CN108513316B - Wireless network protocol performance modeling method - Google Patents
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- CN108513316B CN108513316B CN201810316356.4A CN201810316356A CN108513316B CN 108513316 B CN108513316 B CN 108513316B CN 201810316356 A CN201810316356 A CN 201810316356A CN 108513316 B CN108513316 B CN 108513316B
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Abstract
The invention discloses a wireless network protocol performance modeling method, which comprises the steps of collecting transmission data, and converting packets received in a link into a binary sequence; and decomposing the packets on the link with the time length of T into a plurality of segments, decomposing each segment into d sets containing w packets, and obtaining the expected transmission times aE on the anycast link and the expected transmission times bE on the broadcast link of each set as the aE and bE of the original sequence. The invention relates to a wireless network protocol performance modeling method, which repeatedly adjusts parameters to ensure that a model reaches the best, compares a model generation data packet with an original data packet according to the quality of a wireless network and the performance of a link level, checks the feasibility of the model, adjusts the values of d and w if the difference between the model and the original data packet is too far, ensures the accuracy while fully considering the packet receiving rate, the time correlation and the correlation of a spatial link, effectively reduces the error generated by the existing model simulation, and provides the accuracy of a simulation result.
Description
Technical Field
The invention relates to the field of wireless communication research, in particular to a wireless network protocol performance modeling method.
Background
In the field of communications, wireless network protocol modeling is a very important method for protocol design and performance testing. The markov model is widely used for simulating network communication, but the model must be simulated under the condition of no transmission error, the gilbert model is a model which can be used for simulating transmission distortion probability, and the hidden markov model can be used for simulating a burst link by controlling the transmission duration and simulating a network communication protocol.
With the expected transmission times (E) as the performance index of the protocol, the expected transmission times can be divided into three types according to the difference of the protocol: unicast expected transmission times (uE), anycast expected transmission times (aE), and broadcast expected transmission times (bE). In the existing research, a one-dimensional model and a two-dimensional model are established for simulating the performance of a protocol, wherein the one-dimensional model takes a packet receiving rate PRR as a model transmission performance index, and the packet receiving rate PRR captures long-term link behaviors so that some short-term uE cannot be captured, and therefore the one-dimensional model does not meet the requirements. The two-dimensional model considers the packet receiving rate PRR and the time distribution on the link into the model at the same time, the two-dimensional model can capture uE basically, but when the two-dimensional model captures aE and bE, the two-dimensional model cannot capture aE and bE because the correlation of the link on the space has great influence on anycast and broadcast links. The existing communication simulation model cannot fully consider the packet receiving rate, the time correlation and the spatial link correlation, so that the simulation result is not accurate enough.
Disclosure of Invention
The invention aims to solve the technical problem that the existing communication simulation model cannot fully consider the packet receiving rate, the time correlation and the spatial link correlation, so that the simulation result is not accurate enough, and the invention aims to provide a wireless network protocol performance modeling method to solve the problems.
The invention is realized by the following technical scheme:
a wireless network protocol performance modeling method comprises the following steps: s1: collecting transmission data, and converting packets received in a link into a binary sequence; s2: dividing the received transmission data packet into a plurality of segments, decomposing the packet of each segment into d sets containing w packets, and obtaining the expected transmission times aE on an anycast link and the expected transmission times bE on a broadcast link of each set as the aE and the bE of an original sequence; s3: obtaining the packet receiving rate PRR of each set according to the d sets and the w packets in the sets in the S2, and forming PRR tuples by the packet receiving rates PRR of the d sets; s4: obtaining a new sequence according to the PRR tuple of S3, and obtaining aE and bE of the new sequence; s5: comparing aE of the original sequence with aE of the new sequence, and comparing bE of the original sequence with bE of the new sequence; if the difference between aE of the original sequence and the new sequence is smaller than a difference threshold value and the difference between bE of the original sequence and the new sequence is smaller than the difference threshold value, the new sequence is considered to bE a reasonable model, otherwise, the new sequence is considered to bE an unreasonable model; s6: if the new sequence is an unreasonable model, the values of d and w are modified and S2, S3, S4, S5 and S6 are performed in sequence.
