5G NR SIMULATOR

The 5G NR (New Radio) is a crucial specification of fifth generation networks which efficiently supports diverse spectrum bands like mmWave etc. Feel free to drop us an email or contact us for any updates. Rest assured, our knowledgeable experts will ensure that your 5G thesis is original and free from plagiarism Based on 5G NR simulator, we propose several project concepts that are trending as well as research-worthy in recent years:

1. 5G NR Performance Evaluation under Different Channel Conditions

• Explanation: Based on diverse channel conditions like rural, urban or suburban platforms, this research simulates and assesses the functions of 5G NR.
• Fundamental Tools: OpenAirInterface, NS-3 with NR module and MATLAB.
• Key Metrics: BER (Bit Error Rate), packet loss, throughput and latency.

2. Massive MIMO in 5G NR

• Explanation: On network potential and user throughput, evaluate their implications by executing and simulating massive MIMO methods.
• Fundamental Tools: NS-3, OpenAirInterface and MATLAB.
• Key Metrics: Beamforming efficiency, user throughput and spectral efficiency.

3. Dynamic Spectrum Sharing in 5G NR

• Explanation: To enhance the spectrum allocation, simulate the effective spectrum sharing among 5G NR and other wireless technologies such as LTE.
• Fundamental Tools: MATLAB and NS-3.
• Key Metrics: Interference levels, throughput and spectrum efficiency.

4. 5G NR Network Slicing

• Explanation: For various applications like mMTC, eMBB and URLLC, develop virtual networks by executing network slicing in a 5G NR simulator and examine their functions.
• Fundamental Tools: MATLAB, OpenAirInterface and NS-3.
• Key Metrics: Latency, throughput, slice isolation and resource utilization.

5. 5G NR for Vehicular Communications (V2X)

• Explanation: Conduct a research on function of V2X applications like traffic management and automated driving by simulating 5G NR-based vehicular communications.
• Fundamental Tools: SUMO, MATLAB and NS-3.
• Key Metrics: Communication range, integrity and latency.

6. Energy Efficiency in 5G NR

• Explanation: Across various configurations, evaluate the energy usage of 5G NR networks and suggest some effective techniques.
• Fundamental Tools: MATLAB and NS-3.
• Key Metrics: Throughput per watt, battery durability of devices and energy usage.

7. 5G NR Beamforming and Beam Management

• Explanation: In 5G NR, improve the coverage and potential by executing and simulating beam management and beamforming methods.
• Fundamental Tools: NS-3 and MATLAB.
• Key Metrics: Interference levels, beamforming gain and coverage area.

8. 5G NR Handover Mechanisms

• Explanation: As a means to reduce latency in the course of mobility and assure effortless connectivity by simulating diverse handover technologies in 5G NR.
• Fundamental Tools: MATLAB and NS-3.
• Key Metrics: Packet loss in the course time of handover, handover success rate and latency.

9. 5G NR for IoT Applications

• Explanation: It mainly emphasizes the URLLC (Ultra-Reliable Low-Latency Communications) and mMTC (massive Machine-Type Communications) and for IoT applications, assess the performance of 5G NR.
• Fundamental Tools: NS-3 and MATLAB.
• Key Metrics: response time, integrity and device density support.

10. 5G NR Interference Management

• Explanation: Improve the network performance in extensive 5G NR employments through creating and simulating interference management algorithms.
• Fundamental Tools: MATLAB and NS-3.
• Key Metrics: SNR (Signal-to-Noise Ratio), throughput and interference levels.

11. 5G NR Multi-Connectivity

• Explanation: In order to enhance integrity and throughput, this study involves simulating the multi-connectivity conditions where the devices are connected to various RATs (Radio Access Technologies) or several 5G NR base stations.
• Fundamental Tools: MATLAB and NS-3.
• Key Metrics: Integrity, latency and throughput.

12. 5G NR Ultra-Reliable Low-Latency Communications (URLLC)

• Explanation: Considering the 5G NR applicable areas like industrial automation and remote surgery establish and assess the URLLC (Ultra-Reliable Low-Latency Communications).
• Fundamental Tools: NS-3 and MATLAB.
• Key Metrics: Packet delivery ratio, integrity and latency.

