WO2016122138A1  Backoff counter selection device for random channel access and method therefor  Google Patents
Backoff counter selection device for random channel access and method therefor Download PDFInfo
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 WO2016122138A1 WO2016122138A1 PCT/KR2016/000255 KR2016000255W WO2016122138A1 WO 2016122138 A1 WO2016122138 A1 WO 2016122138A1 KR 2016000255 W KR2016000255 W KR 2016000255W WO 2016122138 A1 WO2016122138 A1 WO 2016122138A1
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 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04W—WIRELESS COMMUNICATION NETWORKS
 H04W28/00—Network traffic or resource management
 H04W28/02—Traffic management, e.g. flow control or congestion control
 H04W28/04—Error control

 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04W—WIRELESS COMMUNICATION NETWORKS
 H04W74/00—Wireless channel access, e.g. scheduled or random access

 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04W—WIRELESS COMMUNICATION NETWORKS
 H04W74/00—Wireless channel access, e.g. scheduled or random access
 H04W74/08—Nonscheduled or contention based access, e.g. random access, ALOHA, CSMA [Carrier Sense Multiple Access]
Abstract
Description
The invention hundred of random channel access  in consideration of, more particularly, to network state information in a wireless network system using a random backoff (Backoff) the counter (counter) that relates to a counter selection apparatus and method for offbackoff to a technique for selecting the counter value.
All wireless devices based on the IEEE802.11 wireless LAN system, access to the wireless medium into a contentionbased in order to transmit a data packet, and then ends the data transmission of another radio Distributed Inter Frame Space (DIFS), wait for the time contention window (Contention Widow: CW) and randomly determines the transmission time to the selected counter value in the interval.
Random backoff value in a medium access method of a conventional wireless LAN system is obtained from a random variable with a uniform distribution (Uniform Distribution). At this time, each of the wireless LAN device increases the range of the uniform distribution depending on the result of the collision after the transmission data set in the initial range of the first uniform distribution to a minimum value (CW _{0).}
This distribution range to lower the probability of collision by assuming a network larger number of wireless devices depending on the radio access the first attempt without using the current status information of the network setting to a minimum a distribution range in the assumption that the number of radio equipment smaller and conflict results increase is due to.
Therefore, if a large number of the wireless device initially exist becomes the transmission success probability of the plurality of wireless devices decreases, initially small distribution range of high probability of collision due to, had the largest range of uniformly distributed increased by the collision is the wireless device has a transmission delay time by a large backoff value can be increased as compared to the other wireless device a random backoff value having a relatively small contention window size.
Thus, this case is increased, the average transmission rate and packet transmission delay time of each terminal can be a cause of the overall system performance or cause to lower the fair channel access opportunity of each wireless device.
As such wireless LAN, so increases in accordance with the contention window size of each of the wireless devices in the conflict results from the minimum value can reduce the collision probability in a certain moment, but the short term fairness for some wireless devices which increase the contention window according to the collision (Short Term Fairness; STF) can cause problems.
That is, since part of the transmission of the radio equipment is collision to increase the contention window and select a random backoff value from random back contention window of a wireless device without a transmission collisions are relatively small size when selecting a value in the off counter constant during the period it may occur if you do not occupy the channel.
Embodiment of the present invention is to provide a transmission rate and shortterm fairness (Short Term Fairness) to select a random backoff counter to improve the device and method according to the conflicts between wireless devices in a dense environment.
Is not limited to this aspect are the technical problems referred to above of the invention, still another aspect are not mentioned will be clearly understood by those skilled in the art from the following description.
Random backoff counter selection device according to the embodiment of the present invention includes a step of obtaining the network state information in a wireless network system; To apply the network status information to the distribution function may include the step of calculating the random backoff counter value.
The network status information, the data transmission probability, the network number of the wireless device, each packet transmission collision time average value according to the broadcast of the other wireless devices, any wireless device is also at least one or more of the average amount of time not occupied by the channel (Idle Time) It can be included.
Calculating the random backoff counter value is, it is possible to use the binomial distribution function, or a Poisson distribution function as a distribution function.
