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Irish Interdisciplinary Journal of Science & Research (IIJSR)
Volume 7, Issue 2, Pages 34-41, April-June 2023
ISSN: 2582-3981 https://iijsr.com
34
A MATLAB Simulink Study on the Performance of QAM Modulation Scheme
in AWGN, Rayleigh, and Rician Fading Channels: BER Analysis
Amit Halder1*, Mst. Naila Akter2, Jayanta Roy3, Md. Riyad Tanshen4 & Mir Afzal Hossain5
1-5Department of EEE, World University of Bangladesh, Dhaka-1230, Bangladesh.
Corresponding Author (Amit Halder) E-mail: amit.rueten@gmail.com*
DOI: https://doi.org/10.46759/IIJSR.2023.7205
Copyright © 2023 Amit Halder et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Article Received: 17 March 2023 Article Accepted: 24 April 2023 Article Published: 30 April 2023
░ 1. Introduction
Wireless communication has witnessed significant growth over the past few years, with mobile communication
being the most widely used [1]. However, the transmission of wireless signals is prone to various technical
challenges such as fading, shadowing, interference, and propagation path loss, which have a detrimental impact on
signal quality [2]. To address these challenges and ensure high-quality service, orthogonal frequency division
multiplexing (OFDM) has emerged as a suitable technique for high-bandwidth data transmission. OFDM converts
wideband signals into narrowband signals for transmission using orthogonal carriers. One critical aspect of wireless
communication is bit error rate (BER) performance [3]. In this context, this paper presents a study on BER
performance using Quadrature Amplitude Modulation (QAM) model under different communication channels,
including additive white Gaussian noise (AWGN) and fading channels (Rayleigh and Rician), using MATLAB.
The study aimed to investigate the reduction of noise and bit error rate in communication channels.
In 2012, Vineet Sharma et al., conducted a study to determine the performance of OFDM-8PSK and QAM systems
using direct error correction codes in the AWGN channel. The study aimed to encode the data stream for wireless
communication without wire [4]. In 2013, Sharif Nasr Abdel-Razek et al., modelled the QAM modulator and
demodulator using MATLAB Simulink. They highlighted the widespread use of QAM modulation in digital
communication systems due to its high bandwidth utilization [5].
Sanjeev Kumar et al. (2013) compared the performance of Rayleigh and Rician channel models using MATLAB
modelling. They developed algorithms to calculate the envelope and the likelihood of failure in the fading channels,
considering parameters such as source speed and interruption probability. This analysis is crucial in designing
efficient digital communication systems that can withstand multipath blurring [6]. Singya et al. (2020) analyzed the
ABST RACT
Wireless communication is the fastest-growing segment in the communication industry, with mobile communication being the most widely used.
However, it faces several technical challenges, such as Fading, Shadowing, Interference, and Propagation path loss. Meeting the higher demand for
capacity with high-quality service is crucial. Orthogonal Frequency Division Multiplexing (OFDM) is a technique that converts wideband signals
into narrowband signals for transmission, making it a suitable option for high bandwidth data transmission. The transmission of these narrowband
signals is executed with an orthogonal carrier. This paper focuses on building a QAM model using MATLAB to simulate Bit Error Rate (BER)
performance for real data communication under different communication channels, including AWGN and fading channels (Rayleigh and Rician).
The aim is to investigate the reduction of noise and bit error rate in communication channels. The simulation model built for this research work
demonstrates that QAM scheme performs better in AWGN channels than Rayleigh or Rician fading channels.
Keywords: Digital modulation; Quadrature Amplitude Modulation (QAM); AWGN; Rayleigh fading; Rician fading.
