Bangabandhu Sheikh Mujibur Rahman Aviation and Aerospace University (BSMRAAU)
Project Report
Name of Project: Signal Jamming Using MATLAB.
Date of Submission: 12-May-2024
Submitted by:
Nafiz-Uz-Zaman
Tanvir Anowar
( ID - 21024021 )
(ID -21024024)
Md. Ahsan Ullah Tuhin (ID- 21024028)
Department: Aeronautical Engineering (Avionics)
Course Name: Signals and Systems
Course Code: AVE4501
Submitted to: Asst. Prof. Md Sakir Hossain
Signal Jamming Using MATLAB
Nafiz-uz-Zaman
Id-21024021
Department of Aeronautical
Engineering (Avionics)
Tanvir Anowar
Id-21024024
Department of Aeronautical
Engineering (Avionics)
Abstract— This paper presents the design and methodology
of jamming signal using MATLAB code. This code is a simulation
of how a transmitted signal gets jammed and how it looks after
jamming. It helps in understanding the effects of jamming on
wireless communication. The visualization part of the code helps
in understanding these effects more intuitively. This project
involves a MATLAB-based simulation of Radio Frequency (RF)
jamming, which is a form of denial-of-service attack targeting
wireless networks. The simulation produces both a transmitted
and a received signal, each dependent on time, frequency, and
amplitude. The target signal is determined by the difference
between the transmitted and received signals. Subsequently, a
jamming signal with designated amplitude and frequency is
introduced.
Keywords— Radio Frequency (RF), Transmitted signal,
received signal, jamming signal, Denial of Service (DoS).
I. INTRODUCTION
In the age of pervasive wireless communication,
ensuring the security and reliability of these networks is of
utmost importance. Radio Frequency (RF) jamming, a denialof-service attack that can impair network operations, stands
out as a significant threat to wireless communication. This
project is designed to simulate and illustrate the impact of RF
jamming on wireless signals.[1]
Utilizing MATLAB, a sophisticated language and
interactive platform for numerical analysis, visualization, and
programming, the project initiates by generating a transmitted
signal with defined frequency and amplitude parameters.
Subsequently, a received signal is formulated, derived from
the transmitted signal. The target signal, which depicts optimal
communication without interference, is determined by the
difference between the transmitted and received signals.
To emulate the jamming effect, a jamming signal with
designated amplitude and frequency is introduced. The
resulting jammed signal, indicative of communication postinterference, is the aggregate of the target signal and the
jamming signal.
Md. Ahsan Ullah Tuhin
Id-21024028
Department of Aeronautical
Engineering (Avionics)
The project offers graphical depictions of the transmitted,
received, target, jamming, and post-jamming signals,
facilitating a more intuitive comprehension of RF jamming's
impact on wireless communication.
This document will explore the simulation's intricacies,
including signal generation, jamming effect incorporation,
and the resultant visualizations. The insights gleaned from this
project may advance the understanding of RF jamming and
aid in forging more resilient wireless communication
frameworks.
II. OBJECTIVE
The primary goal of this project is to simulate and analyze
the impact of Radio Frequency (RF) jamming on wireless
networks using MATLAB. It will involve generating a
transmitted signal, receiving a signal, computing the target
signal, introducing a jamming signal, and visualizing the
jamming effects on the target signal. Understanding the effects
of RF jamming is crucial to improving the resilience of
wireless communication systems against such disturbances.
Moreover, this project seeks to provide a clear understanding
of RF jamming through visual aids, making it a valuable
educational tool for both students and professionals in the field
of wireless communications.
III. THEORY:
DoS: A Denial of Service (DoS) attack on wireless
networks is a type of cyber-attack that aims to disrupt the
normal functioning of the network. The attacker floods the
network with unnecessary requests, causing an overload of
the network’s resources. This prevents legitimate users from
accessing the network service.[2]
Here we used jamming attacks, which is also a type of
DoS. In jamming attacks, the attacker uses a device to emit
radio signals that interfere with the wireless network’s
frequency, causing disruption in the network’s
communication. In this project we used MATLAB instead of
any device to simulate or visualize the jamming process
using MATLAB code. [3]
The provided code is a MATLAB simulation that models
the Radio Frequency (RF) jamming process, a denial-ofservice attack on wireless networks. The theory behind this
code is divided into several key components:
Signal Generation: The simulation starts by generating a
transmitted signal based on time, frequency, and amplitude
parameters. A received signal is then produced, derived from
the transmitted signal. The target signal, which represents
ideal communication without interference, is determined by
subtracting the transmitted signal from the received signal.
Jamming Signal: A jamming signal with specified
amplitude and frequency is introduced. This signal aims to
disrupt the target signal, mimicking the impact of an actual
jamming attack.