In addition, the method for calculating the random backoff counter value may be, to set a value obtained by adding 1 to the random value calculated using the binomial distribution function, or a Poisson distribution function to back off counter value.
Further, the step of obtaining the network state information can be transmitted using a packet collision time average value according to the broadcast of the network can be a wireless device and a different wireless device, to calculate the probability of the data transmission.
Further, the step of obtaining the network state information can be calculated, the data transmission probability by using the Equation (1) or (2) below.
Equation 1
Equation (2)
Here, N is the number of network radio, τ * is the optimal data transmission probability,
Is the average packet transmission time to collision, Nτ is the average bit rate.A value of the equation (1) or Equation (2) are 0 and 1, is minimized can be calculated as a probability of the data transmission.
In addition, the method for calculating the random backoff counter value is, it is possible to calculate the average value of the random backoff value using the data transmission probability.
In addition, the method for calculating the random backoff counter value is
[Equation 3]
(Where μ is the mean value of the random backoff value, τ also means the optimal data transmission probability)
Below the average value of the random backoff values can be calculated using the equation (3).
In addition, the method for calculating the random backoff counter value, may be defined as the binomial distribution function, and Equation (4) below.
[Equation 4]
(Wherein, α, β, γ are the binomial distribution parameter Im)
Binomial distribution parameter α is defined by the equation (5) below.
[Equation 5]
(Where N is the number of network radio, μ is the mean value of the random backoff value)
Binomial distribution parameter β may be defined using equation (6) below.
[Equation 6]
(Where, α is a binomial distribution parameter, μ is the mean value of the random backoff values)
Binomial distribution parameter γ is defined by the equation (7) below.
[Equation 7]
(Wherein, α, β are the binomial distribution parameter, μ is the mean value of the random backoff value)
Calculating the random backoff counter value, it may be defined by the Poisson distribution function and equation (8) below.
[Equation 8]
(Where, e is Euler's number (natural constant), λ is a Poisson distribution of the average value, μ is the mean value of the random backoff value)
In addition, the Poisson distribution of the average value λ can be calculated using the equation (9) below.
[Equation 9]
(Here, the τ refers to the data transmission probability)
Backoff counter selection device of a random access channel according to an embodiment of the present invention, network status information acquisition unit for acquiring the network status information in a wireless network system; And it may include a control unit for calculating the random backoff counter value by applying the network status information to the distribution function.
Acquiring the network state information comprises: data transmission probability, the number of network radio, each broadcast packet transmission collision time mean value by the other wireless devices, any wireless device is also at least one of the average amount of time not occupied by the channel (Idle Time) It can be obtained later.
The controller may calculate the random value using the binomial distribution function, or a Poisson distribution function for the network state information.
Wherein the control unit may set a value obtained by adding 1 to the random value calculated using the binomial distribution function, or a Poisson distribution function to back off counter value.
The network status information obtaining unit uses the data transmission probability can be used to calculate the packet transmission time to collision mean value.
The techniques can be secured up to the yield (Throughput) and shortterm fairness (Short term fairness), it is possible existing uniform distributionbased homogeneous or heterogeneous system and the coexistence of the effect.
1 is a configuration diagram of the backoff counter selection device of a random access channel according to an embodiment of the present invention.
Figure 2 is a flow chart illustrating a backoff counter select in the random access channel according to an embodiment of the present invention.
Figure 3 is a flow chart illustrating a method of determining the binomial distribution characteristics and binomial parameters according to an embodiment of the invention.
Figure 4 is a flow chart illustrating a parameter calculation method of the binomial distribution according to an embodiment of the invention.
Figure 5 is a flow chart illustrating a method for determining a characteristic and average value of the Poisson distribution according to an embodiment of the invention.
Figure 6 is a flow chart illustrating a method of calculating the average of a Poisson distribution according to an embodiment of the invention.
7 is a flow chart illustrating a method of calculating a data transmission probability (τ) in the binomial distribution, and Poisson distribution determination process according to an embodiment of the present invention.
8 is a configuration diagram of the computer system also applies the backoff counter select in the random access channel according to an embodiment of the present invention.