Irish Interdisciplinary Journal of Science & Research (IIJSR)
Volume 7, Issue 2, Pages 34-41, April-June 2023
ISSN: 2582-3981 https://iijsr.com
35
performance of a dual-hop variable gain AF mixed RF/FSO system with outdated channel state information. They
model the RF link with the generalized Nakagami-m fading and the FSO link with the Gamma-Gamma distribution
affected by atmospheric turbulence and pointing error impairments. They derive analytical expressions of outage
probability, ergodic capacity, and generalized average symbol error rate for various QAM schemes, and present a
comparative study highlighting the impact of various impairments on system performance [7]. Forkan et al. (2022)
provides a comparative analysis of the performance of two widely used modulation techniques, BPSK and 8-FSK,
over an AWGN fading channel in wireless communication systems. The authors use MATLAB Simulink software
to synthesize the design and analyze the error rate quantity of each technique. The findings indicate that BPSK
outperforms 8-FSK modulation in terms of error rate, which can be useful in determining the best technique for
implementing in an AWGN fading channel [8]. Farzamnia et al. (2022) present a study on the bit error rate (BER)
performance of M-PSK and M-QAM schemes in AWGN, Rayleigh, and Rician fading channels. The authors
investigate the impact of channel fading on the performance of these modulation schemes and analyze the BER for
different values of signal-to-noise ratio (SNR). The study highlights the importance of choosing an appropriate
modulation scheme for different channel conditions in order to ensure reliable communication [9].
The proposed QAM model is an effective tool for evaluating BER performance in real-world data transfer settings.
The results show that the QAM modulation method outperforms Rayleigh and Rician fading channels in AWGN
fading channels. The contribution of this work is noteworthy because it provides insights into the performance of
QAM in different fading channels, which can be used to design better wireless communication systems.
░ 2. Design Methodology
The MATLAB environment was used to analyze QAM modulations, and the effects of various types of noise on
QAM were studied by adding them and analyzing the results using the Communication blockset in MATLAB
Simulink. The methodology used in this research work involved building a QAM model using MATLAB to
simulate Bit Error Rate (BER) performance for real data communication under different communication channels,
including AWGN and fading channels (Rayleigh and Rician). The QAM model was constructed by implementing
orthogonal frequency division multiplexing (OFDM) technique which converts wideband signals into narrowband
signals for transmission. The transmission of these narrowband signals was executed with an orthogonal carrier.
The simulation model was used to investigate the reduction of noise and bit error rate in communication channels.
The performance of the QAM model was evaluated by analyzing the BER performance under different
communication channels, including AWGN and fading channels (Rayleigh and Rician). The results obtained were
then compared and analyzed to determine the best performing channel for QAM communication.
2.1. Numerical Equations
Quadrature Amplitude Modulation (QAM) is a modulation scheme that transmits digital data by simultaneously
varying the amplitude and phase of the carrier signal. The mathematical equation for a QAM signal is [10]:
]}[][Re{)( 2memAts tfj m
(1)
Where s(t) is the QAM signal, A[m] is the amplitude of the mth symbol, fm is the frequency of the carrier signal,
][m
is the phase of the mth symbol, and Re{} denotes the real part of the complex signal. The symbol rate is given
Irish Interdisciplinary Journal of Science & Research (IIJSR)
Volume 7, Issue 2, Pages 34-41, April-June 2023
ISSN: 2582-3981 https://iijsr.com
36
by 1/Ts, where Ts is the symbol period. The number of bits that can be transmitted per symbol depends on the
number of points in the QAM constellation, which determines the amplitude and phase combinations used for each
symbol. For example, a 16-QAM constellation has 16 points and can represent 4 bits per symbol.
The equation for the additive white Gaussian noise (AWGN) channel is given by [11]:
)()()( tntsty
(2)
Where y(t) is the received signal, s(t) is the transmitted signal, and n(t) is the noise introduced by the channel. The
noise n(t) is modeled as a Gaussian process with zero mean and a power spectral density of N0/2, where N0 is the
noise power.
The equation for Rayleigh fading channel can be represented as follows [12]:
)),1(),1((2 LirandnLrandnh
(3)
Where:
h is the complex fading coefficient representing the channel response;
L is the length of the channel;
randn(1,L) is a Gaussian random variable with mean 0 and variance 1;
i is the imaginary unit;
2
is a normalization factor to ensure that the average power of the channel is 1/2.
The fading channel response h varies randomly with time and position, and represents the attenuation and phase
shift experienced by the transmitted signal.
