In mmWave technology, there are several research topics and ideas emerging continuously. We work on all areas of  mmWAVE NS3 be free to address us all your doubts and get guidance from world’s leading writers by discussing with our team all your queries get valued insights for your work. To research mmWave mechanisms, the following are few progressive research topics and plans:

1. Beamforming Techniques in mmWave Networks

Research Topic:

• In mmWave networks, research the effectiveness of different beamforming approaches such as digital, analog, and hybrid beamforming.

Plans:

• For efficient beamforming in various settings, like rural, urban, and indoor platforms, aim to construct suitable methods.
• The influence of beamforming on network throughput, consistency, and delay has to be investigated.
• To sustain extreme link quality in the existence of mobility, deploy beam tracking methods.

2. Channel Modeling and Estimation

Research Topic:

• Determining aspects like multipath propagation, path loss, and shadowing, create precise channel models for mmWave interaction.

Plans:

• Focus on applying various channel models in ns-3 and contrast their effectiveness.
• On mmWave channel features, research the influence of different ecological aspects such as vegetation, buildings.
• Specifically, for mmWave frameworks, construct and assess channel estimation approaches.

3. Interference Management in Dense mmWave Deployments

Research Topic:

• For dense mmWave implementations, including small cells and heterogenous networks, research interference management policies.

Plans:

• For dynamic intervention organization and reduction, aim to construct methods.
• In decreasing intervention, explore the effectiveness of coordinated multipoint (CoMP) approaches.
• To enhance network effectiveness, apply spectrum sharing and dynamic frequency selection techniques.

4. Energy Efficiency in mmWave Networks

Research Topic:

• In mmWave communication frameworks, investigate policies to improve energy effectiveness.

Plans:

• For mmWave base stations and user devices, deploy power control and energy-efficient methods.
• On energy utilization and network effectiveness, research the influence of sleep mode approaches.
• In various settings, assess the trade-offs among energy efficacy and interaction effectiveness.

5. Mobility Management and Handover

Research Topic:

• To sustain consistent connectivity, research mobility management and handover approaches in mmWave networks.

Plans:

• In order to reduce delay and packet loss at the time of user mobility, focus on constructing handover methods.
• The influence of user mobility on beamforming and beam monitoring effectiveness has to be investigated.
• In ns-3, deploy and assess pre-emptive and responsive handover technologies.

6. Integration of mmWave with Sub-6 GHz Networks

Research Topic:

• To attain coherent and consistent interaction, examine the combination of mmWave and sub-6 GHz networks.

Plans:

• For enhanced effectiveness, construct multi-connectivity methods that utilizes mmWave as well as sub-6 GHz bands.
• It is appreciable to investigate the effectiveness of dual connectivity and carrier aggregation approaches.
• On the basis of network situations, apply methods for dynamic traffic steering among mmWave and sub-6 GHz bands.

7. Vehicular Communication using mmWave (mmWave V2X)

Research Topic:

• The purpose of mmWave technology for vehicular communication (V2X) to assist automated driving and intelligent transportation frameworks has to be explored.

Plans:

• Mainly, for V2X interaction, aim to build and assess beamforming and beam tracking methods.
• Focus on investigating the influence of vehicle mobility and road platform on mmWave V2X effectiveness.
• Through the utilization of mmWave V2X interaction, deploy and examine safety and non-safety applications.

8. Quality of Service (QoS) and Quality of Experience (QoE)

Research Topic:

• In mmWave networks, research QoS and QoE parameters and aim to create approaches to assure high-quality service delivery.

Plans:

• For mmWave networks, deploy QoS-aware scheduling and resource allotment methods.
• The influence of various network metrics on QoE and QoS for different applications such as VR, video streaming has to be investigated.
• Aim to create technologies to dynamically adapt network metrics in order to sustain extreme QoS.

9. Security and Privacy in mmWave Networks

Research Topic:

• In mmWave interaction, investigate safety and confidentiality limitations and focus on constructing approaches to solve them.