It will be described in detail below through illustrative drawings of some embodiments of the invention. In addition as the reference numerals in the respective drawings of the component, as to the same elements even though shown in different drawings It should be noted that and to have the same reference numerals as much as possible. Further, in the following description of an embodiment of the present invention, a detailed description of known functions and configurations, which we determined to hinder the understanding of the embodiments of the present invention, the detailed description thereof will be omitted.
In explaining the example component embodiment of the present invention, first, it is possible to use a & quot; and the like 2, A, B, (a), (b). These terms do not include the nature and order or sequence of the components is not limited by may make that term to distinguish that component and other components. Further, a, including technical and scientific terms, all terms used herein that are not otherwise defined shall have the same meaning as commonly understood by one of ordinary skill in the art. Such terms as those defined in a generally used dictionary are to be interpreted as having the same meaning in the context of the relevant art, unless expressly defined in this application, be interpreted to have an idealistic or excessively formalistic meaning no.
Embodiment of the invention proposes a new distribution function and the distribution function characteristics determination method for a random backoff selected to correct the transmission rate decrease and the short term fairness (Short Term Fairness) problem caused by the collision between the wireless devices in a dense environment .
The present invention uses a binomial function (Binomial Distribution) and the Poisson distribution function to select the distribution function. In particular, the data transmission probability (τ), the wireless network device number (N), each average value of the packet transfer time to collision caused by simultaneous transmission of different radio equipment (
) Discloses a method to derive the binomial distribution function, and from the Poisson distribution function.
Hereinafter, with reference to FIG. 1 to FIG. 8, it will be described in detail the embodiments of the invention.
1 is a configuration of the random access channel (random access channel) backoff counter (backoff counter) of the selection device according to an embodiment of the present invention.
Backoff counter selector 110 of the random access channel in accordance with an embodiment of the present invention includes a network status information obtaining unit 110, a storage unit 120, the control unit 130, the output buffer 140.
Network status information obtaining unit 110 obtains the network status information from the wireless network system. At this time, the network state information is data transmission probability (τ), the number of radio network (N), the average packet transmission time to collision with each other due to the simultaneous transmission of other radio equipment (
), The average time (Idle Time), any wireless device not occupied by the channels ( ) A.Network status information obtaining unit 110 packet transmission collision time average value according to the broadcast of a number of radio network (N) and a different wireless device (
) Was used to calculate the probability of the data transmission (τ) shown in the specific mathematical expression equation 1 and equation 2 below. However, the data transmission probability (τ) may be obtained through measurement.
In Equation 1 and Equation 2, N is the number of network radio, τ * is the optimal data transmission probability,
Is the average packet transmission time to collision, Nτ is the average bit rate.Network status information obtaining unit 110 and calculates a value between 0 and 1, the said equation (1) or Equation (2) is minimum in the data transmission probability.
In addition, the network status information obtaining unit 110 may calculate an average value (m) of the random backoff value using the data transmission probability (τ). That is, the network status information obtaining unit 110 calculates an average value (m) of the random backoff value using the equation (3) below.
Where μ is the mean value of the random backoff value, τ refers to the optimal data transmission probability.
The storage unit 120 stores the random value calculated through the network state information and the control unit 130 obtained from the network status information obtaining unit 110.
The control unit 130 calculates the random value to apply the network status information to the binomial distribution function, or a Poisson distribution function, and by adding 1 to the random value calculating a random backoff counter value. At this time, the controller 130 selects the one to select the distribution function of the binomial distribution function and the Poisson distribution function according to the situation of the radio network or wireless device. At this time, the controller 130 selects one side is easy to generate a random number of the Poisson distribution and the transposition according to the hardware performance.
Binomial distribution function can be defined as shown in Equation (4) below.
Here, α, β, γ indicates a probability as being the binomial distribution parameter, α will not attempt to transmit all of the other devices other than any device of the N radio equipment, and b is making a binomial distribution that the average of this each represents the number of other modifications binomial distribution (modified binomial distribution) for, and the value is determined as a positive integer in the range below. g denotes a constant value which is used to modify the binomial distribution, the average of binomial random variable X such that μ.
The control unit 130 may be defined by using Equation (5) below, the binomial distribution parameter α.