The equations for the Rician fading channel [13]:
j
e
k
k
H)
1
(
(4)
Where H is the complex gain of the channel, K is the Rician factor, and Theta is a random phase uniformly
distributed between 0 and 2π.
The power of the Rician channel can be expressed as:
)1( k
P
Pdir
ric
(5)
Where,
dir
P
is the power of the direct path and
ric
P
is the total power of the Rician channel.
2.2. Block Set Configuration
This study presents simulation models for different digital modulation techniques, with a focus on Quadrature
Amplitude Modulation (QAM) as one of the most widely used techniques. The simulation involves feeding a
Irish Interdisciplinary Journal of Science & Research (IIJSR)
Volume 7, Issue 2, Pages 34-41, April-June 2023
ISSN: 2582-3981 https://iijsr.com
37
Bernoulli Binary Generator into the QAM modulation scheme and evaluating its performance under various noise
conditions such as AWGN, Rayleigh, and Rician fading channels. The Bit Error Rate (BER) is calculated through
adaptive filter simulations using MATLAB.
Figure 1. Block diagram of AWGN Fading channel on QAM modulation scheme
Figure 2. Block diagram of Rayleigh Fading channel on QAM modulation scheme
Figure 3. Block diagram of Rician Fading channel on QAM modulation scheme
In this research, the impact of AWGN, Rayleigh, and Rician fading channels on the Bit Error Rate (BER) of
Quadrature Amplitude Modulation (QAM) schemes is studied through MATLAB simulations. The effects of
varying parameters such as sample time, signal power, initial seed, sample period, maximum Doppler shift,
k-factor, and average path gain on the QAM technique are investigated.
░ 3. Numerical Outcomes and Discussion
By using the MATLAB Communication Blockset and Simulink the following effects of different fading channels
were observed.
3.1. Effect of AWGN fading channel on QAM Modulation Scheme
The impact of an AWGN channel on the performance of the QAM modulation scheme was examined by analyzing
the bit error rate (BER) relative to the signal-to-noise ratio (SNR) of the fading channel. The analysis of the AWGN
fading channel involved assessing two parameters: the input or transmitted signal power and the SNR. The
variation of the BER with respect to the SNR of the channel for different input or transmitted signal powers was
plotted in Figure 4. The results showed that the BER decreased gradually as the SNR increased, and the lowest BER
was observed for low transmitted power. For input signal powers of 200mW, 2000mW, and 100mW, the BER was
Irish Interdisciplinary Journal of Science & Research (IIJSR)
Volume 7, Issue 2, Pages 34-41, April-June 2023
ISSN: 2582-3981 https://iijsr.com
38
recorded as 0.1818 from 20 dB to 55 dB, while from 65 dB to 90 dB, the BER was recorded as 0.09091 for the same
input signal powers. The observation was conducted using an initial seed of 100 and a sample time of 1 second.
Figure 4. Plot of SNR vs. BER for AWGN fading channel
3.2. Effect of Rayleigh fading channel on QAM Modulation Scheme
In this analysis, as shown in Figure 5, the impact of the multipath delay vector on the BER performance of QAM
modulation in the Rayleigh fading channel was studied. The multipath delay vector was varied from [0 1e-3]
seconds to [0 9e-3] seconds, while the multipath gain vector was changed with three different reference values: [0
0], [0 2], and [0 -2] dB.
Figure 5. Plot of Delay Vector vs. BER for Rayleigh fading channel
The maximum Doppler shift was set to 40 Hz, with an initial seed of 100 and a sample time of 0.0025 seconds. The
results showed that the BER was higher in the Rayleigh fading channel compared to the AWGN fading channel.
10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Signal to Noise Ratio (SNR), Eb / N0 (dB)
Bit Error Rate (BER)
200 mW
2000 mW
100 mW
[0 1e-3] [0 2e-3] [0 3e-3] [0 4e-3] [0 5e-3] [0 6e-3] [0 7e-3] [0 8e-3] [0 9e-3]
0.87
0.872
0.874
0.876
0.878
0.88
0.882
0.884
0.886
Delay Vector (sec)
Bit Error Rate(BER)
Gain Vector=[0 0]dB
Gain Vector=[0 2]dB
Gain Vector=[0 -2]dB
Irish Interdisciplinary Journal of Science & Research (IIJSR)
Volume 7, Issue 2, Pages 34-41, April-June 2023
ISSN: 2582-3981 https://iijsr.com
39
Additionally, setting the multipath gain vector to [0 -2] dB resulted in better performance than the other two values.
