Building a simple 5G simulation model is examined as an interesting as well as important process. Several procedures have to be followed for conducting this process in an efficient manner. To build a simple 5G simulation model using ns-3, we offer an in-depth instruction, including explicit sample script:


  • Initially, ensure that you have installed ns-3 including all the major modules, such as nr or mmWave modules for 5G.
  • It is important to have fundamental expertise in ns-3 scripting and C++ programming.

Simulation Model Elements

  1. Network Topology
  2. Traffic Model
  3. Mobility Model
  4. PHY and MAC Layer Arrangements
  5. EPC Incorporation
  6. Data Gathering and Analysis

Sample Script for 5G Simulation Model in ns-3

By including two User Equipments (UEs) and one gNodeB (gNB), the following instance builds a basic 5G network. For New Radio (NR) simulations, it is approachable to utilize the nr module.

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

#include “ns3/point-to-point-module.h”

#include “ns3/mobility-module.h”

#include “ns3/config-store-module.h”

#include “ns3/applications-module.h”

#include “ns3/nr-module.h”

#include “ns3/epc-helper.h”

#include “ns3/mmwave-helper.h”

using namespace ns3;

int main (int argc, char *argv[])


// Simulation parameters

double simTime = 10.0;

uint16_t numUeNodes = 2;

uint16_t numGnbNodes = 1;

// Set up logging (optional)

LogComponentEnable(“NrHelper”, LOG_LEVEL_INFO);

// Create nodes for UEs and gNBs

NodeContainer ueNodes;


NodeContainer gnbNodes;


// Set up mobility models

MobilityHelper mobility;



mobility.SetMobilityModel(“ns3::RandomWalk2dMobilityModel”, “Bounds”, RectangleValue(Rectangle(-100, 100, -100, 100)));


// Install internet stack

InternetStackHelper internet;



// Set up the NR and EPC helpers

Ptr<NrHelper> nrHelper = CreateObject<NrHelper>();

Ptr<PointToPointEpcHelper> epcHelper = CreateObject<PointToPointEpcHelper>();


// Install NR devices on gNB and UEs

NetDeviceContainer gnbDevices = nrHelper->InstallGnbDevice(gnbNodes);

NetDeviceContainer ueDevices = nrHelper->InstallUeDevice(ueNodes);

// Attach UEs to the gNB

nrHelper->Attach(ueDevices, gnbDevices.Get(0));

// Assign IP addresses to UEs

Ipv4InterfaceContainer ueIpIfaces;

ueIpIfaces = epcHelper->AssignUeIpv4Address(NetDeviceContainer(ueDevices));

// Set up application traffic

uint16_t dlPort = 1234;

uint16_t ulPort = 2000;

OnOffHelper dlClient(“ns3::UdpSocketFactory”, InetSocketAddress(ueIpIfaces.GetAddress(0), dlPort));

dlClient.SetAttribute(“DataRate”, DataRateValue(DataRate(“100Mb/s”)));

dlClient.SetAttribute(“PacketSize”, UintegerValue(1024));

ApplicationContainer clientApps = dlClient.Install(gnbNodes.Get(0));



PacketSinkHelper dlPacketSinkHelper(“ns3::UdpSocketFactory”, InetSocketAddress(Ipv4Address::GetAny(), dlPort));

ApplicationContainer serverApps = dlPacketSinkHelper.Install(ueNodes.Get(0));



OnOffHelper ulClient(“ns3::UdpSocketFactory”, InetSocketAddress(gnbNodes.GetAddress(0), ulPort));

ulClient.SetAttribute(“DataRate”, DataRateValue(DataRate(“50Mb/s”)));

ulClient.SetAttribute(“PacketSize”, UintegerValue(1024));

clientApps = ulClient.Install(ueNodes.Get(0));



PacketSinkHelper ulPacketSinkHelper(“ns3::UdpSocketFactory”, InetSocketAddress(Ipv4Address::GetAny(), ulPort));

serverApps = ulPacketSinkHelper.Install(gnbNodes.Get(0));



// Enable tracing


// Run the simulation




return 0;