The control unit 130 may be defined using equation (6) below, the binomial distribution parameter β.
The control unit 130 may be defined using Equation (7) below, the binomial distribution parameter γ.
On the other hand, the Poisson distribution function can be defined as Equation (8) below.
Here, e is Euler's number (natural constant), λ is a Poisson distribution of the average value, μ is the average value of the random backoff value.
The control unit 130 can be calculated using the equation (9) below the mean value λ Poisson distribution.
Here, τ denotes the transmission probability data.
The control unit 130 when this way, adding 1 to the random value Y calculated by the distribution function back calculate the off counter value X, and the backoff is not the channel is occupied for the counter Xslot time the wireless device transmits data It is.
Backoff counter selectors in a random channel access according to the present invention having such a configuration, the maximum yield by determining an optimum random backoff counter selected distribution function, and selects a backoff counter value by using the optimum selected distribution function according to the network conditions and a short period of time it is possible to ensure fairness (short term fairness).
Hereinafter, also illustrating the selection of a random backoff counter channel access method according to an embodiment of the present invention with reference to FIG.
First, the network status information obtaining unit 110 obtains the network status information from the wireless network (S100). At this time, network status information, the data transmission probability (τ), number of network radio (N), packet transmission time to collision with each other due to the average value simultaneous transmission of another radio unit (
), The average time (Idle Time), any wireless device not occupied by the channels ( ) A.The control unit 130 selects one of the binomial distribution function and the Poisson distribution function a selected distribution function according to the situation of the radio network or wireless device (S200). At this time, the controller 130 selects one side is easy to generate a random number of the Poisson distribution and the transposition according to the hardware performance.
Then, the control unit 130 when the binomial distribution function is selected according to the selection of the process S200 when applying the binomial distribution function, by calculating a random value (S300), the Poisson distribution function is selected by applying a Poisson distribution function random value calculates (S400).
Then, the control section 130 sets a backoff counter value by adding 1 to a random value obtained by applying the binomial distribution function, or a Poisson distribution function (S500).
12. The following detailed description of a method for determining a binomial distribution characteristics and the binomial distribution parameters, according to an embodiment of the present invention will be described with reference to FIG.
Network status information obtaining unit 110 packet transmission collision time average value (
) It is determined that the known value (S301), if the value a known yen packet transmission collision time average value ( ) To be applied to the binomial distribution (S305).On the other hand, packet transmission collision time average value (
) In this case, known nonvalue, the network status information obtaining unit 110 is the average value of the packet transmission time to collision with each other due to the simultaneous transmission of other radio equipment ( ) The estimated (S302). Exemplary of the average value estimated for example, makes a judgment of collision when there is no ACK response for a signal of each terminal is transmitted to the average estimates the transmission time of the packet. In addition, the average value of the same packet transmission time to collision in a network in one embodiment of the estimated mean value ( ) (Such as a network management unit in order to apply the example, AP) in the average value of the packet transfer time to collision on the network co collects information from each mobile station ( ) Can determine the value to be passed to the respective nodes have.On the other hand, the network status information obtaining unit 110 is applied to the wireless device number (N) is judged that the known value (S303), if the value a known yen of network radio number (N) of the network to the binomial and (S305). The binomial distribution function is shown in Equation (4) described above.
If the wireless device number (N) of the network is not a known value, obtain network status information 110 is the average time (Idle Time) do not occupy the channel any wireless device (
) It can be estimated for the wireless device number (N) of the network using (S304).This is applicable to the wireless device number (N) of the estimated network to the binomial distribution function (S305).
At this time, the packet transmission collision time average value (
) And a wireless device number (N) of the network are used in calculating data transmission probability (τ) for the binomial distribution function is applied.
12. The following detailed description of a parameter calculation method of binomial distribution according to an embodiment of the present invention will be described with reference to FIG.
First, a network status information obtaining unit 110 calculates a data transmission probability (τ) (S311).
Then, to calculate the network status information obtaining unit 110 data transmission probability mean value (μ) of the random backoff value using the (τ) (S312). At this time, as expression by equation (3) described above to calculate the mean value (μ) of the random backoff value.