Jammed Signal: The jammed signal is obtained by
combining the jamming signal with the target signal,
depicting the communication signal post-interference.
Visualization: The simulation plots the transmitted,
received, target, jamming, and jammed signals, offering a
clear visual representation of RF jamming's impact on
wireless communication.
In summary, the code's theory involves signal generation,
jamming signal introduction, and signal visualization preand post-jamming. This simulation offers insights into RF
jamming's effects on wireless networks and serves as a tool
for analyzing and countering such attacks.
This theoretical framework underpins our project, with
subsequent sections addressing the practical implementation
and outcomes of this simulation. A detailed examination of
RF jamming simulation in wireless networks will follow.
IV. METHODOLOGY
A. Algorithm of the MATLAB code:
I. Initialize Parameters: Clear all variables, close all
figures, and clear the command window. Set the time range,
amplitude, carrier frequency, modulation frequency, and
modulation index.
II. Generate Transmitted Signal: Generate a transmitted
signal which is a function of time, frequency, and amplitude
parameters.
III. Generate Received Signal: Generate a received signal
which is a function of the transmitted signal.
IV. Calculate Target Signal: Calculate the target signal by
subtracting the transmitted signal from the received signal.
V. Generate Jamming Signal: Generate a jamming signal
with a specific amplitude and frequency.
VI. Calculate Jammed Signal: Calculate the jammed signal
by adding the jamming signal to the target signal.
VII. Visualize Signals: Plot the transmitted signal, received
signal, target signal, jamming signal, and the jammed signal in
different subplots for visualization.
This algorithm provides a step-by-step procedure of how the
code simulates the process of Radio Frequency (RF) jamming,
a type of denial-of-service attack on wireless networks.
B. CODE:
clc;close all;clear all;
t=-.10:1e-7:.20;
A=1;
fc=200;
fm = 15;
a=.5;
m_values = 1.7;
transmittedSignal = A * (1 + m_values *
cos(2*pi*fm*t)) .* cos(2*pi*fc*t);
received_signal= a * m_values *
sin(2*pi*fm*t) .* (1+cos(2*pi*fc*t));
a1=4;
targetsignal=received_signaltransmittedSignal;
jamming_amplitude=2;
jamming_frequency = 10;
jamming=jamming_amplitude *
exp(2*pi*jamming_frequency*t);
received_signal1=exp(2*pi*100*t).*sin(2*p
i*100*t);
targetsignal1=received_signal1transmittedSignal;
jammed=targetsignal+jamming;
jammed1=targetsignal1+jamming;
figure(1);
subplot(4,2,1);
plot(t*1e3,transmittedSignal);
title('transmitted');
ylabel('Amplitude');
xlabel('Time (ns)');
grid on;
subplot(4,2,2);
plot(t*1e3,received_signal);
title('recieved');
ylabel('Amplitude');
xlabel('Time (ns)');
grid on;
subplot(4,2,5);
plot(t*1e3,targetsignal);
title('target');
ylabel('Amplitude');
xlabel('Time (ns)');
grid on;
subplot(4,2,[3,4]);
plot(t*1e3,jamming);
title('jamming signal');
ylabel('Amplitude');
xlabel('Time (ns)');
grid on;
subplot(4,2,6);
plot(t*1e3,jammed);
title('After jamming');
ylabel('Amplitude');
xlabel('Time (ns)');
grid on;
subplot(4,2,7);
plot(t*1e3,targetsignal1);
title('targetsignal1');
ylabel('Amplitude');
xlabel('Time (ns)');
grid on;
subplot(4,2,8);
plot(t*1e3,jammed1);
title('jammed1');
ylabel('Amplitude');
xlabel('Time (ns)');
grid on;
ii.
Received Signal:
Fig 02: Received Signal
iii.
Jamming Signal:
V. RESULTS:
i. Transmitted Signal:
Fig 03: Provided Jamming Signal
iv.
Target Signal: Actually, Target Signal found by
subtracting the transmitted signal from the received
signal.
Fig 01: Transmitted Signal
Fig 04: Target Signal
v.
After Jamming: After jamming Process, the target
signal become like this shape:
interference, is derived by deducting the transmitted signal
from the received signal.
Fig 05: After Jamming
vi.
Second Target Signal: This is an optional signal that
we used to find out to compare jamming that a high
frequency signal can’t jam by any low frequency
signal:
Fig 06: Target Signal 02
vii.
Second jammed Signal:
Actually, the jamming signal is introduced, possessing
a predetermined amplitude and frequency designed to
disrupt the target signal, thereby emulating the impact of
an actual jamming attack. The jammed signal is computed
by amalgamating the jamming signal with the target
signal.
