Performance analysis of image transmission with various channel conditions/modulation techniques

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 3, June 2020, pp. 1158~1168
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i3.14172  1158
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Performance analysis of image transmission with
various channel conditions/modulation techniques
Marwa Jaleel Mohsin1
, Wasan Kadhim Saad2
, Bashar J. Hamza3
, Waheb A. Jabbar4
1,2,3
Engineering Technical College-Najaf, Al-Furat Al-Awsat Technical University, Iraq
4
Faculty of Electrical & Electronic Engineering Technology, Universiti Malaysia Pahang, Malaysia
4
IBM Centre of Excellence, University Malaysia Pahang, Malaysia
Article Info ABSTRACT
Article history:
Received Sep 21, 2019
Revised Jan 18, 2020
Accepted Feb 7, 2020
This paper investigates the impact of different modulation techniques for
digital communication systems that employ quadrature phase shift keying
(QPSK) and quadrature amplitude modulation (16-QAM and 64-QAM) to
transmit images over AWGN and Rayleigh fading channels for the cellular
mobile networks. In the further steps, wiener and median filters has been
adopted to the simulation are used at the receiver side to remove the impulsive
noise present in the received image. This work is performed to evaluate
the transmission of two dimensional (2D) gray-scale and color-scale (RGB)
images with different values from signal to noise ratios (SNR), such as;
(5, 10 and 15) dB over different channels. The correct conclusions are made
by comparing many of the observed Matlab simulation results. This was
carried out through the results that measure the quality of received image,
which is analyzes in terms of SNRimage peak signal to noise ratio (PSNR) and
mean square error (MSE).
Keywords:
AWGN
Gray-scale and color-scale
(RGB) images
Mean square error (MSE)
Peak signal to noise ratio
(PSNR)
Rayleigh fading channel This is an open access article under the CC BY-SA license.
Corresponding Author:
Waheb A. Jabbar,
Faculty of Electrical & Electronic Engineering Technology,
Universiti Malaysia Pahang (UMP),
26600, Pekan, Pahang, Malaysia.
Email: 1
marwa.jaleel@atu.edu.iq, 2
was-saad@atu.edu.iq, 3
coj.bash@atu.edu.iq, 4
waheb@ieee.org
1. INTRODUCTION
Wireless channels are extremely random and time-varying, which propagates across multiple paths,
where it is unlike fixed and predictable wire channels. However, the proliferation of multiple paths in
the wireless system is a challenge [1-3]. The resulting phenomenon via the arrival of multiple copies of
the signal sent to the receiver, causes the inter symbol interference (ISI) to be distorted, this produced highly
distorted the signal sent at the receiver. In the same time, this results in time attenuation, dispersion and phase
offset in the receiving signal, which is known as fading [4-6].
In the digital communication system, modulation can be defined as the process of sending a signal
message, where the information are shows as the bits and the input image is utilized in the different modulation
techniques [7-9]. As a result, using the 64-QAM modulation technique will improve the degraded image, this
is necessary for image transfer and provides the best results compared with different modulation techniques,
like QPSK and 16-QAM because it has higher data rates. Thus, the performance of the system would be
acceptable even with a certain level of noise from the critical channel. In another meaning, the performance of
the system can not be tolerable when the noise level is higher than this critical level [10-12].