Thus, the control unit 130 calculates a formula for binomial distribution parameter α by using the mean value (μ) of the random backoff value (S313), calculating the binomial distribution parameter α shown in Equation (5).
Next, the control unit 130 is shown in Equation (6), the expression for calculating the binomial distribution parameter β by the binomial distribution parameter α and (S314) calculating the binomial distribution parameter β.
Then, the control unit 130 calculates a formula for binomial parameters α, β and γ binomial parameters used and (S315) calculating the binomial distribution parameter γ shown in Equation (7).
Will be described in the following, the method of FIG determines a mean value (μ) of the attribute and the random backoff value of the Poisson distribution according to an embodiment of the present invention to 5 in detail.
First, the control unit 130 binomial distribution similarly to the function is applied, the packet transmission collision time mean value (μ) and a wireless device number (N) of the network is to determine if the information already known (S401, 403), I do not know the information case the estimated (S402, S404).
Then, the packet transmission collision time average value (
) And a wireless device number (N) of the network, calculates a random value applied to a Poisson distribution function (S405). At this time, the Poisson distribution function can be defined as the abovedescribed equation (8).Hereinafter, FIG. 6 is a detailed description of a calculation average value (μ) of the Poisson distribution according to an embodiment of the present invention method.
First, a network status information obtaining unit 110 calculates a data transmission probability (τ) (S411).
Then, to calculate the network status information obtaining unit 110 data transmission probability mean value (μ) of the random backoff value using the (τ) (S412). At this time, as expression by equation (3) described above to calculate the mean value (μ) of the random backoff value.
12. The following describes how to calculate the degree binomial probability and data transfer required when the Poisson distribution function application (τ) according to an embodiment of the present invention with reference to the example 7 in detail.
Data transmission probability (τ) is a measure or the network N,
Values can be determined by Equation (1) or (2) used.Network status information obtaining unit 110 selects one of the formula (1) or Equation 2 (S421). In this case, the network status information obtaining unit 110 may be selected to be the calculation of the equation (1) and equation (2) rapidly.
After the network status information obtaining unit 110 is the average value of the packet transfer time to collision caused by simultaneous transmission of different wireless devices with each other and the wireless device number (N) of the network in Equation (1) or (2) (
) Is applied to calculate the optimal data transmission probability τ * a (S422, S423.)Then, the network status information obtaining unit 110 determines the optimum data transmission probability τ * to the data transmission probability (τ) (S424).
Thus, the present invention backbyoff determined by applying information about the state of a counter value determined at the network, to improve the average transmission rate of a terminal and minimizes packet transmission delay time, improving the performance of the communication system and gongpyeon a channel connection for each wireless device, It can be given the opportunity to improve fairness.
8 is a configuration diagram of the computer system also applies the backoff counter select in the random access channel according to an embodiment of the present invention.
8, the computing system 1000 includes at least one processor which is connected via a bus 1200, 1100, memory 1300, user interface input devices 1400, user interface output devices 1500, a storage 1600, and it may include a network interface 1700.
Processor 1100 may be a semiconductor device for executing the processing for the central processing unit (CPU) or a memory 1300 and / or instructions stored in the storage 1600. Memory 1300 and storage 1600 may include various types of volatile and nonvolatile storage media. For example, memory 1300 may include ROM (Read Only Memory) and RAM (Random Access Memory).
Thus, the steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module, or in a combination of the two to be executed by the processor 1100. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a storage medium (i.e., memory 1300 and / or storage 1600), such as a CDROM You may.
An exemplary storage medium is coupled to the processor 1100, the processor 1100 can read information from, the storage medium may write information to, the storage medium. In the alternative, the storage medium may be integral to the processor 1100. The processor and the storage medium may reside in an application specific integrated circuit (ASIC). The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
The above description is only to those described as the technical idea of the present invention by way of example, those skilled in the art that various modifications, additions and substitutions will be possible without departing from the essential characteristics of the present invention.
Accordingly, the disclosed invention embodiments is for illustrative and not intended to limit the technical idea of the present invention, not by such an embodiment is the technical scope of the present invention is not limited. The scope of protection of the invention is to be interpreted by the following claims, all spirits within a scope equivalent will be construed as included in the scope of the present invention.