In the prior art, the correlation of a spatial uplink has a great influence on an anycast link and a broadcast link, so that a two-dimensional model cannot capture aE and bE, and an existing communication simulation model cannot fully consider the packet receiving rate, the time correlation and the correlation of the spatial link, so that the simulation result is not accurate enough.
When the invention is applied, transmission data is collected, and packets received in a link are converted into a binary sequence; dividing the received transmission data packet into a plurality of segments, decomposing each segment of packet into d sets containing w packets, and obtaining the expected transmission times aE on the anycast link and the expected transmission times bE on the broadcast link of each set as the aE and bE of the original sequence; then, according to the d sets and the w packets in the sets in the S2, obtaining the packet receiving rate PRR of each set, and forming PRR tuples by the packet receiving rates PRR of the d sets; thus, three factors of packet receiving rate, link time correlation and space correlation can be considered, the packets on a link in a period of time are decomposed into d sets containing w packets, and the packet receiving rate PRR is calculated to obtain the PRR tuple. The method comprises the steps of receiving a period of packets on a link, wherein the receiving time can be set by self, defining the packets received in the period as a large set, and then decomposing the large set into d small sets with the same short time, wherein each set comprises w packets. The probability rates (PRRs) of received packets within w packets are computed so that each large set can compute d PRRs, which are grouped into a PRR tuple. Obtaining a new sequence according to the PRR tuple of S3, and obtaining aE and bE of the new sequence; then comparing the aE of the original sequence with the aE of the new sequence, and comparing the bE of the original sequence with the bE of the new sequence; if the difference between aE of the original sequence and the new sequence is smaller than a difference threshold value and the difference between bE of the original sequence and the new sequence is smaller than the difference threshold value, the new sequence is considered to bE a reasonable model, otherwise, the new sequence is considered to bE an unreasonable model; if the new sequence is an unreasonable model, modifying the values of d and w, and sequentially executing S2, S3, S4, S5 and S6 until the new sequence is a reasonable model; therefore, the accuracy of the invention in the using process can be ensured. Through the steps, the accuracy is ensured while the packet receiving rate, the time correlation and the correlation of the spatial link are fully considered, the error generated by the existing model simulation is effectively reduced, and the accuracy of the simulation result is provided.
Further, 1 in the binary sequence in step S1 represents the reception of a packet, and 0 represents the loss of a packet.
Further, aE in step S2 represents the expected number of transmissions of the packet received by at least one receiving node.
Further, the aE is derived by the following formula:
in the formula, p (0) is the probability that all receiving nodes have not received a packet.
Further, the bE in step S2 indicates the expected number of transmissions that all receiving nodes received the packet.
Further, the bE is given by:
in the equation, P (X ═ k) is the probability that all receiving nodes have received a packet after transmitting k times.
Further, the P (X ═ k) is obtained by the following formula:
P(X＝k)＝P(X>k-1)-P(X>k)；
wherein P (X > k) is the probability that at least one node has not received a packet after k transmissions; p (X > k-1) is the probability that at least one node has not received a packet after k-1 transmissions.
Further, the P (X > k) is derived by the following formula:
in the formula (I), the compound is shown in the specification,
the probability that m nodes do not receive a packet after k times of transmission; smContains m lost packets for a set of packets;
is the probability that m packets are not received.
When the method is applied, in order to more effectively improve the accuracy of the model, the inventor divides the value of bE into k grades, the accuracy of the model is improved but the memory used by the model is increased along with the improvement of the k value, the accuracy of the model is reduced but the memory used by the model is reduced along with the reduction of the k value, and generally, the value of the k value can bE 3-8 so as to meet the requirements of the accuracy and the memory.
Further, in step S5, the difference threshold is 15% to 25%.
