Method for evaluating lifetime of dynamic operation cellular network, computer device and medium

Abstract

The application discloses a life evaluation method of a dynamic operation cellular network, a computer device and a medium, and the method comprises the following steps: S1, setting an initial value; S2, obtaining a first life distribution parameter and a second life distribution parameter of each base station; S3, obtaining a jth fault interval time τ i,j of an ith base station; S4, obtaining the working states of all the base stations at different time points t i ; S5, obtaining the ratio δ T of the network coverage at each time point to the initial coverage based on the working states of all the base stations at different time points; S6, comparing the ratio δ T with a specified value P L , and judging whether the life time is reached; if δ T ≤ P L , a life value T is obtained, k=k+1, and the step S7 is entered; if δ T >P L , j=j+1, and the step S3 is entered; S7, comparing the cycle number k of the life value T with a cycle number n, and judging: if k

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cellular network design, performance evaluation and operation management. More particularly, it relates to a method for evaluating the life of a dynamically operated cellular network, a computer device and a medium. BACKGROUND

[0002] A cellular network, also known as a mobile network, is a kind of mobile communication hardware architecture. Common types of cellular networks include GSM networks, CDMA networks, 3G networks, FDMA networks, etc. The composition of a cellular network includes mobile stations and base stations. Mobile stations refer to network terminal devices, such as mobile phones or some cellular industrial control devices. Base stations include mobile base stations, wireless transceiver devices, optical fibers, etc.

[0003] Life is one of the important indicators for measuring the effectiveness of a cellular network. The definition of the life of a cellular network generally refers to the time during which the service capability of the cellular network is not lower than a certain specified value. The specified value generally depends on industry standards, user requirements, etc. In previous literature, discussions about the service capability of a cellular network mainly focused on two directions. One is that the service capability of a cellular network depends on the coverage rate of base stations in a certain area within the network. The higher the coverage rate, the higher the service capability. The other is that the improvement of the special features of a cellular network, such as the improvement of the packet loss rate, signal-to-noise ratio and network delay performance, etc. Both of the above aspects consider the static performance of a cellular network, i.e., the service capability possessed by the network at the initial stage of network construction, without considering that such capability will change over time due to the aging and failure of base stations and the resulting maintenance problems.

[0004] During the operation of a cellular network, on the one hand, due to the aging and failure of base stations that constitute the network, the failure interval of each base station gradually decreases after maintenance, which macroscopically shows that the coverage rate of the cellular network presents a downward trend in the time dimension. On the other hand, due to the fact that the time of failure of each base station in the network is independent of each other, the coverage rate of the cellular network at different times has a certain randomness. For a cellular network in a dynamic operation state, the network coverage rate presents both degradation and randomness in the time dimension, making it difficult to give an effective life evaluation method for a dynamically operated cellular network in engineering. SUMMARY

[0005] One object of the present application is to provide a life evaluation method for a dynamically operated cellular network. The present application takes into account the failure and maintenance of base stations during the operation of a cellular network, and considers the time sequence of the coverage rate of a cellular network. The time during which the coverage rate of a cellular network is not lower than a certain specified value is taken as the life evaluation index. By combining the analytical method with the Monte Carlo method, the present application dynamically simulates the time sequence of the coverage rate of a repairable cellular network, and provides a life evaluation method for a dynamically operated cellular network.

[0006] To achieve the above object, the present application adopts the following technical solutions:

[0007] The first aspect of the present application provides a life evaluation method of a dynamic operation cellular network, the method comprising,

[0008] S1, setting an initial value, setting a base station timer t i , i∈[1-N], the failure interval order j of the base station, j=1, the cycle number k, k=1, turn to S2;

[0009] S2, obtaining the first life distribution parameter and the second life distribution parameter of each base station, wherein the first life distribution parameter of the i-th base station is β i , the second life parameter is η i ; i∈[1-N], N is the number of base stations, turn to S3;

[0010] S3, obtaining the j-th failure interval time τ i,j of the i-th base station, turn to S4;

[0011] S4, obtaining the working state of each base station at different time t i Turn to S5;

[0012] S5, obtaining the ratio δ T of the network coverage rate to the initial coverage rate in each time based on the working state of each base station at different time, turn to S6;

[0013] S6, comparing the ratio δ T with the specified value P L to determine whether the life time is reached: if δ T ≤P L , the life value T is obtained, k=k+1, turn to S7; if δ T >P L , j=j+1, turn to S3;

[0014] S7, comparing the cycle number k of the life value T with the cycle number n to determine: if k<n, turn to S1; if k≥n, obtaining the life confidence lower limit under the given confidence level.

[0015] Optionally, S2 further comprises establishing the life distribution function F i (t) of the base station,

[0016]

[0017] Optionally, S3 further comprises taking the inverse function of the life distribution function F i (t) of the base station to obtain the j-th failure interval time τ​i,j , i ∈ [1-N], j = 1, 2…

[0018]

[0019] where (β i , η i ) are the Weibull distribution parameters of the i-th base station; x i,j is a random number from a uniform distribution U(0, 1).

[0020] Optionally, the S4 further comprises that the working state is 1, indicating normal, and the working state is 0, indicating failure, and the S4 further comprises:

[0021] At t i = 1,

[0022] At t i > 1,

[0023] wherein, represents τ i,j downward rounding; tm represents the time required for the base station to recover normal operation after being repaired from the failure state, t i = t i + τ i,j + tm.

