of ECE, Sir CRR College of Engineering, Eluru, AP, India.
sudheera.vadlamudi95@gmail.com
Abstract— Recently, varied development work has been carried out on mobile robots due to
their high level performance and accuracy. The autonomous navigation of a mobile robot in
an unknown environment has always been a very challenging task. In order to achieve safe
and collision free navigation, distance to target is very important and is often estimated by
using ultrasound. Ultrasonic measurements are mostly based on determination of TOF. A
factor of great importance when assessing the accuracy of ultrasonic range finders is the
knowledge of speed of sound, necessary to convert temporal into spatial information. A
digital signal-processing technique using Discrete Extended Kalman filter for making an
ultrasonic transducer capable of automatically compensating for variations in the speed of
sound due to temperature and to reduce the distortion effects of cross correlation estimation
is proposed and discussed in this paper. SC-FDMA has become most prominent in
broadband uplink wireless systems. Since it offers low distortion and free from PAPR
problem which often arises in OFDMA (Orthogonal frequency division multiple access)
systems. In this paper wavelets are substituted instead of FFT for this Orthogonality
achievement. The discrete wavelets of orthogonal and bi-orthogonal type proves to be best
when substituted instead of FFT analysis .The proposed approach shows outstanding
performance in terms of BER ,PAPR when compared against the conventional system.
Keywords— Wavelets, FDMA, FFT, SC-FDMA
INTRODUCTION
Ultrasonic
based
measurements
are
extensively used both in research and
production field, spanning in endless
applications: environment sensing of
autonomous mobile robots, high definition
imaging of biomedical devices, precise
location of micro-flaws in materials, accurate
estimation of the level of flammable fluids or
dangerous rivers, and so on [11]. The reason
of this success mainly relies upon the
opportunity offered by ultrasonic’s of
conceiving rather simple methods or building
I.
up relatively cheap meters, characterized by
satisfactory accuracy, reduced measurement
time, and, above all, high level of intrinsic
safety. OFDM (Orthogonal frequency
division multiplexing) has become more
popular and been used in most of the latest
wireless technologies like Bluetooth, zigbee,
Wireless LAN ,Wi-Fi, Wi-max and
protocols like DVB,DAB and many .This due
to the main reason that it supports wider data
with high data rates. It is robust against ICI
and ISI. OFDM suffers from a major
drawback which is high PAPR due to this it
not much preferable to be used in uplink
A.Navya Lakshmi, IJECS Volume 4 Issue 3 March, 2015 Page No.11088-11094
Page 11088
broadband wireless systems. Whereas this
can be overcome by using SC-FDMA where
a low PAPR with simple amplifiers are been
utilized. On the other hand, with its high
signaling rate, the frequency domain
equalizer of an SC-FDMA link is far more
complicated than an OFDM equalizer. With
SC-FDMA transmission confined to the
Long Term Evolution (LTE) uplink,
complicated equalizers are required only at
base stations and not at mobile terminals.
There are several techniques for equalization
such as Zero forcing (ZF) equalization,
MMSE, DFE.
II.SC-FDMA
The block diagram of SC-FDMA is shown in
figure 1. The input and the output of the block
diagram are complex modulated symbols.
The type of the modulation is dependent on
the type of the channel for instance for weak
channel BPSK is adapted while for the strong
channels QAM is adapted. The data block
consists of M complex modulation symbols
generated
at
a
rate
Rsource
(symbols/second). The M point FFT
produces M frequency-domain symbols that
modulate M out of N orthogonal subcarriers spread over a bandwidth.
W  N. fo
1
Where fo is the sub carrier frequency spacing
Rchannel  (
N