Plans:

• Appropriate for mmWave interaction, apply encryption and authentication protocols.
• It is approachable to explore the susceptibility of mmWave networks to different assaults such as jamming, eavesdropping and construct appropriate solutions.
• For safe and confidentiality beamforming and beam management, create suitable approaches.

Instance Implementation in ns-3

You can utilize the mmWave module constructed by the NYU Wireless forum to deploy and simulate these research topics in ns-3. For simulating mmWave interaction, this module offers beneficial tools and frameworks. The following is a simple instance of how to configure an mmWave simulation in ns-3:

1. Set Up ns-3 Environment: It is advisable to assure that you have ns-3 installed and the mmWave module combined.
2. Create a Simulation Script: To describe the network metrics, aim to write a C++ or Python script and simulate the appropriate setting.

#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/mmwave-module.h”

using namespace ns3;

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

{

    // Set up default simulation parameters

    double simTime = 10.0;

    uint16_t numberOfNodes = 2;

    // Create nodes

    NodeContainer nodes;    nodes.Create(numberOfNodes);

    // Set up mobility model

    MobilityHelper mobility;    mobility.SetMobilityModel(“ns3::ConstantPositionMobilityModel”);

    mobility.Install(nodes);

    // Set up mmWave devices and channel

    mmwave::MmWaveHelper mmWaveHelper;

    NetDeviceContainer devices = mmWaveHelper.InstallEnbUeDevice(nodes);

    // Set up the Internet stack

    InternetStackHelper internet;

    internet.Install(nodes);

    // Assign IP addresses

    Ipv4AddressHelper ipv4;

    ipv4.SetBase(“10.1.1.0”, “255.255.255.0”);

    Ipv4InterfaceContainer interfaces = ipv4.Assign(devices);

    // Set up applications

    uint16_t port = 50000;

    OnOffHelper onOffHelper(“ns3::UdpSocketFactory”, InetSocketAddress(interfaces.GetAddress(1), port));    onOffHelper.SetConstantRate(DataRate(“100Mbps”));

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

    apps.Start(Seconds(1.0));    apps.Stop(Seconds(simTime));

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

    apps = packetSinkHelper.Install(nodes.Get(1));

    apps.Start(Seconds(1.0));    apps.Stop(Seconds(simTime));

    // Run the simulation    Simulator::Stop(Seconds(simTime));

    Simulator::Run();

    Simulator::Destroy();

    return 0;

}

3. Run the Simulation: Through the utilization of the ns-3 build framework, compile and execute the script.

./waf –run your_simulation_script

4. Analyze Results: By utilizing ns-3’s tracing and logging characteristics, gather and examine the simulation outcomes.

How to simulate mmWave research work using ns3?

Numerous procedures should be adhered to configure your simulation platform, describe the network topology, arrange metrics, and examine the outcomes, when simulating mmWave research works with ns-3. We provide an extensive instruction that assist you to initiate the mmWave simulations in ns-3:

Step 1: Install ns-3 and the mmWave Module

Initially, together with the mmWave module, you must have ns-3 installed. As described below, you can copy the warehouse and develop ns-3 with the mmWave module:

1. Clone ns-3 and the mmWave module repository:

git clone https://gitlab.com/nsnam/ns-3-allinone.git

cd ns-3-allinone

./download.py

git clone https://github.com/signetlabdei/mmwave.git

2. Build ns-3 with the mmWave module:

./build.py –enable-examples –enable-tests

Step 2: Set Up Your Simulation Script

To explain your mmWave network metrics and setting, construct a novel simulation script or alter a previous script.