TELKOMNIKA Telecommun Comput El Control 
Performance analysis of image transmission with various channel… (Marwa Jaleel Mohsin)
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Fading occurs because of overlapping between two or more from the signals sent to the receiver at
slightly different times. However, these waves are multipath waves that unite in the receiving antenna and give
the result of a signal that can vary greatly in amplitude and phase, according to the transmitted signal width,
the relative propagation time of the waves and the distribution of intensity [13-15]. Unexpected delays change
in regarded with various signal paths in a multipath fading channel, as well as can be statistically distinguished
only. However, the central limit theorem can be applied as a random process of the complex-valued Gaussian
to model the time-variant impulse response of the channel when there are a large number of paths. If the impulse
response is designed as a Gaussian process with zero complex value, it is said that the channel is Rayleigh
fading channel [16, 17]. However, Rayleigh’s models assume that the signal magnitude that passed through
this communication channel will change randomly or fades based on Rayleigh’s distribution. Rayleigh fading
is regarded as a reasonable model for the propagation of tropospheric and ionospheric signals and highly-built
urban environments effect on the radio signals when there are a lot of objects in the environment that spread
the radio signal before it reaches to the receiver. If there is too much scattering, the channel pulse response will
be well designed as a Gaussian process according to the central limit theorem regardless of the individual
components distribution, where there are zero mean and phase equally distributed between 0 and 2 radians
when there is no predominant component to the scatter. Thus, the response envelope of the channel will be
distributed to Rayleigh [18].
Image filtering do not use to improve the image quality, but it is also utilized as a pre-processing stage
in many applications, which includes; image compression, pattern recognition, image encryption, and target
tracking. However, noise reduction is utilized to restore the ideal image from the degraded version of the image.
Image filtering algorithms are used to eliminate the various noise types that exist either in the image while
being captured or injected into the image through the transmission. Thus, Median Filter is performed through
taking the magnitude of each vectors within the mask and sorted based on the magnitudes, then,
the medium-magnitude pixel is utilized to replace the studied pixels. While, the Wiener Filter performs an
optimal trade-off between the noise smoothing and the inverse filtering because it reflects camouflage and
eliminates the additive noise simultaneously [19-21].
The authors discuss in [18] the effect of various equalization techniques that use MPSK and MQAM
modulation technology for digital communication systems to transfer the information of color image on
the Rayleigh fading channel. The analysis of this performance has been made in terms of bit error rate through
use Matlab simulation. The solution for this problem is to increase the SNR value. Thus, the various properties
values, such as; average path gain, path delay, doppler spectrum and maximum doppler shifts parameters are
selected accurately in the simulate the Rayleigh fading channel to turns out a realistic fade channel. However,
the size of the step and the forgetting factor, etc. is also chosen for the various adaptive algorithms utilized by
the linear equalizer (LE) and decision feedback equalizer (DFE) correctly. Moreover, a compression was used
to transfer the color image that provides 82% less data compare with the uncompressed JPEG image. Each
analysis offers the worst equalizer for the Rayleigh fading channel linear equalizer with the LMS adaptive
algorithm. In addition, to reduce the signal dispersion and ISI, the maximum likelihood sequence estimate
(MLSE) equalizer provides the better equalization. However, the results explained that both of DFE and LE
adaptive algorithm equalization gives best result than l ms. While, DFE offers the best performance compare
with the linear equalizer, other than that it gives the poor performance with the lower SNR. But the MLSE
equalizer gives the better performance among all the equalizers used in this work. In general, all the results
explained that utilize of the 4QAM modulation method and the MLSE equalizer give a best result for
transferring the color JPG color images.
In [11] the authors demonstrated that the transmission of image via digital wireless communications
by used different digital modulation techniques, like; QPSK, 16-QAM and 64-QAM are implemented through
utilized the AWGN channel to design and implement the wireless digital communication system as well as
know the better modulation technique suitable for transmitting the image. However, the performance of this
system will be acceptable like many other digital wireless communications systems to reach a appointed noise
level from the critical channel. In another meaning, the system performance cannot be very quickly when
the noise level is higher than the critical level. The current designed system in this work generates worse image
quality instead of losing the image sent completely when the channel is in high noise. Simulation results
explained that, 64-QAM modulation techniques has higher data rates, this is necessary for image transfer, offer
best results compare with the another modulation techniques (i.e., QPSK and 16-QAM) when the SNR value
is 10 dB.
The authors in [22] concentrated on the transfer of images by used M-QAM OFDM system for
the fading channel. The Generalized K Fading channel is evaluation through sending still images by
the 16-QAM- OFDM modulated system with various values from fading and shading parameters. The images
sent through composite fading channels will be exceptionally sensitive to the influence of multipath
propagation, which may result the image quality degradation in the receiver. The simulation results showed

 ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 3, June 2020: 1159 - 1168
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that M-QAM-OFDM resulted a good tradeoff between symbol-error durability, throughput and reduce of
the peak-to-average ratio of the OFDM signal. In addition, the generalized K Fading channel is very effective
for prediction the fading and shadowing performance as well as showing its effects at the same time. Moreover,
the results examined show the influence of fade and shading from where of bit error rate and visual quality of
the received images. However, because of the mobile radio signal casing usually consists of a fast fading
component overlay on a slowly fading component, thus, it is clear that from the results presented the channel
model which consists of mixed distributions will be useful in design the wireless systems and networks.
Furthermore, one type of this digital data, which corresponds to to a two-dimensional gray-size (2D)
image is utilized for evaluation the overall of functionalion and the performance of the OFDM system with
the impact of the AWGN channel in [22]. Various configurations of the modulation techniques, likes; M-PSK
and M-QAM are considered within the OFDM system for evaluation of the system as well as the necessary
correct conclusions are made from comparing many of noted simulation results in Matlab. However, the OFDM
system was implemented using various modulation techniques to transmit images via the AWGN channel.
The recovered image quality is best at high SNR values regardless of the utilized modulation method.
In the case of low-SNR, the quality of the recovered image is very low because of high amount of noise in
the AWGN channel. In this work, it was found that OFDM system provides fewer errors, higher recovered
image quality at the receiver and less BER with 16-QAM modulation technique compared to the OFDM
systems applied with other techniques.
This paper analyze the performance of image transfer across different channels which display various
effects on image transfer performance through use different filters. In this work, there are two types of digital
data which corresponds to a 2D gray-scale and color-scale (RGB) of JPEG format images are used for
transmission data and assess the comprehensive performance and the functionality of the system with the effect
of AWGN and Rayleigh fading channels. The various channels show various impacts on the execution of
the transfer of images utilizing different modulation techniques. However, wiener and median filters are used
at the receiver side to remove the impulsive noise present in the received image. The remainder of the paper is
orderly as follows: the proposed image transmission system model is discussed in section 2. Section 3 presents
and discusses the performance evaluation of the proposed system, where the simulation results and discussion
for the proposed system is highlighted. Finally, section 4 is drawn the concluding remarks of this paper.
2. PROPOSED TRANSMISION SYSTEM MODEL
Figure 1 explain the implemention of the system model, which describes the sequence of events utilize
in the current work to send Gray-scale and RGB images of size 512x512 over AWGN and Rayleigh Fading
channels, respectively. The communication channel is designed by adding the Gaussian white noise effect and
the carrier influence. The images are reshaped based on the modulation technique used, and then sent through
these channels. The test image chosen for evaluation of the proposed image transfer system appears in
Figures 2 and 3. This virtual image is existing with JPG format, size 512 x 512 with 262144 pixels, where each
pixel 8-bits, and accessible through the name "Penguin Image". To transfer this image available in a two
dimensional (2D) matrix or signal, we need to do some pre-processing to convert this 2D image to a one
dimensional (1D) signal. In the start, read the image that is obtainable from Matlab database. After that, we
change it to a double coordinate then restructure the data to convert them from the (512x512) matix
representation to the (1×262144) vector representation, and then we convert it to the binary that results a vector
of binary data (512×512×8=2097152 bits) for gray-scale image and (512×512×8×3) for RGB image, where
this image becomes now ready for transmission. This data is used as a data source or input signal for image
transmission system, where this source data is modulated through use either QPSK, 16-QAM, or 64-QAM
techniques. Finally, according to the modulation method utilized, we must be conversion the vector data to
an appropriate format.
For instance, if we utilize QPSK modulation technique, we will conversion the vector data to
the binary data (four signal elements, i.e., 00, 01, 10 and 11, which are 0, 1, 2, and 3, respectively). However,
it will be a good comparison if we use another modulation techniques as the transmitter modulation, such as;
16-QAM and 64-QAM. Thus, for 16-QAM modulation, which corresponds to (sixteen signaling elements, i.e.,
0000, 0001, to 1111, which are 0, 1, to 15, respectively), and for 64-QAM modulation, which is compatible
with (sixty four signaling elements, i.e., 000000, to 111111, which are 0 to 63, respectively). On the receiver
side, the opposite operations are achieved to effectively restore the transfered original image after recovering
the digital bits. The system parameters utilize in this simulation will be tabulate in Table 1, which displays
the characteristic of the original image used and the corresponding simulation parameters values that are
consider in the simulation Matlab.
However, there are two types of objective metrics that are widely utilized in the evaluating image
transmission techniques, such as: Peak Signal to Noise Ratio (PSNR), which is utilized to measure the image

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quality, also, Mean Square Error (MSE), which uses to measure the image distortion and may be explained as
the square of error between the stego ( )jiP xR , and cover ( )jiPxT , images. Thus, the reconstructed images quality
with size M×N in terms of PSNR is given by [23-25]:






=
MSE
PSNR
2
10
255
log10 (1)
where;
( ) ( ) = =
−

=
M
i
N
j
RT jiPjiP
NM
MSE xx
1 1
2
,,
1 (2)
Figure 1. System model
Table 1. Features of penguin test image and Matlab simulation parameters
Properties Corresponding Values
Original Image Size (512 x 512) pixels
Total Pixels 262144
Size data for each pixel 8-bits
Size of the signal elements or source signal for the QPSK
transmission size
262144×8/2 = 1048576 (gray-scale image)
262144×8×3/2 = 3145728 (RGB image)
Size of the signal elements or source signal for the 16-QAM
transmission size
262144×8/4 = 524288 (gray-scale image)
262144×8×3/4 = 1572864 (RGB image)
Size of the signal elements or source signal for the 64-QAM
transmission size
262144×8/6 = 349525 (gray-scale image)
262144×8×3/6 = 1048576 (RGB image)
Modulation Schemes QPSK, 16-QAM, 64-QAM
Channel type AWGN and Rayleigh fading
Range of SNR in dB 5, 10 and 15
3. RESULTS AND PERFORMANCE ANALYSIS
In this work, two types of digital data which represents two-dimensional (2D) gray-scale and RGB
images are utilized to assess the overall performance and the functions of the proposed system with the effect
of typical AWGN and Rayleigh fading channels and various filters, which uses on the receiver side to remove
the impulsive noise in the received image in Matlab simulation. However, various configurations of prominent
modulation techniques, like; QPSK, 16-QAM and 64-QAM have been used in this work to evaluate
the proposed system as well as make the correct conclusions for comparing many results of simulation Matlab.
In the first part of this section, we are going to compare the result of the two types of images transmission with
three different SNR over AWGN channel. In the next step we compare the results with the simulation and
the theoretical performance of the image transmission over over the Rayleigh fading channel. However,
the graph will analyze the comparison between two images which utilizes to discuss the performance.
The system has been implemented as described in section 2, with use QPSK, 16-QAM and 64-QAM as
a modulation technique for analysis the performance of image transfers by using various channel conditions

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with and without two types from filters through Matlab simulation. These filters are Wiener and Median,
respectively, which uses to remove the noise from the image. Thus, we can see that the simulation results are
not the same as the original image, but they are almost identical. Simulation results are conducted to compare
the images recovered for three various SNR values are 5, 10 and 15 dB, respectively, which conforming to
various modulation techniques. Tables 2-7 are a good tool to come up the system performance comparison for
MSE, PSNR and SNR image on different types of images, SNR channel, filters, and modification techniques
for AWGN and Rayleigh fade channels. Obviously from the PSNR results, the system works well in the
AWGN channel compare with the Rayleigh fading channel, as well as with a slight increase in signal strength,
the image will be received ideally, where the higher PSNR will improve the quality of recovered image.
Table 2. Comparison MSE for different images, modulation schemes and filters (AWGN channel)
64-QAM16-QAMQPSK
SNR
channel
(dB)
Image
Types
Median
filter
(dB)
Wiener
filter
(dB)
Withou
t filter
(dB)
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
Median
filter
(dB)
Wiener
filter (dB)
Without
filter
(dB)
1.2620e+
03
1.9147e+
03
5.360e
+03
280.654352.8171.5378e
+03
69.3990344.4691.011e+
03
5
Gray-
scale
Image
442.5251.0820e+
03
3.817e
+03
70.6717128.085540.26456.956943.786630.237810
156.2942570.05492.605e
+03
55.681063.1253163.71146.585540.628722.679415
1.2280e+
03
1.6364e+
03
5.2678
e+03
310.983290.0131.6574e
+03
78.5478341.577958.9945
RGB
Image
461.74731.0582e+
03
3.7758
e+03
66.0657137.857580.78344.693649.47020.170110
101.2831558.84092.2483
e+03
50.797857.1098102.65234.535246.88640.046915
Table 3. Comparison PSNR for different images, modulation schemes and filters (AWGN channel)
64-QAM16-QAMQPSKSNR
channel
(dB)
Image
Types Median
filter
Wiener
filter
Without
filter
Median
filter
Wiener
filter
Without
filter
Median
filter
Wiener
filter
Without
filter
17.120214.334310.839223.649120.292216.261729.591422.717018.07335Gray-
scale
Image
21.671417.476512.313630.341525.995620.804731.413830.213734.631710
28.394720.766913.973032.914330.094625.990035.448332.227665.221015
18.238915.750111.014523.931121.105015.936529.179522.795918.31265
RGB
Image
22.486817.747912.360730.782527.736520.490732.011430.470535.083710
29.247620.623814.612332.511730.798030.017232.757231.346061.421415
Table 4. Comparison PSNR for different images, modulation schemes and filters (Rayleigh fading channel)
64-QAM16-QAMQPSKSNR
channel
(dB)
Image
Types
Median
filter
Wiener
filter
Without
filter
Median
filter
Wiener
filter
Without
filter
Median
filter
Wiener
filter
Without
filter
14.039513.41969.589216.920214.425110.708217.759314.619012.88255Gray-
scale
Image
16.185714.769210.530526.371922.236118.099031.339029.315824.774610
25.104816.454912.645232.405628.719823.418732.924130.470540.595915
16.457115.151110.688622.688820.420414.409927.341520.700316.11795
RGB
Image
19.169016.169511.272929.292527.314819.164030.437628.996027.234610
28.653422.800714.142532.222630.742128.749932.762327.192949.910615
Table 5. Comparison SNRimage for different images, modulation schemes and filters (AWGN channel)
64-QAM16-QAMQPSK
SNR
channel
(dB)
Image
Types
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
6.62905.26333.48859.89358.33596.199812.86469.44457.10565Gray-
scale
Image
8.90466.96324.225712.939711.08538.471313.775812.692815.384810
12.78339.00385.055415.526113.133311.063918.793114.199828.019515
6.31805.69453.15589.24518.01975.666812.28839.09656.85495
RGB
Image
8.94416.64123.878913.089811.06687.943914.074313.133815.240410
12.82248.02755.004713.954413.097612.707115.080613.971528.409215