13. NR Integrated Access and Backhaul (IAB)

• Explanation: Regarding the dense urban platforms in 5G NR, examine the functions of workability with the application of 5G NR for both access and backhaul by simulating IAB (Integrated Access and Backhaul).
• Fundamental Tools: MATLAB and NS-3.
• Key Metrics: Access latency, network capacity and backhaul throughput.

14. 5G NR Security Mechanisms

• Explanation: In opposition to diverse assaults, secure and verify data reliability and privacy through evaluating and executing security techniques in 5G NR.
• Fundamental Tools: NS-3 and MATLAB.
• Key Metrics: Data reliability, latency and attack detection rate.

15. 5G NR Resource Allocation Algorithms

• Explanation: To enhance the application of network resources, this research area creates and simulates enhanced resource utilization algorithms for 5G NR.
• Fundamental Tools: MATLAB and NS-3.
• Key Metrics: Throughput, fairness index and resource deployment capability.

Instance of a Simulation Configuration in NS-3

In NS-3, build a Simple 5G NR Scenario

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

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

#include “ns3/applications-module.h”

#include “ns3/nr-module.h”

Using namespace ns3;

NS_LOG_COMPONENT_DEFINE (“5gNrSimulation”);

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

{

  CommandLine cmd;

  cmd.Parse (argc, argv);

// create nodes for UE and gNB

  NodeContainer ueNodes;

  ueNodes.Create (1);

  NodeContainer gNbNodes;

  gNbNodes.Create (1);

 // Install the NR stack on the nodes

  NrHelper nrHelper;

  nrHelper.SetAttribute (“NrRelease”, StringValue (“15”));

  NetDeviceContainer ueDevices;

  NetDeviceContainer gNbDevices;

 UeDevices = nrHelper.InstallUeDevice (ueNodes);

  GNbDevices = nrHelper.InstallGnbDevice (gNbNodes);

  // Install the internet stack on the nodes

  InternetStackHelper internet;

  internet.Install (ueNodes);

  internet.Install (gNbNodes);

  // Assign IP addresses to the devices

  Ipv4AddressHelper address;

  address.SetBase (“10.1.1.0”, “255.255.255.0”);

  Ipv4InterfaceContainer ueInterfaces = address.Assign (ueDevices);

  Ipv4InterfaceContainer gNbInterfaces = address.Assign (gNbDevices);

// create and install applications

  Uint16_t port = 9;   // Discard port (RFC 863)

  OnOffHelper onoff (“ns3::UdpSocketFactory”, Address (InetSocketAddress (ueInterfaces.GetAddress (0), port)));

  onoff.SetConstantRate (Data Rate (“20Mbps”));

  ApplicationContainer apps = onoff.Install (gNbNodes.Get (0));

  apps.Start (Seconds (1.0));

  apps.Stop (Seconds (10.0));

  PacketSinkHelper sink (“ns3::UdpSocketFactory”, Address (InetSocketAddress (Ipv4Address::Get any (), port)));

  Apps = sink. Install (ue Nodes.Get (0));

  apps.Start (Seconds (1.0));

  apps.Stop (Seconds (10.0));

 // Run simulation

  Simulator::Run ();

  Simulator::Destroy ();

Return 0;

}

Measures to Evaluate the Performance

1. Configure the Simulation Environment:

• First of all, install NS-3 and the required modules such as the NR module.

2. Specify the Simulation Parameters:

• Determine the parameters like the number of UEs, mobility models, gNBs and traffic patterns.

3. Execute Simulations:

• For various scenarios and set ups, run the simulations.

4. Gather Data:

• To gather performance data, make use of NS-3’s tracing and logging characteristics.

5. Evaluate Findings :

• Regarding the data analysis and visualization, deploy tools such as Python, MATLAB or Wireshark.
• Visualize the packet loss, latency, throughput and other metrics by developing charts and graphs.

6. Enhance and Revise:

• Assess the developments through enhancing the network parameters and reiterate the simulations in terms of analysis.

How to analyze performance evaluation in 5g networks?