When the method is applied, in order to accurately distinguish the difference between the new sequence and the original sequence, the difference threshold value is selected to be 15-25%, so that the difference between the new sequence and the original sequence can be accurately and effectively embodied.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention relates to a wireless network protocol performance modeling method, which repeatedly adjusts parameters to ensure that a model reaches the best, compares a model generation data packet with an original data packet according to the quality of a wireless network and the performance of a link level, checks the feasibility of the model, adjusts the values of d and w if the difference between the model and the original data packet is too far, ensures the accuracy while fully considering the packet receiving rate, the time correlation and the correlation of a spatial link, effectively reduces the error generated by the simulation of the existing model, and provides the accuracy of a simulation result;
2. according to the wireless network protocol performance modeling method, the value of bE is divided into k levels, the accuracy of the model is improved but the memory used by the model is increased along with the improvement of the k value, so that the accuracy of the model is effectively improved and the model is more flexible to use.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic diagram of the steps of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
As shown in fig. 1, the invention relates to a wireless network protocol performance modeling method, which comprises the following steps: s1: collecting transmission data, and converting packets received in a link into a binary sequence; s2: dividing the received transmission data packet into a plurality of segments, decomposing the packet of each segment into d sets containing w packets, and obtaining the expected transmission times aE on an anycast link and the expected transmission times bE on a broadcast link of each set as the aE and the bE of an original sequence; s3: obtaining the packet receiving rate PRR of each set according to the d sets and the w packets in the sets in the S2, and forming PRR tuples by the packet receiving rates PRR of the d sets; s4: obtaining a new sequence according to the PRR tuple of S3, and obtaining aE and bE of the new sequence; s5: comparing aE of the original sequence with aE of the new sequence, and comparing bE of the original sequence with bE of the new sequence; if the difference between aE of the original sequence and the new sequence is smaller than a difference threshold value and the difference between bE of the original sequence and the new sequence is smaller than the difference threshold value, the new sequence is considered to bE a reasonable model, otherwise, the new sequence is considered to bE an unreasonable model; s6: if the new sequence is an unreasonable model, the values of d and w are modified and S2, S3, S4, S5 and S6 are performed in sequence.
In the implementation of this embodiment, transmission data is collected, and packets received in a link are converted into a binary sequence; dividing the received transmission data packet into a plurality of segments, and dividing each segment of packets into d sets containing w packets, for example, 1000 received packets, if d is selected to be 4 and w is selected to be 5, the number of each segment of packets is 20, and at this time, the packets need to be divided into 50 segments; and obtaining the expected transmission times aE on the anycast link and the expected transmission times bE on the broadcast link of each set as aE and bE of the original sequence; then, according to the d sets and the w packets in the sets in the S2, obtaining the packet receiving rate PRR of each set, and forming PRR tuples by the packet receiving rates PRR of the d sets; thus, three factors of packet receiving rate, link time correlation and space correlation can be considered. The method comprises the steps of receiving packets for a period of time on a link, wherein the receiving time can be set by a user, defining the packets received for the period of time as a large set, dividing each large set into a plurality of segments, and then decomposing each segment into d small sets with the same short time, wherein each small set comprises w packets. The probability rates (PRRs) of received packets within w packets are computed so that each large set can compute d PRRs, which are grouped into a PRR tuple. Obtaining a new sequence according to the PRR tuple of S3, and obtaining aE and bE of the new sequence; then comparing the aE of the original sequence with the aE of the new sequence, and comparing the bE of the original sequence with the bE of the new sequence; if the difference between aE of the original sequence and the new sequence is smaller than a difference threshold value and the difference between bE of the original sequence and the new sequence is smaller than the difference threshold value, the new sequence is considered to bE a reasonable model, otherwise, the new sequence is considered to bE an unreasonable model; if the new sequence is an unreasonable model, modifying the values of d and w, and sequentially executing S2, S3, S4, S5 and S6 until the new sequence is a reasonable model; therefore, the accuracy of the invention in the using process can be ensured. Through the steps, the accuracy is ensured while the packet receiving rate, the time correlation and the correlation of the spatial link are fully considered, the error generated by the conventional model simulation is effectively reduced, and the accuracy of the model simulation result is provided.
