How to Simulate Cellular Network Projects Using MATLAB

To simulate the cellular network projects in MATLAB that has encompasses to design the network modules, signal propagation, user behavior, and channel conditions. MATLAB tool provides built-in functions and toolboxes like LTE Toolbox, 5G Toolbox, Communications Toolbox, which deliver the utilities for designing and replicating the cellular networks at numerous levels that containing physical, MAC, and higher layers. Here’s a common guidelines for simulating the Cellular network projects in MATLAB.

Steps to Simulate Cellular Network Projects Using MATLAB

1. Set Up MATLAB Environment

• Make sure we have installed the MATLAB along with the Communications Toolbox, LTE Toolbox, or 5G Toolbox, which relying on the kind of cellular network we require to replicate like 4G LTE or 5G NR.

2. Define System Parameters

• Cellular network simulations include configuring the metrics like frequency, bandwidth, number of base stations (BS), number of users (UE), cell layout, and transmission power.

% Example of cellular network parameters

numBaseStations = 3;           % Number of base stations

numUsers = 20;                 % Number of users

carrierFrequency = 2e9;        % Carrier frequency (2 GHz)

bandwidth = 10e6;              % Bandwidth (10 MHz)

txPowerBS = 43;                % Transmission power of base station in dBm

noiseFigure = 5;               % Noise figure (in dB)

3. Create Cellular Layout

• Describe the geographical layout of the cellular network, which encompassing the locations of users and base stations. We can replicate diverse topologies, like hexagonal or grid layouts.

Example of locating base stations and users randomly in a 2D area:

% Define the area of simulation (e.g., 1km x 1km)

areaSize = 1000;  % 1 km

% Generate random positions for base stations and users

bsPositions = areaSize * rand(numBaseStations, 2);  % Random BS positions

uePositions = areaSize * rand(numUsers, 2);         % Random UE positions

We can envision the layout using plot:

% Plot the base station and user positions

figure;

plot(bsPositions(:,1), bsPositions(:,2), ‘ro’, ‘MarkerSize’, 8, ‘DisplayName’, ‘Base Station’);

hold on;

plot(uePositions(:,1), uePositions(:,2), ‘b*’, ‘MarkerSize’, 5, ‘DisplayName’, ‘User Equipment’);

xlabel(‘X Position (meters)’);

ylabel(‘Y Position (meters)’);

title(‘Cellular Network Layout’);

legend show;

grid on;

4. Model Path Loss and Channel Conditions

• Path loss is important within cellular networks since it defines how the signal strength reduces across the distance. We can execute the path loss models such as the Hata, Okumura, or Cost231 models.

Example of a simple path loss model (free-space model):

% Path loss model (Free-space)

function pl = pathLossFreeSpace(d, fc)

c = 3e8;  % Speed of light

lambda = c / fc;  % Wavelength

pl = 20*log10(d) + 20*log10(fc) + 20*log10(4*pi/c);  % Path loss in dB

end

% Compute path loss between each base station and user

for ue = 1:numUsers

for bs = 1:numBaseStations

distance = sqrt(sum((bsPositions(bs, 🙂 – uePositions(ue, :)).^2));

pathLoss(ue, bs) = pathLossFreeSpace(distance, carrierFrequency);

end

end

5. Interference Modeling

• Interference plays a main role in the cellular network’s performance. In an interference-limited network, the signal-to-interference-plus-noise ratio (SINR) find out the quality of service.

Calculate the SINR for each user:

% Assume all base stations transmit at the same power (in dBm)

txPowerBS_dBm = txPowerBS;

txPowerBS_W = 10^((txPowerBS_dBm – 30) / 10);  % Convert dBm to Watts

noisePower_dBm = -174 + 10*log10(bandwidth);  % Noise power in dBm

noisePower_W = 10^((noisePower_dBm – 30) / 10);  % Convert dBm to Watts

for ue = 1:numUsers

receivedPower = txPowerBS_W ./ (10.^(pathLoss(ue, 🙂 / 10));  % Received power at each user

totalInterference = sum(receivedPower) – max(receivedPower);  % Interference from other BSs

sinr(ue) = max(receivedPower) / (totalInterference + noisePower_W);  % SINR calculation

end

6. Model Cellular Network Scheduling

• LTE and 5G are utilizing the resource block (RB) allocation to allocate the bandwidth to users actively. Execute a basic scheduling algorithm, like Round-Robin, Proportional Fair, or Max SINR.