Performance evaluation often elucidates the potential of a system by considering its key performance metrics and functions. In 5G networks, an extensive guide is proposed by us that assist you on carrying out an impactful performance evaluation:

Significant Performance Metrics

1. Throughput: Across the network, it depicts the rate of data transfer.
2. Latency: In the network, this metric represents the time delay.
3. Packet Loss: Throughout the transmission process, it exhibits the ratio of missed packets.
4. Jitter: The differences in packet emergence are clearly illustrated.
5. Quality of Service (QoS): QoS evaluates the network, in what way it addresses the predefined service capacity standards.
6. Energy Efficiency: According to the performance, it depicts the energy-usage.
7. Resource Utilization: It significantly analyzes the network resources like memory, CPU and bandwidth on how it is utilized.
8. Reliability: To offer consistent and seamless service, this metric examines the potential or capability of a network.

Tools and Simulators

1. NS-3: This simulator is specifically designed for discrete-event network simulation.
2. OpenAirInterface (OAI): OAI is considerably applicable for Core Network simulation and 5G RAN.
3. Matlab: To examine the performance and for conventional simulations, MATLAB could be more beneficial.
4. Wireshark: Regarding network traffic analysis and packet capture, it enacts a crucial role.
5. Iperf: In the process of evaluating performance and network bandwidth, iperf is very essential.
6. SrsRAN: It is a publicly-accessible 5G software radio suite.
7. OMNeT++: For network simulation, OMNeT++ is highly adaptable.
8. MATLAB and Simulink: Communication systems are simulated and evaluated in a dynamic manner through these simulators.

Measures for Performance Evaluation

1. Configure the Simulation Environment

• Initially, install and build the preferable simulator, it may be OAI or NS-3.
• The network topology required to be specified and execute the 5G network parameters.

2. Develop the Simulation Scenarios

• To examine network setups, various traffic loads and mobility patterns, specify the different conditions.
• Analyze the edge cases and emphasize the network by incorporating scenarios.

3. Formulate and Capture Traffic

• Simulate various types of traffic such as web browsing, video streaming and file transfer with the help of traffic generation tools like iperf.
• Use tools such as Wireshark for extensive analysis to derive the network traffic data.

4. Execute Simulations

• For the illustrated scenarios, run the simulations.
• In order to collect significant data, examine the time interval of simulations.

5. Gather Performance Data

• Accumulate the data on packet loss, throughput and latency by using pre-defined tracing and logging techniques.
• Particularly for sufficient analysis, transfer the gathered data.

6. Evaluate the  Gathered Data

• Application of NS-3
• Throughput Calculation:

Ptr<PacketSink> sink = DynamicCast<PacketSink> (sinkApp.Get (0));

Double throughput = (sink->GetTotalRx () * 8) / (simulationTime * 1000000.0); // Mbps

• Latency Calculation: To evaluate the end-to-end latency, make use of FlowMonitor module,

FlowMonitorHelper flowmon;

Ptr<FlowMonitor> monitor = flowmon.InstallAll ();

…

Monitor->SerializeToXmlFile (“flowmon.xml”, true, true);

• Packet Loss Calculation:

Monitor->CheckForLostPackets ();

Ptr<Ipv4FlowClassifier> classifier = DynamicCast<Ipv4FlowClassifier> (flowmon.GetClassifier ());

std::map<FlowId, FlowMonitor::FlowStats> stats = monitor->GetFlowStats ();

For (std::map<FlowId, FlowMonitor::FlowStats>: const_iterator i = stats.begin (); i! = stats. End (); ++i)

{

    Ipv4FlowClassifier::FiveTuple t = classifier->FindFlow (i->first);

    std::cout << “Flow” << i->first << ” (” << t.sourceAddress << ” -> ” << t.destinationAddress << “)\n”;

    std::cout << “Tx Packets: ” << i->second.txPackets << “\n”;

    std::cout << “Rx Packets: ” << i->second.rxPackets << “\n”;

    std::cout << “Lost Packets: ” << i->second.lostPackets << “\n”;

}

• Application of Wireshark
  • Throughput Analysis:

In the course of time, visualize the throughput by deploying Wireshark’s I/O graph.

• Latency Analysis:

By using Wireshark’s “Statistics” -> “Latency” tools, evaluate the RTT (Round-Trip Time).

• Packet Loss Analysis:

To observe the statistics of packet loss, employ Wireshark’s “Statistics” -> “Summary”.