Example 2
In this embodiment, on the basis of embodiment 1, the difference threshold in step S5 is 15% to 25%.
In the implementation of this embodiment, in order to accurately distinguish the difference between the new sequence and the original sequence, the difference threshold is selected to be 15% to 25%, which can accurately and effectively represent the difference between the new sequence and the original sequence, and the value range can be further optimized to 20%.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A wireless network protocol performance modeling method is characterized by comprising the following steps:
s1: collecting transmission data, and converting packets received in a link into a binary sequence;
s2: dividing the received transmission data packet into a plurality of segments, decomposing the packet of each segment into d sets containing w packets, and obtaining the expected transmission times aE on an anycast link and the expected transmission times bE on a broadcast link of each set as the aE and the bE of an original sequence;
s3: obtaining the packet receiving rate PRR of each set according to the d sets and the w packets in the sets in the S2, and forming PRR tuples by the packet receiving rates PRR of the d sets;
s4: obtaining a new sequence according to the PRR tuple of S3, and obtaining aE and bE of the new sequence;
s5: comparing aE of the original sequence with aE of the new sequence, and comparing bE of the original sequence with bE of the new sequence; if the difference between aE of the original sequence and the new sequence is smaller than a difference threshold value and the difference between bE of the original sequence and the new sequence is smaller than the difference threshold value, the new sequence is considered to bE a reasonable model, otherwise, the new sequence is considered to bE an unreasonable model;
s6: if the new sequence is an unreasonable model, the values of d and w are modified and S2, S3, S4, S5 and S6 are performed in sequence.
2. The method of claim 1, wherein 1 in the binary sequence in step S1 represents the reception of a packet, and 0 represents the loss of a packet.
3. The method of claim 1, wherein the aE in step S2 represents an expected number of transmissions of the packet received by at least one receiving node.
4. The method of claim 3, wherein the aE is derived by the following equation:
in the formula, p (0) is the probability that all receiving nodes have not received a packet.
5. The method of claim 1 wherein the bE indicates the expected number of transmissions that all receiving nodes received the packet in step S2.
6. The method of claim 5, wherein the bE is derived by:
in the equation, P (X ═ k) is the probability that all receiving nodes have received a packet after transmitting k times.
7. The method of claim 6, wherein P (X ═ k) is given by:
P(X＝k)＝P(X>k-1)-P(X>k)；
wherein P (X > k) is the probability that at least one node has not received a packet after k transmissions; p (X > k-1) is the probability that at least one node has not received a packet after k-1 transmissions.
8. The method of claim 7, wherein P (X > k) is obtained by:
in the formula (I), the compound is shown in the specification,
the probability that m nodes do not receive a packet after k times of transmission; smContains m lost packets for a set of packets;
is the probability that the number of lost packets is m.
9. The method of claim 1, wherein the difference threshold at step S5 is 15-25%.
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| CN102986186A (en) * | 2011-06-23 | 2013-03-20 | 华为技术有限公司 | Method for terminal network element registration, terminal network element and router |
| CN104796288A (en) * | 2015-04-08 | 2015-07-22 | 广东睿江科技有限公司 | Anycast communication method and device |
| CN106507489A (en) * | 2016-12-30 | 2017-03-15 | 宇龙计算机通信科技(深圳)有限公司 | A kind of resource allocation methods, and access device |
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| CN102986186A (en) * | 2011-06-23 | 2013-03-20 | 华为技术有限公司 | Method for terminal network element registration, terminal network element and router |
| CN104796288A (en) * | 2015-04-08 | 2015-07-22 | 广东睿江科技有限公司 | Anycast communication method and device |
| CN106507489A (en) * | 2016-12-30 | 2017-03-15 | 宇龙计算机通信科技(深圳)有限公司 | A kind of resource allocation methods, and access device |
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