MATLAB Experts Online are given by us with detailed implementation and explanation. If you are struggling in any area of MATLAB then be in touch with us to get good solution. By online itself you can get best results for all types of MATLAB issues. In recent years, several project ideas have evolved based on the simulation of various protocols. By considering different protocol simulations, we suggest a few significant instances that might be more intriguing to you:

Sample 1: Simulating a Basic MAC Protocol

Goal

For a wireless network, a basic Medium Access Control (MAC) protocol has to be simulated, in which nodes compete to gain access to a distributed channel.

MATLAB Code

% Simulation parameters

numNodes = 10;

numSlots = 1000;

probabilityTransmit = 0.1;

% Initialize variables

channelState = zeros(1, numSlots);

transmissions = zeros(numNodes, numSlots);

% Simulate the MAC protocol

for slot = 1:numSlots

for node = 1:numNodes

if rand < probabilityTransmit

transmissions(node, slot) = 1;

end

end

% Check for collisions

if sum(transmissions(:, slot)) > 1

channelState(slot) = -1; % Collision

elseif sum(transmissions(:, slot)) == 1

channelState(slot) = find(transmissions(:, slot)); % Successful transmission

else

channelState(slot) = 0; % Idle

end

end

% Analyze results

numCollisions = sum(channelState == -1);

numSuccesses = sum(channelState > 0);

numIdle = sum(channelState == 0);

% Display results

disp([‘Number of Collisions: ‘, num2str(numCollisions)]);

disp([‘Number of Successful Transmissions: ‘, num2str(numSuccesses)]);

disp([‘Number of Idle Slots: ‘, num2str(numIdle)]);

Sample 2: Simulating the ALOHA Protocol

Goal

The ALOHA protocol must be simulated, in which collisions can arise through nodes that transmit packets in a random way.

MATLAB Code

% Simulation parameters

numNodes = 10;

numSlots = 1000;

probabilityTransmit = 0.1;

% Initialize variables

channelState = zeros(1, numSlots);

transmissions = zeros(numNodes, numSlots);

% Simulate the ALOHA protocol

for slot = 1:numSlots

for node = 1:numNodes

if rand < probabilityTransmit

transmissions(node, slot) = 1;

end

end

% Check for collisions

if sum(transmissions(:, slot)) > 1

channelState(slot) = -1; % Collision

elseif sum(transmissions(:, slot)) == 1

channelState(slot) = find(transmissions(:, slot)); % Successful transmission

else

channelState(slot) = 0; % Idle

end

end

% Analyze results

numCollisions = sum(channelState == -1);

numSuccesses = sum(channelState > 0);

numIdle = sum(channelState == 0);

% Display results

disp([‘Number of Collisions: ‘, num2str(numCollisions)]);

disp([‘Number of Successful Transmissions: ‘, num2str(numSuccesses)]);

disp([‘Number of Idle Slots: ‘, num2str(numIdle)]);

Sample 3: Simulating the CSMA/CD Protocol

Goal

In this project, we focus on simulating the CSMA/CD protocol (Carrier Sense Multiple Access with Collision Detection).

MATLAB Code

% Simulation parameters

numNodes = 10;

numSlots = 1000;

probabilityTransmit = 0.1;

% Initialize variables

channelState = zeros(1, numSlots);

transmissions = zeros(numNodes, numSlots);

backoff = zeros(numNodes, 1);

% Simulate the CSMA/CD protocol

for slot = 1:numSlots

for node = 1:numNodes

if backoff(node) > 0

backoff(node) = backoff(node) – 1;

elseif rand < probabilityTransmit

transmissions(node, slot) = 1;

end

end

% Check for collisions

if sum(transmissions(:, slot)) > 1

channelState(slot) = -1; % Collision

% Random backoff for nodes involved in collision

collidingNodes = find(transmissions(:, slot));

backoff(collidingNodes) = randi([1, 10], length(collidingNodes), 1);

elseif sum(transmissions(:, slot)) == 1

channelState(slot) = find(transmissions(:, slot)); % Successful transmission

else

channelState(slot) = 0; % Idle

end

end

% Analyze results

numCollisions = sum(channelState == -1);

numSuccesses = sum(channelState > 0);

numIdle = sum(channelState == 0);

% Display results

disp([‘Number of Collisions: ‘, num2str(numCollisions)]);

disp([‘Number of Successful Transmissions: ‘, num2str(numSuccesses)]);

disp([‘Number of Idle Slots: ‘, num2str(numIdle)]);

Sample 4: Simulating the LoRaWAN Protocol

Goal

By concentrating on data sharing from end devices to a gateway, the LoRaWAN protocol should be simulated.