• Application of MATLAB
• Data Import

Data = readable (‘simulation_output.csv’);

• Throughput Calculation:

Throughput = data.ReceivedBytes * 8 / simulationTime; % in bits per second

• Latency Calculation:

Latency = mean (data. Latency);

• Packet Loss Calculation:

Packet loss = (data.SentPackets – data.ReceivedPackets) / data.SentPackets * 100; % in percentage

7. Visualize the Findings

• Illustrate the performance metrics by implementing graphical tools in Python (seaborn, matplotlib), MATLAB or pre-defined tools.
• Beyond various conditions or metrics throughout the years, design graphs for packet loss, throughput, latency and other metrics.

8. Understand the Outcomes

• For the purpose of developments, detect areas, challenges and patterns by evaluating the visualized data.
• To interpret the implications of diverse determinants on network performance, contrast the performance over various conditions.

9. Enhancement and Refinement

• Optimize the network performances by suggesting and executing developments in accordance with analysis.
• Verify the advancements through rewriting the simulations with the updated configurations.

Instance of Scenario configurations in NS-3

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

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

#include “ns3/applications-module.h”

#include “ns3/flow-monitor-module.h”

Using namespace ns3;

NS_LOG_COMPONENT_DEFINE (“5GPerformanceEvaluation”);

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

{

  CommandLine cmd;

  cmd.Parse (argc, argv);

  NodeContainer nodes;

  nodes.Create (4);

  PointToPointHelper pointToPoint;  pointToPoint.SetDeviceAttribute (“DataRate”, StringValue (“5Gbps”));

  pointToPoint.SetChannelAttribute (“Delay”, StringValue (“1ms”));

  NetDeviceContainer devices;

  Devices = pointToPoint. Install (nodes);

  InternetStackHelper stack;

  Stack. Install (nodes);

  Ipv4AddressHelper address;

  address.SetBase (“10.1.1.0”, “255.255.255.0”);

  Ipv4InterfaceContainer interfaces = address.Assign (devices);

  Uint16_t port = 9;   // Discard port (RFC 863)

  OnOffHelper onoff (“ns3::UdpSocketFactory”, Address (InetSocketAddress (interfaces.GetAddress (1), port)));

  onoff.SetConstantRate (DataRate (“2Gbps”));

  ApplicationContainer apps = onoff.Install (nodes.Get (0));

  apps.Start (Seconds (1.0));

  apps.Stop (Seconds (10.0));

  PacketSinkHelper sink (“ns3::UdpSocketFactory”, Address (InetSocketAddress (Ipv4Address::GetAny (), port)));

  Apps = sink.Install (nodes.Get (1));

  apps.Start (Seconds (1.0));

  apps.Stop (Seconds (10.0));

  FlowMonitorHelper flowmon;

  Ptr<FlowMonitor> monitor = flowmon.InstallAll ();

  Simulator::Stop (Seconds (11.0));

  Simulator::Run ();

Monitor->CheckForLostPackets ();

  Ptr<Ipv4FlowClassifier> classifier = DynamicCast<Ipv4FlowClassifier> (flowmon.GetClassifier ());

  std::map<FlowId, FlowMonitor::FlowStats> stats = monitor->GetFlowStats ();

  For (std::map<FlowId, FlowMonitor::FlowStats>:: const_iterator i = stats.begin (); i != stats. End (); ++i)

  {

    Ipv4FlowClassifier::FiveTuple t = classifier->FindFlow (i->first);

    NS_LOG_UNCOND (“Flow” << i->first << ” (” << t.sourceAddress << ” -> ” << t.destinationAddress << “)”);

    NS_LOG_UNCOND (“Tx Packets: ” << i->second.txPackets);

    NS_LOG_UNCOND (“Rx Packets: ” << i->second.rxPackets);

    NS_LOG_UNCOND (“Lost Packets: ” << i->second.lostPackets);

    NS_LOG_UNCOND (“Throughput: ” << i->second.rxBytes * 8.0 / (i->second.timeLastRxPacket.GetSeconds () – i->second.timeFirstTxPacket.GetSeconds ()) / 1000000 << “Mbps”);

    NS_LOG_UNCOND (“Mean delay: ” << i->second.delaySum.GetSeconds () / i->second.rxPackets);

  }

  Simulator::Destroy ();

  Return 0;

}