The findings of this analysis highlight the importance of understanding the effects of various parameters on the
performance of wireless communication systems, particularly in a fading channel such as the Rayleigh fading
channel.
3.3. Effect of Rician fading channel on QAM Modulation Scheme
This study investigates the impact of various parameters on the performance of Quadrature Amplitude Modulation
(QAM) in the Rician fading channel. Unlike the Rayleigh and Additive White Gaussian Noise (AWGN) fading
channels, the Rician fading channel involves more parameters for evaluation. These parameters include the K
factor, multipath gain vector, delay vector, maximum diffuse Doppler shift, and Doppler shift of the direct path. For
this study, the K factor was set to 1, the Doppler shift of the direct path was set to 0 Hz, and the maximum diffuse
Doppler shift was set to 40 Hz.
The observation was carried out for the Bit Error Rate (BER) performance of QAM with respect to the delay vector,
which was varied from [0 1e-3] to [0 9e-3] seconds. Additionally, the multipath gain vector was changed with three
different reference values: [0 0], [0 2], and [0 -2] dB. The results showed that the BER performance in the Rician
fading channel was lower than that of the Rayleigh fading channel but still higher than that of the AWGN fading
channel. Figure 6 displays the lowest BER recorded for the different multipath gain vectors. The results indicate
that the QAM performance was much better when the multipath gain vector was set to [0 -2] dB. The trajectories of
the curve were quite similar in the delay vector range, with optimal BER at [0 0] dB, lower BER at [0 -2] dB, and
higher BER at [0 2] dB. These findings suggest that setting the multipath gain vector to a negative value results in
better QAM performance than setting it to a positive value.
Figure 6. Plot of Delay Vector vs. BER for Rician fading channel
Based on the findings of this outcome, it is recommended that wireless communication systems be optimized for
performance in the Rician fading channel by setting the multipath gain vector to a negative value. This optimization
could lead to improved reliability and performance of wireless communication systems in real-world scenarios.
[0 1e-3] [0 2e-3] [0 3e-3] [0 4e-3] [0 5e-3] [0 6e-3] [0 7e-3] [0 8e-3] [0 9e-3]
0.68
0.7
0.72
0.74
0.76
0.78
0.8
Delay Vector (sec)
Bit Error Rate (BER)
Gain Vector=[0 0] dB
Gain Vector=[0 2] dB
Gain Vector=[0 -2] dB
Irish Interdisciplinary Journal of Science & Research (IIJSR)
Volume 7, Issue 2, Pages 34-41, April-June 2023
ISSN: 2582-3981 https://iijsr.com
40
░ 4. Conclusion and Future Recommendation
In conclusion, this study aimed to investigate the performance of Quadrature Amplitude Modulation (QAM) in
different wireless communication channels, including the additive white Gaussian noise (AWGN) channel and
fading channels (Rayleigh and Rician). The simulation model built for this research demonstrated that the QAM
scheme performs better in AWGN channels than in Rayleigh or Rician fading channels. The study highlights the
importance of addressing technical challenges, such as fading, shadowing, interference, and propagation path loss,
in wireless communication to meet the increasing demand for high-capacity and high-quality service. The use of
techniques such as Orthogonal Frequency Division Multiplexing (OFDM) can be a suitable option for
high-bandwidth data transmission in wireless communication systems. Future research could explore the impact of
other parameters on the performance of wireless communication systems in different fading channels, such as the
effects of channel coding, modulation schemes, and multiple antenna techniques. Additionally, researchers could
investigate the use of machine learning and artificial intelligence techniques to optimize the performance of
wireless communication systems in fading channels.
Declarations
Source of Funding
This research did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors.
Competing Interests Statement
The authors declare no competing financial, professional, or personal interests.
Consent for publication
The authors declare that they consented to the publication of this research work.
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