MATLAB Code

% Simulation parameters

numDevices = 50;

numSlots = 1000;

probabilityTransmit = 0.01;

spreadingFactor = 7;

% Initialize variables

channelState = zeros(1, numSlots);

transmissions = zeros(numDevices, numSlots);

receivedPackets = 0;

% Simulate LoRaWAN protocol

for slot = 1:numSlots

for device = 1:numDevices

if rand < probabilityTransmit

transmissions(device, slot) = 1;

end

end

% Check for collisions

if sum(transmissions(:, slot)) > 1

channelState(slot) = -1; % Collision

elseif sum(transmissions(:, slot)) == 1

channelState(slot) = find(transmissions(:, slot)); % Successful transmission

receivedPackets = receivedPackets + 1;

else

channelState(slot) = 0; % Idle

end

end

% Analyze results

numCollisions = sum(channelState == -1);

numIdle = sum(channelState == 0);

% Display results

disp([‘Number of Collisions: ‘, num2str(numCollisions)]);

disp([‘Number of Successful Packets: ‘, num2str(receivedPackets)]);

disp([‘Number of Idle Slots: ‘, num2str(numIdle)]);

Sample 5: Simulating TCP/IP Protocol

Goal

In order to interpret the congestion control and flow control techniques, we simulate a basic TCP/IP protocol.

MATLAB Code

% Simulation parameters

numPackets = 100;

packetLossProbability = 0.1;

RTT = 100; % Round-Trip Time in ms

cwnd = 1; % Initial congestion window size

ssthresh = 64; % Slow start threshold

% Initialize variables

packetsSent = 0;

packetsAcknowledged = 0;

packetsLost = 0;

time = 0;

% Simulate TCP/IP protocol

while packetsAcknowledged < numPackets

for i = 1:cwnd

if packetsSent < numPackets

packetsSent = packetsSent + 1;

if rand < packetLossProbability

packetsLost = packetsLost + 1;

else

packetsAcknowledged = packetsAcknowledged + 1;

end

end

end

% Update congestion window size

if packetsLost > 0

ssthresh = max(cwnd / 2, 1);

cwnd = 1;

packetsLost = 0;

else

if cwnd < ssthresh

cwnd = cwnd * 2; % Exponential growth (slow start)

else

cwnd = cwnd + 1; % Linear growth (congestion avoidance)

end

end

% Increment time by RTT

time = time + RTT;

end

% Display results

disp([‘Total Packets Sent: ‘, num2str(packetsSent)]);

disp([‘Total Packets Acknowledged: ‘, num2str(packetsAcknowledged)]);

disp([‘Total Simulation Time: ‘, num2str(time), ‘ ms’]);

Matlab experts for all research departments

MATLAB is examined as a robust platform that is generally employed in an extensive manner across numerous fields. For utilization of MATLAB in various domains like Biomedical Engineering, Civil Engineering, Mechanical Engineering, Electrical Engineering, and Computer Science, we list out some interesting topics and suitable instances:

Computer Science and Engineering

Topic: Image Processing and Computer Vision

Instance: Edge Detection with Canny Approach

% Load an image

img = imread(‘peppers.png’);

grayImg = rgb2gray(img);

% Perform edge detection using the Canny method

edges = edge(grayImg, ‘Canny’);

% Display the original image and the edges

figure;

subplot(1, 2, 1);

imshow(grayImg);

title(‘Original Image’);

subplot(1, 2, 2);

imshow(edges);

title(‘Edges Detected using Canny’);

Topic: Machine Learning

Instance: Training a Decision Tree Classifier

% Load sample data

load fisheriris;

data = meas;

labels = species;

% Train a decision tree classifier

tree = fitctree(data, labels);

% View the tree

view(tree, ‘Mode’, ‘graph’);

Electrical and Electronics Engineering

Topic: Signal Processing

Instance: Modeling a Digital Filter

% Design parameters

Fs = 1000; % Sampling frequency

Fp = 200; % Passband frequency

Fst = 250; % Stopband frequency

Ap = 1; % Passband ripple

Ast = 60; % Stopband attenuation

% Design the filter using the Kaiser window method

d = designfilt(‘lowpassfir’, ‘PassbandFrequency’, Fp, ‘StopbandFrequency’, Fst, …

‘PassbandRipple’, Ap, ‘StopbandAttenuation’, Ast, ‘SampleRate’, Fs);

% View the filter response

fvtool(d);