) Rsource
M
(2)
The bandwidth spreading factor can be given
as
Q (
Rsource
)
Rchannel
Xk 
M 1
 xme
 j
2
mk
M
( 4)
m0
Where M is the FFT length and after applying
the IFFT it can be expressed as follows
yn 
1
N
N 1
 xne
 j
2
nl
N
(5)
l 0
The transmitter in Fig.1 performs two other
signal processing operations prior to
transmission. It inserts a set of symbols
referred to as Cyclic Prefix (CP) in order to
provide a guard time to prevent Inter-Block
Interference (IBI) due to multipath
propagation. The transmitter also performs a
linear filtering operation referred to as pulse
shaping in order to reduce out-of band signal
energy. The CP is a copy of the last part of
the block [3], inserted at the start of each
block for two reasons. First, the CP acts as a
guard time between successive blocks. If the
length of the CP is longer than the maximum
delay spread of the channel, or roughly, the
length of the channel impulse response, then,
there is no IBI.Second, since the CP is a copy
of the last part of the block, it converts a
discrete-time linear convolution into a
discrete- time circular convolution. Thus,
transmitted data propagating through the
channel can be modeled as a circular
convolution between the channel impulse
response and the transmitted data block,
which in the frequency domain is a pointwise multiplication of the FFT samples.
Then, to remove the channel distortion, the
FFT of the received signal can simply be
(3)
The SC-FDMA system can handle up to Q
orthogonal source signals with each source
signals with each source occupying a different
set of M orthogonal sub- carriers. The signal
after FFT can be expressed as
A.Navya Lakshmi, IJECS Volume 4 Issue 3 March, 2015 Page No.11088-11094
Page 11089
Demodulatio
n
Parallel to
serial
DWT
M-point
N-point
DWT
Subcarriers
Demapping
Npoint
IFFT
A.Navya Lakshmi, IJECS Volume 4 Issue 3 March, 2015 Page No.11088-11094
Radio
Frequency/
An analog to
Digital
Remove
Cyclic Prefix
Digital-to-an
analog/
Radio
Frequency
Add Cyclic
prefix/ Pulse
Mapping
Parallelto- Serial
NPoint
IFFT
Serial-toparallel
N-Point FFT
Sub-carrier
mapping
MPoint
FFT
equalization
Serial-toparallel
Radio
Frequency/ An
analog to
Digital
Remove Cyclic
Prefix
Serial-toparallel
N-Point
FFT
equalization
Sub-Carrier
De mapping/
IFFT
M-point
serial
Parallel-to-
Detect
Channel
Fig 1: Transmitter and Receiver block of the SC-FDMA and OFDMA
Channel
Fig 1: Transmitter and Receiver block of the SC-FDMA and OFDMA
Page 11090
Digital-to-an
analog/ Radio
Frequency
Add Cyclic
prefix/ Pulse
Mapping
Parallel-toSerial
N- Point
IFFT
Sub-carrier
mapping
M- Point
FFT
Serial-toparallel
be divided by the FFT of the channel impulse
response point-wise. The FFT at the receiver
of Fig. 1 transforms the received signal to the
frequency domain in order to recover N subcarriers. The de-mapping operation isolates
the M frequency-domain samples of each
source signal. Because SC-FDMA uses
single-carrier modulation, it encounters
substantial linear distortion manifested as
Inter- Symbol-Interference (ISI). Figure 2
Sub-carriers mapping modes; distributed and
localized.
III. WAVELETS FOR SC-FDMA
Below figure 3 shows the proposed
algorithm block diagram where the
modulation process is applied on the user
data, the resulting signal is transformed by
the wavelet transform via the DWT. The
output of the single-level Haar wavelet
transform consists of two signals, which
represent the approximation coefficients
and the detail coefficients. It can be
expressed as in Equations. (6) and (7). For
two-level Haar wavelet transform, the
approximation coefficients of the first level
are the input to the second level. The output
on this case consists of three signals, which
represent the approximation coefficients and
the detail coefficients signals of the second
level and the detail coefficients signal of the
first level
a1 (m) 
d (m) 
Fig 3(a): Distributed Mode
Fig 3(b): Localized Mode