Description of the Script

  1. Node Development:
  • For gNodeB (gNB) and User Equipment (UE), develop nodes.
  1. Mobility Patterns:
  • To specify the motion patterns of gNBs and UEs, configure mobility models for them.
  1. Installation of Internet Stack:
  • In order to facilitate IP-related interaction, consider the internet stack installation on the nodes.
  1. NR and EPC Helpers:
  • For handling the 5G network elements, arrange EPC (Evolved Packet Core) and NR (New Radio) helpers.
  1. Device Installation:
  • On gNBs and UEs, install NR devices.
  1. UE Connection:
  • Particularly for allowing UEs to interact via the gNB, connect UEs and gNB.
  1. IP Address Allocation:
  • For network interaction, the IP addresses have to be allocated to the UE devices.
  1. Application Arrangement:
  • To simulate data transmission among the gNB and UEs, set up application traffic (downlink and uplink).
  1. Tracing:
  • As a means to gather data for performance analysis, facilitate tracing functionality.
  1. Simulation Performance:
  • For a certain timeframe, execute the simulation.

Performance Metrics for Analysis

Consider the following major performance metrics once executing the simulation:

  • Throughput: The data rates that are accomplished among the gNB and the UEs are assessed in throughput metrics.
  • Latency: For packet transmission, the end-to-end delay has to be evaluated.
  • Packet Loss: Specifically for packet loss metrics, assess the total counts of packets that are lost at the time of transmission.
  • Signal Quality: The Signal-to-Noise Ratio (SNR) and other major signal standard-based metrics have to be measured.

What are best simulators for telecommunication?

In the domain of telecommunications, several simulators are employed based on various requirements and objectives. Specifically for this domain, we recommend numerous prominent simulators that are considered as appropriate and extensively utilized simulators:

  1. ns-3


  • For academic and research objectives, ns-3 is employed extensively. It is examined as a discrete-event network simulator. For simulating wireless and wired networks, it facilitates an efficient platform, and also offers in-depth models for network protocols.


  • Ns-3 has extensive documentation and effective community assistance.
  • For the simulation of complicated network settings such as IoT, LTE, and 5G, it is highly appropriate.
  • It has an extensive standard of scalability and flexibility.
  • Provides a strong library of network models and protocols.

Application Areas:

  • Performance assessment of previous network frameworks and protocols.
  • Simulation of extensive network placements.
  • Exploration and creation of novel networking protocols.

Relevant Website: ns-3.

  1. OMNeT++


  • OMNeT++ is majorly utilized for the creation of network simulators. It is referred to as a modular, adaptable, component-related C++ simulation framework and library.


  • For mobile and wireless network simulations, OMNeT++ offers effective assistance.
  • Enables combination with numerous architectures such as Veins, Simu5G, and INET.
  • It has more adaptability and scalability.
  • To arrange and visualize simulations, it has an extensive graphical user interface (GUI).

Application Areas:

  • Simulation of interaction networks, such as ad hoc and wireless networks.
  • Combination with physical layer simulators.
  • Simulations of Vehicle-to-everything (V2X) communication.

Relevant Website: OMNeT++

  1. MATLAB with Simulink


  • MATLAB is highly efficient for numerical computation, programming, and visualization. It is a robust interactive platform and high-level language. For model-related design and multi-domain simulation, Simulink is a block diagram platform.


  • For communication systems, it offers a wide range of toolboxes such as the 5G Toolbox.
  • Facilitates incorporation with actual-time simulation functionalities and hardware.
  • Specifically for algorithm creation and signal processing, it provides effective features.
  • To design and simulate complicated systems, it is very useful because of having high-level abstraction.

Application Areas:

  • Quick modeling and evaluation of novel interaction techniques.
  • Performance analysis based on network layer and physical layer protocols.
  • Modeling and simulation of interaction systems and methods.

Relevant Website: MATLAB

  1. QualNet


  • QualNet mainly facilitates the designing of extensive wired and wireless networks. It is considered as an adaptable network simulation environment.


  • For hybrid, wired, and wireless networks, QualNet offers a large collection of models.
  • It has actual-time simulation abilities.
  • It is more ideal for military as well as general applications.
  • Supports high-fidelity and adaptable simulations.

Application Areas:

  • Network evaluation and analysis in actual-time.
  • Simulation of extensive network platforms.
  • Performance assessment of network frameworks and protocols.

Relevant Website: QualNet

  1. GNS3


  • To simulate complicated networks, GNS-3 (Graphical Network Simulator-3) enables the integration of actual and virtual devices. It is publicly accessible network software that is capable of emulating intricate networks.


  • Offers a wider repository of network topologies and strong community assistance.
  • For network design and evaluation, it provides an excellent graphical interface.
  • Enables effective combination with different virtual machines and network devices.
  • With actual hardware, it supports real-time network emulation.

