802.11 mac and application throughput measurement -凯发k8网页登录

throughput in 802.11 networks

the ieee® 802.11™ working group is continually adding features to 802.11 specification [ 1 ] to improve the throughput and reliability in wlan networks. throughput is the amount of data transmitted over a period of time. medium access control (mac) layer throughput refers to the amount of data successfully transmitted by the mac layer over a period of time. mac protocol data unit (mpdu) is the unit of transmission at mac layer. in 802.11n, mpdu aggregation was introduced to increase the throughput. when mpdu aggregation is supported, mac layer aggregates multiple mpdus into an aggregated mpdu (a-mpdu) for transmission. this reduces the overhead of channel contention for transmitting multiple frames, resulting in enhanced throughput. in 802.11ac [ 1 ] and 802.11ax [ 2 ], the maximum limits for an a-mpdu length were increased resulting in even better throughput in wlan networks.

model 802.11 network

this example models a wlan network with five nodes as shown in this figure. these nodes implement carrier-sense multiple access with collision avoidance (csma/ca) with physical carrier sense and virtual carrier sense. the physical carrier sensing uses the clear channel assessment (cca) mechanism to determine whether the medium is busy before transmitting. whereas, the virtual carrier sensing uses the rts/cts handshake to prevent the hidden node problem.

the model in the example displays various statistics such as the number of transmitted, received, and dropped packets at phy and mac layers. moreover, the runtime figures that help in analyzing/estimating the node-level and network-level performance are also displayed in this model. this model is validated against the published calibration results from the tgax task group [ 4 ] for box 3 scenarios (tests 1a, 1b, and 2a) specified in tgax evaluation methodology [ 3 ].

wlan network

components of a wlan node

the components of a wlan node are shown in this figure. the information is retrieved by pressing the arrow button for each node in the above figure.

application, edca mac, phy, and channel block capabilities

application:

the application layer has the capability to generate data with different priority levels as shown in this figure. these priority levels are configured using access category property in the mask parameters of the application traffic generator block inside a wlan node. you can also configure the packet size, inter-packet interval, and destination node for the application layer.

edca mac:

the edca mac block used in this example has the following capabilities:

phy:

the phy transmitter and phy receiver blocks have the capability to generate and decode waveforms of non-ht, ht-mf, vht, he-su and he-ext-su formats. you can configure the transmit gain and transmit power using the tx gain and tx power properties in the mask parameters of the phy transmitter block inside a wlan node.

similarly, you can configure the receive gain and receive noise figure using the rx gain and rx noise figure properties in the mask parameters of the phy receiver block inside a wlan node.

channel:

channel impairments determined by free-space path-loss model and rayleigh multipath fading are added to the transmitted phy waveform. you can choose to enable or disable these impairment models. in addition to the impairment models, the signal reception range can also be limited by an optional range propagation loss model. to model any of these losses, the channel model must contain both the sender and receiver positions along with the transmitted signal strength. the channel is modeled inside each receiving node, before passing the waveform to the phy receiver block.

throughput measurement

throughput varies for different configuration parameters pertaining to the application, mac & phy layers. any change in the configuration may either increase or decrease the throughput. you can vary the combination of these parameters to measure and analyze the throughput.

along with above parameters, you can also vary the node positions, tx & rx gains, channel loss, number of nodes in the network, mac contention parameters, number of transmit chains and rate adaptation algorithms to analyze mac throughput. this example demonstrates the measurement and analysis of the mac throughput by varying packet size in the application traffic generator block.

application packet size

throughput is directly proportional to the application packet size. smaller packet size results in greater number of packets to be transmitted. at the mac layer, there is an overhead of contention time for each transmitted packet. this is because the mac layer makes sure that the channel is idle for a specific amount of time (refer section 10.3.2.3 of [ 1 ]) before transmitting any packet. therefore, as the packet size decreases, the contention overhead increases resulting in lower throughput.

model configuration

you can configure the application packet size using these steps:

run the simulation and observe the throughput. the tgax calibration results for test-1a in [ 4 ] are shown below:

the above plot compares the calibration results for wlan toolbox against the published results of other companies listed in [ 4 ]. the blue colored curve represents the results of wlan toolbox, while the grey colored curves represent the results of other companies.

simulation results

the simulation of the model generates:

this figure shows mac state transitions with respect to simulation time.

you can also observe the live state of the mac layer transmission buffers using the 'observe mac queue lengths' button in the above visualization.

this figure shows the network statistics at the end of simulation.

validating application layer throughput with tgax calibration results

the tgax task group [ 4 ] published application throughput results for different scenarios. you can observe the layer 3 (above mac layer) throughput of each node in the network in 'throughput' column in 'statisticstable' stored in 'statistics.mat'. the tgax calibration scenarios for mac simulator published results of application throughput for a user datagram protocol (udp) with logical link control (llc) layers overhead.

to calculate application throughput from simulation results use the code below:

% load statistics.mat (output of the simulation) file
simulationresults = load('statistics', 'statisticstable');
% statistics
stats = simulationresults.statisticstable;
% successfully transmitted mac layer bytes in the network
totalmactxbytes = sum(stats.mactxbytes);
% udp & llc overheads (bytes)
udpoverhead = 36;
llcoverhead = 8;
% udp & llc overhead (bytes) in the network
udpandllcoverhead = sum(stats.mactxsuccess)*(udpoverhead   llcoverhead);
% successfully transmitted application bytes
totalapptxbytes = totalmactxbytes - udpandllcoverhead;
% time at which last transmission is completed in the network (microseconds)
simulationtime = max(stats.macrecentframestatustimestamp);
% application throughput (mbps)
applicationthroughput = (totalapptxbytes*8)/simulationtime;
disp(['application throughput = ' num2str(applicationthroughput) ' mbps']);

application throughput = 4.7276 mbps

the application throughput for different tgax calibration scenarios is plotted against different mac service data unit (msdu) sizes for a simulation time of 30 seconds as shown below:

further exploration

configuration options

you can change these configuration parameters to further explore this example:

related examples

refer these examples for further exploration:

this example enables you to create and configure a multi-node 802.11 network using a simulink model for analyzing the mac and application layer throughput. in this model, the mac throughput obtained through the simulation results is used to calculate the application layer throughput. this model is validated using the box 3 scenarios (tests 1a, 1b, and 2a) specified in tgax evaluation methodology [ 3 ] to confirm that it complies with the ieee 802.11 [ 1 ]. this example concludes that the calculated application layer throughput is within the range of minimum and maximum throughput specified in published calibration results [ 4 ].

appendix

the helper functions and objects used in this example are:

references