Wireless network hybrid simulation
View Patent ↗A simulation method and system partitions network traffic into background traffic and explicit traffic, wherein explicit traffic is processed in detail, and background traffic is processed at a more abstract level. The packets of explicit traffic are modeled in complete detail, so that precise timing and behavior characteristics can be determined, whereas large volumes of traffic are modeled more abstractly as background flows, and only certain aspects, such as routing through the network, are simulated. Tracer packets are used to model the background traffic and carry a number of characteristics of interest for generating simulation results. In this manner, the effect of the background traffic on the explicit traffic can be modeled at each network element. The abstract processing of background traffic is facilitated by techniques that include multi-variate table look-up, neural networks, and the like.
1. A method of simulating a network comprising:
determining transmission events at a first node of the network,
determining a plurality of reception events at other nodes of the network corresponding to each of the transmission events, and
simulating the reception events at the other nodes;
wherein:
at least one of the transmission events includes an explicit-traffic packet, and
at least another of the transmission events includes a tracer packet that represents a plurality of background-traffic packets, and
simulating the reception events at the other nodes includes determining an effect of the background-traffic packets on the explicit-traffic packet.
2. The method of claim 1 , wherein
determining the plurality of reception events includes simulating a model of a media access protocol.
3. The method of claim 2 , wherein
simulating the model of the media access protocol includes determining a packet transfer time corresponding to a difference in time between each transmission event and each corresponding reception event.
4. The method of claim 3 , wherein
simulating the model of the media access protocol includes determining at least one output value from a table of values.
5. The method of claim 3 , wherein
simulating the model of the media access protocol includes providing input values to a neural network to determine at least one output value.
6. The method of claim 1 , wherein
determining the plurality of reception events includes:
determining at least one output value from a table of values, and
scheduling a reception event of the plurality of reception events based on the at least one output value.
7. The method of claim 6 , further including
determining the at least one output value for inclusion in the table of values, based on simulations using discrete element models.
8. The method of claim 1 , wherein
determining the plurality of reception events includes:
providing input values to a neural network to determine at least one output value, and
scheduling a reception event of the plurality of reception events based on the at least one output value.
9. The method of claim 8 , further including
training the neural network, based on simulations using discrete element models.
10. The method of claim 1 , wherein
simulating the reception events at the other nodes includes:
generating background-traffic events at the other nodes at a simulation time that is prior to a scheduled time of arrival of the explicit-traffic packet, to determine the effect of the background-traffic packets on the explicit-traffic packet, and
terminating the generating of background-traffic events at the other nodes after the effect of the background-traffic packets on the explicit-traffic packet is determined.
11. The method of claim 1 , wherein
simulating the reception events at the other nodes includes:
determining a state of each node based on flow information contained in the background-traffic packets received at the node, and
estimating parameters associated with the explicit-traffic packet, based on characteristics associated with the state of the node.
12. The method of claim 11 , wherein
each background-traffic packet has an associated duration period, and
determining the state of each node based on the flow information is dependent upon the duration period.
13. The method of claim 11 , wherein
estimating the parameters associated with the explicit-traffic packet based on characteristics associated with the state of the node includes determining at least one output value from a table of values.
14. The method of claim 11 , wherein
estimating the parameters associated with the explicit-traffic packet based on characteristics associated with the state of the node includes providing input values to a neural network to determine at least one output value.
15. The method of claim 1 , wherein
simulating the reception event at the other nodes includes:
simulating the explicit-traffic packet at a first level of detail, and
simulating the background-traffic packets represented by the tracer packet at a second level of detail that is substantially less than the first level of detail.
16. A computer readable medium that includes a program for execution on a processing system that is configured to cause the system to:
determine transmission events at a first node of the network,
determine a plurality of reception events at other nodes of the network corresponding to each of the transmission events, and
simulate the reception events at the other nodes;
wherein:
at least one of the transmission events includes an explicit-traffic packet, and
at least another of the transmission events includes a tracer packet that represents a plurality of background-traffic packets, and
simulating the reception events at the other nodes includes determining an effect of the background-traffic packets on the explicit-traffic packet.
17. The computer readable medium of claim 16 , wherein
determining the plurality of reception events includes determining a packet transfer time corresponding to a difference in time between each transmission event and each corresponding reception event.
18. The computer readable medium of claim 16 , wherein
simulating the reception events includes determining at least one output value from a table of values.
19. The computer readable medium of claim 16 , wherein
simulating the reception events includes providing input values to a neural network to determine at least one output value.
20. The computer readable medium of claim 16 , wherein
determining the plurality of reception events includes:
determining at least one output value from a table of values, and
scheduling a reception event of the plurality of reception events based on the at least one output value.
21. The computer readable medium of claim 16 , wherein
determining the plurality of reception events includes:
providing input values to a neural network to determine at least one output value, and
scheduling a reception event of the plurality of reception events based on the at least one output value.
22. The computer readable medium of claim 16 , wherein
simulating the reception events at the other nodes includes:
generating background-traffic events at the other nodes at a simulation time that is prior to a scheduled time of arrival of the explicit-traffic packet, to determine the effect of the background-traffic packets on the explicit-traffic packet, and
terminating the generating of background-traffic events at the other nodes after the effect of the background-traffic packets on the explicit-traffic packet is determined.
23. The computer readable medium of claim 16 , wherein
simulating the reception events at the other nodes includes:
determining a state of each node based on flow information contained in the background-traffic packets received at the node, and
estimating parameters associated with the explicit-traffic packet, based on characteristics associated with the state of the node.
24. The computer readable medium of claim 16 , wherein
each background-traffic packet has an associated duration period, and
determining the state of each node based on the flow information is dependent upon the duration period.