Application Areas:

  • Training and certification exam planning (for instance: Cisco certifications).
  • For assessment objectives, the emulation of complicated network platforms.
  • Network design and evaluation using practical software and hardware.

Relevant Website: GNS3

  1. OPNET (Riverbed Modeler)


  • For designing, examining, and enhancing network performance, OPNET Modeler is highly helpful. It is an extensive network simulation tool and a phase of Riverbed Modeler.


  • OPNET Modeler is more appropriate for business applications as well as education research.
  • To simulate network applications, wireless networks, and commercial networks, it offers robust assistance.
  • For performance analysis and enhancement, it provides innovative functionalities.
  • Supports in-depth modeling of network devices and protocols.

Application Areas:

  • Simulation of wider network platforms.
  • Performance analysis of network applications and protocols.
  • Design and enhancement of network for service provider and commercial networks,

Relevant Website: Riverbed Modeler

  1. NetSim


  • NetSim is efficient in offering extensive simulation and designing of interaction networks. It is referred to as a network emulation and simulation tool.


  • NetSim has in-depth analysis and visualization tools.
  • It is highly ideal for various objectives like research, business, and academics.
  • Provides a strong library of network frameworks and protocols.
  • Along with drag-and-drop network design, it has a more accessible interface.

Application Areas:

  • It is highly used in the process of teaching and studying network principles.
  • Performance assessment of network applications and protocols.
  • Exploration and creation of novel network mechanisms.

Relevant Website: NetSim

  1. Cooja (Contiki OS)


  • For the simulation of less-power wireless networks that are employed in IoT, Cooja is very useful and effective. It is specifically considered as a network simulator relevant to Contiki OS.


  • Supports in-depth energy utilization modeling.
  • It is appropriate for simulating IoT and sensor networks.
  • For hardware-in-the-loop simulations, it enables combination with actual hardware.
  • It is majorly designed for low-power and lossy networks (LLNs).

Application Areas:

  • Performance assessment of low-power wireless networks.
  • Simulation of extensive sensor networks.
  • Creation and evaluation of IoT applications and protocols.

Relevant Website: Contiki OS

5g Technology Project Proposal Topics

5g Technology Project

Discover the latest 5G Technology Projects making waves in today’s world. Delve into a detailed analysis to uncover research gaps and formulate thought-provoking research questions. We shed light on the chosen research methodology with a clear explanation of the underlying 5G concepts. Each of your chapter will be meticulously crafted with precision, culminating in a thorough discussion of the final results. For further assistance on any areas of 5G, feel free to reach out to us.

  1. Future generation 5G wireless networks for smart grid: A comprehensive review
  2. Internet of vehicle’s resource management in 5G networks using AI technologies: Current status and trends
  3. Blockchain-enabled authentication handover with efficient privacy protection in SDN-based 5G networks
  4. 5G Network Slice Type Classification using Traditional and Incremental Learning
  5. Reliability-aware Dynamic Service Chain Scheduling in 5G Networks based on Reinforcement Learning
  6. Evaluation of adaptive active set management for multi-connectivity in intra-frequency 5G networks
  7. Supporting massive M2M traffic in the Internet of Things using millimetre wave 5G network
  8. Efficient traffic offloading for seamless connectivity in 5G networks onboard high speed trains
  9. User Group Behavioural Pattern in a Cellular Mobile Network for 5G Use-cases
  10. A Vehicle-Centric Probabilistic Approach to Virtual Cell Management in Ultra-Dense 5G Networks
  11. Private 5G Networks for Vertical Industries: Deployment and Operation Models
  12. Wireless network virtualization with SDN and C-RAN for 5G networks: Requirements, opportunities, and challenges
  13. Analysis of the Advantages of Millimeter Waves for Video Traffic Transmission in 5G Networks
  14. Multimedia multicast services in 5G networks: Subgrouping and non-orthogonal multiple access techniques
  15. A software-defined device-to-device communication architecture for public safety applications in 5G networks
  16. The Research of Resource Allocation Method Based on GCN-LSTM in 5G Network
  17. Context-based Latency Guarantees Considering Channel Degradation in 5G Network Slicing
  18. A Two-Arm Archimedean Circularly Polarized Spiral Slot Antenna for IoT Devices in 5G Network
  19. AI based Collaborative Optimization Scheme for Multi-Frequency Heterogeneous 4G/5G Networks
  20. Analysis of Vehicle to Vehicle Communication Parameter on 5G Network
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