A pivotal feature of this simulation is the signal
visualization. The code graphically represents the
transmitted, received, target, jamming, and jammed
signals in separate subplots, offering a transparent and
comprehensible depiction of RF jamming's influence on
wireless communication.
This simulation yields critical insights into the
ramifications of RF jamming on wireless networks,
illustrating how such a signal can impede a network's
regular operations and hinder legitimate users from
utilizing network services. This is especially beneficial for
network administrators and security experts seeking to
comprehend and counteract the impacts of RF jamming on
their networks.
It is crucial to acknowledge, however, that this
simulation represents a simplified version of RF jamming.
In actual scenarios, RF jamming can be considerably more
intricate, with adversaries employing advanced tactics to
avoid detection and interrupt communications. As such,
while this simulation provides a foundational
understanding, it does not encapsulate the full complexity
of real-world RF jamming.
VII. PRECAUTION
Fig 07: Jammed Signal 02
VI. DISCUSSION
The MATLAB code provided facilitates a detailed
simulation of Radio Frequency (RF) jamming, which is a
form of denial-of-service attack targeting wireless
networks. The code adeptly simulates the RF jamming
process, encompassing the generation of transmitted and
received signals, the introduction of the jamming signal,
and the computation of the resultant jammed signal.
The simulation begins with the generation of the
transmitted signal, characterized by a defined frequency,
amplitude, and modulation index. The received signal is
then formulated based on the transmitted signal. The target
signal, indicative of optimal communication free from
i.
Parameter
Tuning:
Parameters
like
amplitude, frequency, and modulation index are
preset in this code. Adjust these values to meet the
specific needs of your simulation.
ii.
Understanding RF Jamming: This code
models RF jamming, a denial-of-service attack on
wireless networks. Comprehending RF jamming's
impact on wireless communication is crucial for
accurate simulation results interpretation.
iii.
Signal Processing Knowledge: The code
employs signal processing concepts such as
modulation and frequency. Grasping these concepts
is vital for code comprehension and modification.
iv.
MATLAB Proficiency: Written in MATLAB,
this code requires familiarity with its syntax and
functions for effective usage.
v.
Computational Resources: The code
performs intensive computations, particularly with
large datasets. Ensure your system has adequate
computational power.
II.
Enhancing Security in Prisons: Jammers are used in
correctional facilities to prevent inmates from using
cell phones for illegal activities.
III.
Protecting Corporate Data: In corporate
environments, jammers can protect sensitive data
and meetings from unauthorized surveillance.
Preventing Disruptions: In theaters, lecture halls,
and during events or performances, jammers can be
used to prevent disruptions caused by mobile
phones.
vi.
Code Modification: Before modifying the
code or adding features, fully understand the current
code and rigorously test any changes.
IV.
vii.
Visualization: The code generates signal
visualization plots. Understand the significance of
each plot, and ensure any additional plots enhance
simulation result comprehension.
V.
viii.
Error Handling: Currently lacking error
handling, it's advisable to implement it for graceful
failure in production environments.
Blocking
Unauthorized
Communications:
Authorities often use jammers to prevent
unauthorized communications in sensitive areas.
VI.
GPS Jamming: GPS signal jamming devices can be
used for personal privacy, maintaining security,
managing drones, or supporting law enforcement
operations.
VIII. CONCLUSION
This project successfully simulated the process of Radio
Frequency (RF) jamming, a type of denial-of-service attack on
wireless networks, using MATLAB. The visualizations
provided a clear and intuitive understanding of the effects of
RF jamming on wireless communication. The project not only
contributed to the understanding of RF jamming but also
provided a valuable educational tool for students and
researchers in the field of wireless communication. Future
work could involve refining the simulation to model more
complex jamming techniques and exploring potential
countermeasures to these attacks. Overall, the project was a
significant step towards understanding and mitigating the
effects of RF jamming on wireless networks.
Besides there are so many functionalities that is useful in our
daily life. And these are:
I.
Maintaining Exam Integrity: In educational
settings, signal jammers can be used to prevent
cheating by blocking internet access during exams.
REFERENCES
L. Kong, Z. Xu, J. Wang and K. Pan, "A novel algorithm
for
jamming
recognition
in
wireless
communication", 2013 6th International Congress on
Image and Signal Processing (CISP), vol. 3, pp. 14731477, 2013.
[2] https://www.cloudflare.com/learning/ddos/glossary/denial-ofservice/
[3] https://link.springer.com/article/10.1007/s11277-020-07776-3
[4] G. Shao, Y. Chen and Y. Wei, "Convolutional neural
network-based radar jamming signal classification with
sufficient and limited samples", IEEE Access, vol. 8, pp.
80588-80598, 2020.
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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnum
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[6] https://koreascience.kr/article/JAKO201403760397668
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