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Table 6. Comparison MSE for different images, modulation schemes and filters (Rayleigh fading channel)
64-QAM16-QAMQPSK
SNR
channel
(dB)
Image
Types
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
2.1565e+
03
2.4281e+
03
7.1477e+
03
1.222+e
03
1.964+e
03
4,268+e
03
1.0893
e+03
1.9391
e+03
3.3067e+
03
5
Gray-
scale
Image
1.5650e+
03
1.8417e+
03
5.7548e+
03
148.758370.498
1.0074e
+03
47.773
3
75.524
4
280.52910
1.3269e+
03
1.9419e+
03
4.1520e+
03
59.226486.6346295.945
47.773
3
75.524
4
59.843415
1.4720e+
03
1.7592e+
03
5.5491e+
03
440.757558.290
2.3556e
+03
119.93
0
549.05
2
1.5896e+
03
5
RGB
Image 787.369
1.4382e+
03
4.8505e+
03
88.531182.282650.849
58.792
7
81.936
6
97.237610
152.663371.024
2.5014e+
03
50.01358.124111.712
54.556
7
124.10
6
0.663815
Table 7. Comparison SNRimage for different images, modulation schemes and filters
(Rayleigh fading channel)
64-QAM16-QAMQPSK
SNR
channel
(dB)
Image
Types
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
Median
filter
(dB)
Wiener
filter
(dB)
Without
filter
(dB)
6.14274.52413.02416.21045.50213.42307.19486.09644.51025Gray-
scale
Image
6.42525.68713.424111.27199.29047.118413.784812.74399.983210
8.88146.59243.824113.271812.44599.778414.784813.743918.366915
6.25244.12653.04298.54308.02974.903511.36938.06575.75755
RGB
Image
6.65244.65983.254612.124111.02147.080512.917312.196611.015910
12.52198.79324.928712.421011.919111.073113.079711.295022.653915
In addition, Figures 2 and 3 show comparison of recovered images at the system receiver without
filter, with median and wiener filters for different modulation schemes over AWGN and Rayleigh fading
channels, respectively. However, the recovered image clearness changed by the modulation type and the filters
used which gives various values of MSE, PSNR and SNR for AWGN channel and Rayleigh fading channels,
respectively, based on the simulation results of the proposed algorithm to different values of SNR. It is seen
that as expected the values of the PSNR and SNRimage of the received images increase with decrease of MSN
when increase of the SNR values with and without filter used, where increase the value of these parameters
with used median and Wiener filters respectively.
Furthermore, Figure 4 displays transmitted information as digital signal, serial symbol for M-array at
the transmitter and waveform for M-array according to the symbolic information for different modulation
schemes. Finally, Figure 5 presents re-obtain symbol and receiving information as digital signal after M-array
for different modulation schemes. However, as we can be view from the images received, the basic problem is
the transmission via the fading channels, which represent the actually existed channels in the wireless
communications. Thus, the results explain that the received images quality decreases due to the rapid
movement of the receiver. In addition, QPSK modulation technique give a better result of recovered images
and it is more immune to the noise than other modulation techniques, because when the number of transmitted
bits is increase, they are more susceptible to the interference. In other words, 64-QAM modulation technique
give a worse result because it be more susceptible to the intersimple interference (ISI).
From Figure 3, the first images in the left column are the original input images. The gray scale and
RGB images are the images used by different modulation techniques, like; QPSK, 16-QAM and 64-QAM will
be applied in the communication system. However, the execution of the system will not be fast when raised
the noise level above the critical level. The feature of the designed system in this work is that if the channel is
with high noise, this makes the system generates the image quality worse rather than losing the completely
image sent. Furthermore, the results of the simulation analysis for (2D) Gray and RGB images which utilizes
for transmission data are presented in comparison to the theoretical results. Thus, the recovered image quality
is best for higher values from SNR regardless of the modulation method utilized. While, in case of low-SNR,
the recovered image quality is very low because of high amount of AWGN noise.

