Modeling and Analysis of Wireless Networks

Wireless networks, whether used in homes, cities, industries, or military operations, must operate reliably across changing conditions and diverse use cases. Applications such as video calls, emergency alerts, and file transfers, each place different demands on the network, requiring varying levels of performance in terms of latency, throughput, capacity, and quality of service (QoS).

Modeling wireless networks helps you evaluate these demands before real-world deployment. You can simulate node mobility, protocol behavior, traffic surges, interference, and failure conditions, all without building a physical network. Modeling also enables you to test how many users the network can support simultaneously, how throughput holds up under load, and how well the system meets QoS requirements as demand grows.

For example:

• A telecom engineer can test 5G cell layouts to ensure QOS and high user capacity.

• A system engineer can simulate Wi-Fi^® and Bluetooth^® coexistence in public spaces to maintain consistent throughput.

• A researcher can study how mobile ad hoc networks (MANETs) perform in military scenarios where reliability and low latency are critical.

Wireless Network Toolbox™ enables you to simulate these scenarios. This topic presents various aspects of modeling, logging, and analyzing networks.

Node and network modeling

Wireless Network Toolbox™ enables you to model these aspects of wireless networks.

• Model node and its protocol stack — Model a wireless node and its protocol stack using the wnet.Node class in Wireless Network Toolbox. This includes defining layers such as application, transport, network, MAC, and physical (PHY). You can configure each layer with custom components to simulate realistic behavior, enabling end-to-end communication scenarios in network simulations. You can model the packet exchanged between the nodes using wirelessPacket. To model the packet queuing, use wnet.FIFOQueue.

  For modeling standard-based nodes, see the nrGNB (5G Toolbox), nrUE (5G Toolbox), bluetoothNode (Bluetooth Toolbox), bluetoothLENode (Bluetooth Toolbox), and wlanNode (WLAN Toolbox) objects.

• Network scalability — As you increase the scale of the network, network performance changes because more nodes share the available channel resources. Networks can range in size from 2 nodes to thousands. By simulating many nodes, you can use network simulation to evaluate scalability and directly measure how changing the number of nodes impacts network performance.

• Node placement — In network simulation, you use node position allocation strategies to assign initial positions to the nodes within a network. Node placement strategies are important when setting up the network, as the spatial arrangement of nodes affects the simulation results. Wireless Network Toolbox provides these features for allocating node positions: random allocation and grid-based allocation. For more information about these features, see the nodePositionRandom and nodePositionGrid functions, respectively.

• Model mobility patterns — You can model wireless node movement to study its impact on signal strength and overall network performance. Wireless Network Toolbox supports three mobility models: random walk (nodeMobilityRandomWalk), random waypoint (nodeMobilityRandomWaypoint), and constant velocity (nodeMobilityConstantVelocity). You can also create custom mobility models using the wnet.Mobility base class.

• Channel modeling — The wirelessNetworkSimulator object provides a free-space path loss model by default and supports the use of custom channel model. For more information about how to add a custom channel model or the path loss model to the wireless network simulation, see addChannelModel.

• Model traffic patterns — You can model various application traffic patterns, such as file transfer protocol (networkTrafficFTP), On-Off (networkTrafficOnOff), video conference (networkTrafficVideoConference), and voice over Internet protocol (networkTrafficVoIP). To see an example of how to use a predefined model, see the Generate and Visualize FTP Application Traffic Pattern. The example demonstrates how to model FTP traffic, featuring file transfers with fixed sizes and varying read times to illustrate FTP behavior. In addition to these predefined models, you can create custom traffic patterns using the wnet.Traffic base class to meet specific simulation requirements.

• PHY fidelity levels — Wireless Network Toolbox enables you to simulate two different fidelity levels of the PHY layer: full PHY and link-to-system, mapping-based, abstracted PHY. Full PHY involves waveform generation and decoding, while abstracted PHY models link quality and performance to calculate the packet error probability.

• Model coexistence — Coexistence modeling represents how multiple wireless systems operating in the same frequency band, such as Bluetooth, Wi-Fi, and ZigBee in the 2.4 GHz ISM band, interact in shared physical environments. These technologies, often deployed in close proximity and sometimes within the same node, can interfere with each other, leading to degraded performance.

  Wireless Network Toolbox enables you to simulate coexistence scenarios involving multiple wireless technologies operating in shared spectrum environments. You can model coexistence between heterogeneous systems such as wireless local area networks (WLAN) and Bluetooth, both of which operate in the 2.4 GHz ISM band. The toolbox enables you to experiment with various coexistence strategies, helping you evaluate their impact on network performance and interference mitigation.

  For examples of coexistence modeling, see the Simulate Noncollaborative Coexistence of WLAN, Bluetooth LE, and Bluetooth BR/EDR Networks (Bluetooth Toolbox), Bluetooth BR/EDR Data and Voice Communication with WLAN Signal Interference (Bluetooth Toolbox), and Noncollaborative Bluetooth LE Coexistence with WLAN Signal Interference (Bluetooth Toolbox) examples .

• Model mobile ad-hoc networks (MANETs) — Wireless Network Toolbox™ supports configuring and simulating node mobility and dynamic topology changes, enabling you to model MANETs. It also facilitates the implementation of ad-hoc routing protocols such as AODV, allowing the network to adapt to structural changes. Furthermore, the toolbox provides tools to assess essential network performance metrics, including throughput, routing overhead, packet delivery ratio, and average end-to-end delay.

  For examples of MANET modeling, see the Performance Evaluation of TDMA-Based MANET and AODV Routing in TDMA-Based MANET examples.

• Run the simulation — You can simulate the configured network scenario using a wireless network simulator. For more information about this simulator, see the wirelessNetworkSimulator object.

• Simulate wireless networks within graphical user interface — Wireless Network Modeler app enables you to model and analyze the performance of both 5G and WLAN.

Log, Visualize, and Analyze Network Behavior

By comparing the performance of different network configurations, algorithms, and deployment scenarios, you can get insight into the behavior of a system and make informed decisions regarding network design, optimization, and resource allocation. Wireless Network Toolbox enables you to:

• Capture IQ samples of the composite signal received at a node, including interference effects, for detailed signal analysis (wirelessIQLogger).

• Log network events such as packet transmissions and receptions for later analysis of node behavior (wirelessNetworkEventTracer).

• View packet communication in the simulated network over time and frequency domains. It shows packet transmission details, the state of the MAC and PHY layers, and identifies packet types within the frequency spectrum (wirelessTrafficViewer).

• View the nodes in the wireless network along with their mobility (wirelessNetworkViewer).

• Log and analyze captured protocol packets using a PCAP file. For more information about this, see pcapReader, pcapWriter, and pcapngWriter.

• Evaluate key performance indicators (KPIs) such as throughput, scheduling fairness, packet error rate, and spectral efficiency, and visualize these metrics.

• Retrieve various statistics captured at different layers of a node.