Instance of a Round-Robin Scheduler:

numRBs = 100;  % Total resource blocks in the system

userRBs = zeros(numUsers, 1);  % RB allocation for each user

% Round-robin scheduling

for rb = 1:numRBs

user = mod(rb-1, numUsers) + 1;  % Select the user in round-robin fashion

userRBs(user) = userRBs(user) + 1;

end

7. Simulate Data Transmission and Reception

• Replicate how data is sent among the base stations and users according to the assigned resource blocks, SINR, and the modulation schemes.

Example of modulation scheme based on SINR:

% Simple adaptive modulation based on SINR

modulationOrder = zeros(numUsers, 1);  % Modulation order for each user

for ue = 1:numUsers

if sinr(ue) > 20

modulationOrder(ue) = 64;  % 64-QAM

elseif sinr(ue) > 10

modulationOrder(ue) = 16;  % 16-QAM

else

modulationOrder(ue) = 4;   % QPSK

end

end

8. Performance Analysis

• Examine the crucial performance parameters like throughput, latency, packet error rate (PER), and coverage, after the simulation.

Example of computing the throughput for each user:

% Calculate the throughput based on resource blocks and modulation order

throughput = zeros(numUsers, 1);

rbBandwidth = bandwidth / numRBs;  % Bandwidth per resource block

for ue = 1:numUsers

throughput(ue) = userRBs(ue) * rbBandwidth * log2(modulationOrder(ue));  % Throughput in bps

end

% Display total throughput

totalThroughput = sum(throughput);

fprintf(‘Total throughput: %.2f Mbps\n’, totalThroughput / 1e6);

9. Visualization

• We can envision the simulation outcomes that containing SINR maps, throughput, and user distributions, with the help of MATLAB’s plotting functions.

Example of plotting a SINR heatmap:

% Plot SINR map (assuming a grid of users in the network)

[X, Y] = meshgrid(linspace(0, areaSize, 100), linspace(0, areaSize, 100));

Z = griddata(uePositions(:,1), uePositions(:,2), sinr, X, Y, ‘v4’);

figure;

contourf(X, Y, Z);

colorbar;

title(‘SINR Heatmap’);

xlabel(‘X Position (meters)’);

ylabel(‘Y Position (meters)’);

10. Advanced Cellular Network Models

Based on the project, we can execute more innovative cellular network models:

• Handover management amongst the cells.
• Massive MIMO simulation for enhancing the capacity within 5G networks.
• Multi-cell interference coordination (ICIC) to mitigate the inter-cell interference.
• HetNets (Heterogeneous Networks), which encompass small cells like femtocells, picocells as well as macro cells.

Example Cellular Network Project Ideas:

1. LTE Network Simulation: Replicate an LTE network, model user mobility, and examine the impact of handover on the system throughput.
2. 5G New Radio (NR) Simulation: Design 5G NR, which containing the mmWave spectrum and massive MIMO methods, and investigate the performance within dense urban situations.
3. Interference Management in Cellular Networks: Replicate the methods such as fractional frequency reuse (FFR) or ICIC to handle the inter-cell interference.

In this simulation, we had thoroughly elucidating the basic simulation techniques that including set up the MATLAB, simulation, visualization and analyse the Cellular Network projects and also we offered advanced models for Cellular network and sample project ideas using MATLAB tool. Further details will be added later. Receive expert guidance on replicating cellular networks across various levels, including physical, MAC, and higher layers, for your projects. At phdprime.com, our team of specialists is dedicated to ensuring timely completion of your work while providing comprehensive explanations for your cellular network simulation projects using MATLAB.