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(a)
(b)
(c)
Figure 2. Comparison of recovered images at the system receiver without filter, with median filter and
with wiener filter for different modulation schemes over AWGN channel;
(a) QPSK modulation scheme, (b) 16-QAM modulation scheme, and (c) 64-QAM modulation scheme

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(a)
(b)
(c)
Figure 3. Comparison of recovered images at the system receiver without filter, with median filter and
with wiener filter for different modulation schemes over Rayleigh fading channel;
(a) QPSK modulation sheme, (b) 16-QAM modulation scheme, and (c) 64-QAM modulation sheme

 ISSN: 1693-6930
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Figure 4. Transmitted information as digital signal,
serial symbol for M-array at the transmitter and
waveform for M-array according to symbolic
information for different modulation schemes
Figure 5. Re-obtain symbol and receiving
information as digital signal after
M-array for different modulation schemes
Figure 6 demonstrates the PSNR vs. SNR curves over AWGN channel for comparison between gray
scale and RGB images for different modulation schemes. It is shown that the higher PSNR value can be get it
when used QPSK compare with other modulation schemes. In addition, Figure 7 explains the PSNR vs. SNR
curves over AWGN channel without filter, with median filter and with Wiener filter, respectively, using QPSK
modulation scheme. As expected, it is seen that increase of the PSNR for the received image without and with
(median and Wiener) filters used, where this is improving the quality of image for higher PSNR.

1167
Figure 6. PSNR vs. SNR for AWGN channel and different modulation schemes
Figure 7. PSNR vs. SNR for AWGN channel and QPSK modulation scheme
4. CONCLUDING REMARKS
In this paper, we analyze the performance of image transmission over wireless digital communications
through present a comparison between data handling using different modulation schemes under various channel
conditions and to identify the better appropriate modulation technique for transmitting images. Generally, we
examined the influence of two varios channels of AWGN and Rayleigh fading on image transfer in the wireless
transceiver model. However, the transmission of images through the communication system is performed by
used digital modulation techniques as well as the results are got through used Matlab software that was
introduced to design and implement the wireless digital communication system. Thus, the carried out of this
system which reaches to a certain level of noise from the critical channel is acceptable. In another meaning,
when raised the noise level above the critical level, the system performance cannot be very quickly.
The simulation results showed that the AWGN channel gives the best result than the Rayleigh fading through
used the same SNR, which comes with a less efficient results. In addition, when the channel of the currently
designed system is in high noise, the system generates worsing in image quality instead of losing the sent
image completely.
ACKNOWLODGMENT
This work is supported in part by Engineering Technical College-Najaf, Al-Furat Al-Awsat Technical
University and in part by the Universiti Malaysia Pahang (www.ump.edu.my) via Research Grant UMP-IBM
Centre of Excellence RDU190304.

 ISSN: 1693-6930
1168
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