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 ].

Simulate medium access control (MAC) quality of service (QoS) traffic scheduling in 802.11a/n/ac/ax network using SimEvents®, Stateflow®, and WLAN Toolbox™. Using QoS, the MAC layer prioritizes the application data and allocates more resources to high priority traffic compared to low priority traffic. The MAC layer used in this example implements the enhanced distributed channel access (EDCA) algorithm to get access to the channel for transmission. This example models a 802.11 network with five nodes that can be simultaneously configured with four types of application traffic namely: Best-effort, Background, Video and Voice. The type and the priority of application traffic is identified by the access category (AC) at the MAC layer. The derived results show that the highest priority voice traffic incurs the lowest latency, whereas the lowest priority background traffic results in highest latency.

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 ].

Model communication between multiple WLAN nodes containing medium access control (MAC) layer and PHY using SimEvents®, Stateflow®, and WLAN Toolbox™. This example models a 802.11 network consisting of five WLAN nodes. The distributed coordination function (DCF) in MAC is modeled as a Discrete Event Chart, whereas the PHY is modeled using MATLAB Discrete Event System block. The derived simulation results include a run-time plot displaying the time spent on channel contention, transmission, and reception for each node. A plot representing the per-node metrics such as number of transmitted, received, and dropped packets at PHY and MAC layers is also shown.

Perform a PHY-focused system-level simulation for IEEE® 802.11ax™. Part (A) validates the simulation scenario, radio characteristics, and large-scale fading model by comparing against published calibration results. Part (B) estimates the packet error rate of the 802.11ax network by simulating individual links between active nodes under a basic clear channel assessment scheme.

Demonstrates IEEE® 802.11ax™ physical layer abstraction for system-level simulation. A link quality model and link performance model based on the TGax evaluation methodology are presented and validated by comparing with published results.

Simulate Bluetooth® low energy (BLE) coexistence with WLAN interference using the Communications Toolbox™ Library for the Bluetooth Protocol and the WLAN Toolbox™. Coexistence mechanisms are used to minimize the interference of WLAN on BLE network. In this example, the collision probability and interference level of each WLAN network is used to corrupt the BLE signals. The simulation results generated in this example conclude that for high collision probability and interference level of a WLAN channel, the achieved success rate of the respective BLE channel is low.

Demonstrates how to model a multinode downlink orthogonal frequency-division multiple access (OFDMA) IEEE® 802.11ax™ [ 1 ] scenario using SimEvents®, Stateflow®, and WLAN Toolbox™. In OFDMA, independent groups of subcarriers are allocated to distinct users to realize simultaneous transmission. This multiplexing technique provides significant advantages in terms of spectrum efficiency, contention, latency, and network jitter. This example provides a model to enable downlink (DL) OFDMA communication in a 802.11ax network. The network topology consists of an access point (AP) and four linearly placed stations. The model enables you to configure multiple applications with same access category (AC) for different destination stations. In this example, the allocation of resource units (RUs) to the stations is fixed based on the number of users. The AP schedules transmissions to the stations in a round-robin fashion. The derived plot shows that the DL throughput at the AP is higher with OFDMA as compared to OFDM.