Communications Networks

How stochastic network traffic causes timing latency and uncertainty in an anti-lock braking system that uses CAN communications.

Model an Ethernet communication network with CSMA/CD protocol using Simulink® messages and SimEvents®. In the example, there are three computers that communicate through an Ethernet communication network. Each computer has a software component that generates data and an Ethernet interface for communication. Each computer attempts to send the data to another computer with a unique MAC address. An Ethernet interface controls the interaction between a computer and the network by using a CSMA/CD communication protocol. The protocol is used to respond to collisions that occur when multiple computers send data simultaneously. The Ethernet component represents the network and the connection between the computers.

Measure the MAC and application layer throughput in a multi-node 802.11a/n/ac/ax network using SimEvents®, Stateflow®, and WLAN Toolbox™. The system-level model presented in this example includes functionalities such as configuring the priority of the traffic at the application layer, capability to generate and decode waveforms of Non-HT, HT-MF, VHT, HE-SU and HE-EXT-SU formats, MPDU aggregation and enabling block acknowledgment of MPDUs. The application layer throughput calculated using this model is validated against published calibration results from the TGax Task Group [ 4 ] for Box 3 scenarios (Tests 1a, 1b, and 2a) specified in TGax evaluation methodology [ 3 ]. The obtained application layer throughput is within the range of minimum and maximum throughput specified in published calibration results [ 4 ].

Demonstrates how to model a multi-node IEEE® 802.11ax™ [ 1 ] network with abstracted physical layer (PHY) using SimEvents®, Stateflow®, and WLAN Toolbox™. A PHY abstraction model largely reduces the complexity and the duration of system-level simulations by replacing the actual physical layer computations. This makes it possible to evaluate systems consisting of large number of nodes, resulting in increased scalability. Abstracted PHY models signal-power, gain, delay, loss and interference on each packet without generating physical layer packets, as specified by the TGax Evaluation Methodology [ 3 ].