Claims (20)
 Obtaining the network state information in a wireless network system;Calculating a random backoff counter value by applying the network status information to the distribution functionSelecting a backoff counter of the random access channel comprises a.
 The method according to claim 1,The network status information, the data transmission probability, the network number of the wireless device, each packet transmission collision time average value according to the broadcast of the other wireless devices, any wireless device is also at least one or more of the average amount of time not occupied by the channel (Idle Time) backoff counter selection method of the random access channel, comprising a step of including.
 The method according to claim 2,Calculating the random backoff counter value isBackoff counter selection method of the random access channel a random channel of the connection, characterized in that using the binomial distribution function, or a Poisson distribution function as a distribution function.
 The method according to claim 3,Calculating the random backoff counter value isThe binomial distribution function, or a Poisson distribution function of the random backoff counter selection device connected to the channel which is obtained by adding 1 to the calculated random value is set to a value wherein the backoff counter value using.
 The method according to claim 3,Obtaining the network state information,The network and different broadcast packet transmission backoff counter selectors in a collision time random access channel using the average value, characterized in that for calculating the probability of the data transmission by the wireless device of the wireless device.
 The method according to claim 3,Obtaining the network state information,Equation (1) or (2) to the backoff counter selection method of the random access channel, characterized in that for calculating a probability of data transmission using a down.Equation 1Equation (2)
 The method according to claim 6,Equation (1) or Equation (2), the backoff counter method of selecting a value between 0 and 1 that is at least the random access channel, characterized in that for calculating the data to the transmission probability.
 The method according to claim 4,Calculating the random backoff counter value isThe data transmission probability and the back off counter selection method of the random access channel, characterized in that for calculating the mean value of the random backoff value using the.
 The method according to claim 8,Calculating the random backoff counter value is[Equation 3](Where μ is the mean value of the random backoff value, τ also means the optimal data transmission probability)Backoff counter selection method of the random access channel, characterized in that calculated using the equation (3) below the mean value of the random backoff value.
 The method according to claim 3,Calculating the random backoff counter value isBackoff counter selection method of the random access channel, characterized in that defined as the binomial distribution function, and Equation (4) below.[Equation 4](Wherein, α, β, γ are the binomial distribution parameter Im)
 The method according to claim 10,Binomial distribution parameter γ is how backoff counter select in the random access channel, characterized in that defining by using Equation 7 below.[Equation 7](Wherein, αβ is the binomial distribution parameter, μ is the mean value of the random backoff value)
 The method according to claim 3,Calculating the random backoff counter value isSelecting a backoff counter of the random access channel wherein the defining, as the Poisson distribution function and equation (8) below.[Equation 8](Where, e is Euler's number (natural constant), λ is a Poisson distribution of the average value, the average value μ of the random backoff value)
 Network status information acquisition unit for acquiring the network status information in a wireless network system; AndA controller for calculating the random backoff counter value by applying the network status information to the distribution functionBackoff counter selection device of a random access channel comprising: a.
 The method according to claim 16,Acquiring the network state information comprises:Characterized in that for obtaining a data transmission probability, the number of network radio, different broadcast packet transmission collision time average value according to the radio equipment, any wireless device also mean the time not occupied by the channel (Idle Time) of at least one backoff counter selection device of the random access channel to.
 The method according to claim 17,Wherein,Backoff counter selection device of the random access channel, characterized in that for calculating a random value using the binomial distribution function, or a Poisson distribution function for the network state information.
 The method according to claim 18,Wherein,The binomial distribution function, or a Poisson distribution function of the random backoff counter selection device connected to the channel which is obtained by adding 1 to the calculated random value is set to a value wherein the backoff counter value using.
 The method according to claim 18,Acquiring the network state information comprises:Backoff counter selection device of the random access channel, characterized in that to calculate the packet transmission collision time average value using the data transmission probability.
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2016
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EP2131535A1 (en) *  20010102  20091209  AT & T Corp.  random medium access methods with backoff adaptation to traffic 
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