Instance Simulation Script

The following is a simple instance of simulation script in C++ that arranges a basic mmWave network through the utilization of the mmWave 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/applications-module.h”

#include “ns3/mmwave-helper.h”

#include “ns3/mmwave-module.h”

using namespace ns3;

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

{

    // LogComponentEnable (“MmWaveExample”, LOG_LEVEL_INFO);

    // Set simulation parameters

    double simTime = 10.0;

    uint16_t numUeNodes = 2;

    uint16_t numEnbNodes = 1;

    // Create nodes

    NodeContainer ueNodes;

    ueNodes.Create(numUeNodes);

    NodeContainer enbNodes;    enbNodes.Create(numEnbNodes);

    // Set up the mobility model

    MobilityHelper mobility;    mobility.SetMobilityModel(“ns3::ConstantPositionMobilityModel”);

    mobility.Install(ueNodes);

    mobility.Install(enbNodes);

    // Set up the mmWave helper

    Ptr<MmWaveHelper> mmwaveHelper = CreateObject<MmWaveHelper>();

    mmwaveHelper->SetSchedulerType(“ns3::MmWaveFlexTtiMaxWeightMacScheduler”);

    // Install mmWave devices on the nodes

    NetDeviceContainer enbDevices = mmwaveHelper->InstallEnbDevice(enbNodes);

    NetDeviceContainer ueDevices = mmwaveHelper->InstallUeDevice(ueNodes);

    // Attach the UEs to the eNB

    mmwaveHelper->AttachToClosestEnb(ueDevices, enbDevices);

    // Set up the Internet stack

    InternetStackHelper internet;

    internet.Install(ueNodes);

    internet.Install(enbNodes);

    // Assign IP addresses

    Ipv4AddressHelper ipv4;

    ipv4.SetBase(“7.0.0.0”, “255.0.0.0”);

    Ipv4InterfaceContainer enbIpIfaces = ipv4.Assign(enbDevices);

    Ipv4InterfaceContainer ueIpIfaces = ipv4.Assign(ueDevices);

    // Set up applications

    uint16_t dlPort = 1234;

    uint16_t ulPort = 2000;

    OnOffHelper dlClientHelper(“ns3::UdpSocketFactory”, InetSocketAddress(ueIpIfaces.GetAddress(0), dlPort));    dlClientHelper.SetConstantRate(DataRate(“100Mbps”));

    ApplicationContainer clientApps = dlClientHelper.Install(enbNodes.Get(0));    clientApps.Start(Seconds(1.0));    clientApps.Stop(Seconds(simTime));

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

    ApplicationContainer serverApps = dlPacketSinkHelper.Install(ueNodes.Get(0));    serverApps.Start(Seconds(1.0));    serverApps.Stop(Seconds(simTime));

    // Run the simulation

    Simulator::Stop(Seconds(simTime));

    Simulator::Run();

    Simulator::Destroy();

    return 0;

}

Step 3: Build and Run the Simulation

1. Save the script: In the scratch folder of your ns-3 directory, save the above script as mmwave-example.cc.
2. Build the simulation: To compile your script, employ the waf build model.

./waf

./waf –run scratch/mmwave-example

./waf

./waf –run scratch/mmwave-example

Step 4: Analyze the Results

To assess the effectiveness of your mmWave network, you can explore the output data after executing the simulation. In order to record network parameters like packet loss, throughput, and latency, ns-3 offers different tracing technologies.

1. Enable Tracing: To gather extensive simulation data, you can facilitate tracking in your script.

AsciiTraceHelper asciiTraceHelper;

mmwaveHelper->EnableAsciiAll(asciiTraceHelper.CreateFileStream(“mmwave-trace.tr”));

2. Post-Process Data: Specifically, to examine the trace files and visualize the outcomes, utilize ns-3’s in-built tools or external tools such as MATLAB or Wireshark.

Progressive Simulation Plans

1. Beamforming and Beam Tracking: Focus on deploying and assessing various beamforming and beam tracking methods in order to enhance link quality and system capability.
2. Mobility Scenarios: To investigate the influence of mobility on mmWave effectiveness, simulate different mobility settings like high-speed trains or urban vehicular platforms.
3. Interference Management: Mainly, in intense mmWave implementations, construct and evaluate intervention management approaches to improve network consistency and effectiveness.
4. Multi-Connectivity: To attain consistent and coherent interaction, examine the advantages of multi-connectivity by combining mmWave along with sub-6 GHs bands.
5. Quality of Service (QoS): In order to make sure service variation and assure effectiveness for significant applications, apply QoS-aware scheduling methods.