% Apply the filter to a signal

t = 0:1/Fs:1-1/Fs;

x = sin(2*pi*100*t) + 0.5*sin(2*pi*300*t); % Example signal

y = filter(d, x);

% Plot the original and filtered signals

figure;

subplot(2, 1, 1);

plot(t, x);

title(‘Original Signal’);

subplot(2, 1, 2);

plot(t, y);

title(‘Filtered Signal’);

Topic: Control Systems

Instance: PID Controller Design

% Define system parameters

num = [1];

den = [1, 10, 20];

sys = tf(num, den);

% Design a PID controller

Kp = 1;

Ki = 1;

Kd = 1;

pid_controller = pid(Kp, Ki, Kd);

% Create closed-loop system

cl_sys = feedback(pid_controller * sys, 1);

% Simulate step response

figure;

step(cl_sys);

title(‘Step Response with PID Controller’);

Mechanical Engineering

Topic: Finite Element Analysis (FEA)

Instance: Structural Analysis of a Cantilever Beam

% Define beam parameters

L = 2; % Length of the beam

E = 210e9; % Young’s modulus (Pa)

I = 1e-4; % Moment of inertia (m^4)

q = 1000; % Uniform load (N/m)

% Number of elements and nodes

n_elements = 10;

n_nodes = n_elements + 1;

% Element length

le = L / n_elements;

% Stiffness matrix assembly

K = zeros(n_nodes);

F = zeros(n_nodes, 1);

for i = 1:n_elements

ke = E*I/le^3 * [12 6*le -12 6*le; 6*le 4*le^2 -6*le 2*le^2; -12 -6*le 12 -6*le; 6*le 2*le^2 -6*le 4*le^2];

K(i:i+3, i:i+3) = K(i:i+3, i:i+3) + ke;

fe = q*le/2 * [1; le/6; 1; -le/6];

F(i:i+1) = F(i:i+1) + fe(1:2);

end

% Apply boundary conditions

K_reduced = K(2:end, 2:end);

F_reduced = F(2:end);

% Solve for displacements

U = K_reduced \ F_reduced;

U = [0; U]; % Add zero displacement at the fixed end

% Plot deflection

x = linspace(0, L, n_nodes);

plot(x, U);

title(‘Beam Deflection’);

xlabel(‘Length (m)’);

ylabel(‘Deflection (m)’);

Civil Engineering

Topic: Structural Dynamics

Instance: Vibration Analysis of a Multi-Story Building

% Define system parameters

m = 1; % Mass (kg)

k = 100; % Stiffness (N/m)

damping_ratio = 0.05;

% Number of stories

n_stories = 3;

% Mass and stiffness matrices

M = m * eye(n_stories);

K = k * (diag(2*ones(n_stories, 1)) – diag(ones(n_stories-1, 1), 1) – diag(ones(n_stories-1, 1), -1));

% Damping matrix

C = damping_ratio * (M + K);

% Solve the eigenvalue problem

[V, D] = eig(K, M);

% Natural frequencies (rad/s)

wn = sqrt(diag(D));

% Plot mode shapes

figure;

for i = 1:n_stories

subplot(n_stories, 1, i);

plot(1:n_stories, V(:,i));

title([‘Mode Shape ‘, num2str(i)]);

xlabel(‘Story’);

ylabel(‘Amplitude’);

end

Biomedical Engineering

Topic: Biomedical Signal Processing

Instance: ECG Signal Denoising

% Load ECG signal

load(‘ecgSignal.mat’); % Assume ecgSignal contains ECG data

fs = 500; % Sampling frequency

% Design a low-pass filter

fcut = 50; % Cutoff frequency (Hz)

[b, a] = butter(4, fcut/(fs/2), ‘low’);

% Filter the ECG signal

ecgFiltered = filtfilt(b, a, ecgSignal);

% Plot original and filtered ECG signals

t = (0:length(ecgSignal)-1) / fs;

figure;

subplot(2, 1, 1);

plot(t, ecgSignal);

title(‘Original ECG Signal’);

xlabel(‘Time (s)’);

ylabel(‘Amplitude’);

subplot(2, 1, 2);

plot(t, ecgFiltered);

title(‘Filtered ECG Signal’);

xlabel(‘Time (s)’);

ylabel(‘Amplitude’);

On the basis of the protocol simulations, we recommended several major instances, along with explicit goals and MATLAB codes. As a means to utilize MATLAB in various domains, numerous fascinating topics and appropriate instances are proposed by us in a clear way. We have the best team to guide in   MATLAB by our Experts in Online who gives immediate response within 30minutes.Drop us with all your details our help desk with give you proper results.