 x(k ) H
k 
o
( 2m  k )
(6)

 x(k )G (2m  k )
k 
o
(7)
The resulting signal from the sub-carriers
mapping is inserted from into the IDWT to
produce the signal the approximation
coefficients signal and the detail coefficients
signal. After that, we add the CP in order to
prevent the IBI. Finally, the resulting signal
is transmitted through the wireless channel.
At the receiver, the CP is removed from the
received signal, and the signal is transformed
into the frequency domain via an N-point
FFT to apply the equalization process on the
signal. The signal is transformed from the
frequency domain into the time domain via
an N Point IFFT and then it is passed
through an N-point DWT to produce the
approximation coefficients signal and the
detail coefficients signal. Finally, the IDWT
A.Navya Lakshmi, IJECS Volume 4 Issue 3 March, 2015 Page No.11088-11094
Page 11091
IV. SIMULATION RESULTS
Performance of SC-FDMA using DWT for different Channels
0
10
In this section experimental results are shown with the
following parameters taken as input

No. of symbols =512;

Size of block size=128

Channel= AWGN, SUI (Stanford university
-1
--BEr
10
Interim)

Rayleigh-IFDMA
SUI-3-IFDMA
AWGN-IFDMA
Rayleigh-LFDMA
SUI-3-LFDMA
AWGN-lFDMA
-2
10
Equalization =MMSE, ZF
Performance of OFDM using FFT for different Channels
0
-3
10
10
Rayleigh
SUI-3
AWGN
-4
10
-1
---BER
10
0
5
10
15
---EbN0
20
25
30
Fig 6: Performance of SC-FDMA
using DWT for Different Channels
-2
10
Performance of SC-FDMA using FFT and DWT for AWGN Channel
0
10
IFDMA-FFT-AWGN
LFDMA-FFT-AWGN
IFDMA-DWT-AWGN
LFDMA-DWT-AWGN
-3
10
0
5
10
15
---Eb/N0
20
25
30
-1
10
---BER
Fig 4: Performance of OFDM using FFT for
Different Channels
Performance of SC-FDMA using FFT for different Channels
0
10
Rayleigh-IFDMA
SUI-3-IFDMA
AWGN-IFDMA
Rayleigh-LFDMA
SUI-3-LFDMA
AWGN-lFDMA
-1
---BER
10
-2
10
-2
-3
10
10
-3
0
5
10
15
---EbN0
20
25
30
Fig 7: Performance of SC-FDMA using FFT
and DWT for AWGN Channel
10
-4
10
0
5
10
15
--EbN0
20
25
30
Fig 5: Performance of SC-FDMA using FFT
for Different Channels
A.Navya Lakshmi, IJECS Volume 4 Issue 3 March, 2015 Page No.11088-11094
Page 11092
Performance of SC-FDMA using FFT and DWT for SUI-3 Channel
0
Performance analysis of equalizers under different channels
0
10
10
IFDMA-FFT-AWGN
LFDMA-FFT-AWGN
IFDMA-DWT-AWGN
LFDMA-DWT-AWGN
-1
Rayleigh-MMSE
SUI-3-MMSE
AWGN-MMSE
Rayleigh-ZF
SUI-3-ZF
AWGN-ZF
-1
10
---BER
--BER
10
-2
10
-2
10
-3
10
0
5
10
15
---Eb/N0
20
25
-3
30
10
Fig 8: Performance of SC-FDMA using FFT
and DWT for SUI-3 Channel
5
10
15
---Eb/N0
20
25
30
Fig 11: Performance of Equalizers under different
channels
Performance of SC-FDMA using FFT and DWT for Rayleigh Channel
0
0
BER analysis with DWT OFDM
0
10
10
Rayleigh
SUI-3
AWGN
-1
10
-1
--BER
------BER
10
-2
10
-2
10
-3
10
IFDMA-FFT-Rayleigh
LFDMA-FFT-Rayleigh
IFDMA-DWT-Rayleigh
LFDMA-DWT-Rayleigh
-3
10
0
-4
10
5
10
0
5
10
15
15
-----Ebno
20
25
30
---Eb/N0
Fig 12 : BER analysis with DWT OFDM
Fig 9: Performance of SC-FDMA using FFT
and DWT for Rayleigh Channel
Magnitude Response (dB)
0
With -FFT
-10
Propsed -DWT
-20
Magnitude (dB)
-30
-40
-50
-60
-70
-80
-90
-100
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
Normalized Frequency ( rad/sample)
0.4
0.6
0.8
Fig 10: Magnitude Response in db
A.Navya Lakshmi, IJECS Volume 4 Issue 3 March, 2015 Page No.11088-11094
Page 11093
equalization. This work may be further extended with
complex channeling models like ETU (extended terrain
urban) channels and complex wavelet transforms like
dual tree complex wavelets.
BER analysis with FFT & DWT OFDM
0
10
Rayleigh-fft
SUI-3-fft
AWGN-fft
Rayleigh-dwt
SUI-3-dwt
AWGN-dwt
-1
------BER
10
REFERENCES
[1]
-2
10
[2]
[3]
-3
10
[4]
-4
10
0
5
10
15
-----Ebno
20
25
30
Fig 13 : BER analysis with FFT and
DWT OFDM
[5]
V. CONCLUSION
Wavelet based single carrier FDMA system is proposed
in this paper, a clear experimental analysis is conducted
on various channels under different equalization
techniques. Based on the above mentioned experimental
analysis it can be concluded that the orthogonal
wavelets can be substituted for better bandwidth
preservation and the system provides outstanding
performance under Rayleigh fading channel. Zero
forcing algorithms may be adapted for better
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A.Navya Lakshmi, IJECS Volume 4 Issue 3 March, 2015 Page No.11088-11094
Page 11094