[0024] Optionally, the S5 further comprises obtaining the ratio δ T of the network coverage at each time to the initial coverage:

[0025]

[0026] Optionally, the obtaining of the life confidence lower limit under a given confidence level further comprises obtaining n groups of life values T, arranging T (1) ,T (2) ,…T (n) from large to small to obtain the life confidence lower limit value under a given confidence level.

[0027] The second aspect of the present application provides a computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to realize the method provided by the first aspect of the present application.

[0028] The third aspect of the present application provides a computer readable storage medium, which stores a computer program, wherein the program is executed by a processor to realize the method provided by the first aspect of the present application.

[0029] The present application has the following beneficial effects:

[0030] The present application is directed to a dynamic operation cellular network which is common in engineering practice and is difficult to handle, taking the fault interval time of each base station in the network as the starting point, and through the inverse function of the life distribution function of the base station, the operation state of each base station in the network is batch processed, and the calculation efficiency of the dynamic simulation cellular network is improved; the technical scheme provided by the present application has strong scalability, and considering that the two-parameter Weibull distribution used in the present application is an extended exponential distribution, the present application is also applicable to the exponential distribution, which is a generalization of the exponential distribution; the method proposed in the present application has clear calculation idea, simple steps, easy implementation, strong scalability, convenient for engineering application, and good practical value. BRIEF DESCRIPTION OF DRAWINGS

[0031] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0032] Figure 1 A flow chart of a life evaluation method of a dynamic operation cellular network is shown;

[0033] Figure 2 A relationship diagram of the number of cycles and the lower limit value of life confidence is shown;

[0034] Figure 3 A relationship diagram of the number of cycles and the number of assignments in each cycle is shown;

[0035] Figure 4 A structural schematic diagram of a computer system for implementing the life evaluation method of the dynamic operation cellular network provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0036] In order to more clearly illustrate the present application, the present application will be further described below with reference to the preferred embodiments and the accompanying drawings. Similar components are denoted by the same reference numerals in the drawings. Those skilled in the art should understand that the specific description below is illustrative rather than limiting, and should not limit the protection scope of the present application.

[0037] The present application fully considers the dynamic characteristics of performance degradation of the base station in the network after failure and maintenance, and proposes a life evaluation method, which is applicable to the related technical fields of cellular network design, performance evaluation and operation management, etc. The method flow chart of the life evaluation method of the dynamic operation cellular network proposed by the present application is shown in Figure 1 .

[0038] In one specific embodiment, taking a cellular network with N=30 base stations as an example, △t is taken as 1h, and 10 years of operation data, i.e. 8760h, are simulated, to further describe the present application in detail.

[0039] This invention proposes a method for assessing the lifetime of dynamically operated cellular networks, with the following specific steps:

[0040] Step 1: Set the base station timer t i , i∈[1-N], t i It is a natural number, the fault interval order j of the base station, the initial value of j is 1, the initial value of the number of cycles k is 1, and proceed to step two;

[0041] Step 2: Based on engineering experience or research and development test data, obtain N sets of Weibull distribution parameters for N base stations, namely (β1, η1), (β2, η2), ..., (β... i η i ), ... (β) N η N ), where the lifetime distribution function formula for N base stations is:

[0042]

[0043] The failure distribution of each base station follows a Weibull distribution, and its distribution parameter (β) i η i See Table 1:

[0044] Table 1 Failure distribution parameters for each base station

[0045]

[0046]

[0047] Step 3: Analyze the lifetime distribution function F i (t) Find the inverse function, and obtain the j-th fault interval τ of the i-th base station according to the following formula. i,j , i∈[1-N], j=1,2…

[0048]

[0049] Among them, (β) i η i Let be the Weibull distribution parameters of the i-th base station; x i,j Given random numbers from a uniform distribution U(0,1), proceed to step three;

[0050] Table 2 shows the fault intervals for some base stations:

[0051] Table 2 shows the fault interval τ of some base stations. i,j

[0052]

[0053] Step 4: Based on the fault interval time τ obtained in Step 3 i,j , the status of base station i at time t r is calculated, and the working status of the i-th base station at any time t is calculated. The working status i is 1 indicating normal, and the working status is 0 indicating a fault. At t = 1, wherein, denotes rounding τ i,j downward, tm represents the time required for the base station to resume normal operation after being repaired from the fault state, and t i = t i + τ i,j + tm; go to Step 5;

[0056] Step 5: Use the following formula to calculate the ratio of the network coverage rate within the time period from T = 1 to min(t i ) to the initial coverage rate;

[0057]

[0058] Step 6: Compare the ratio δ T obtained in Step 5 with the specified value P L of the network coverage rate. If δ T ≤ P L , obtain the network life value T, k = k + 1, and go to Step 7; if δ T > P L , j = j + 1, and go to Step 3;

[0059] Step 7: Compare and judge the loop count k of the life value T with the loop count n: If k < n, go to Step 1; if k ≥ n, obtain n sets of T (the value of n can be determined according to the actual data convergence situation), and arrange them in descending order to get T (1) , T (2) , … T (n) , and obtain the lower confidence limit of the life at a given confidence level, which is the lower confidence limit value of the life with a given confidence level of 70%.

[0060] In a specific embodiment, for the loop count n, Figure 2 shows the distribution of the network life values with a confidence level of 70% when n ranges from 1 to 700, and it can be seen that when the loop count n > 500, the life value ​​​​​​​​​Tend to be stable, so the step S3-S6 is repeated 500 times, 500 groups of P L , arranged in descending order to get the confidence of 70% life confidence lower limit value

[0061] According to the conventional simulation algorithm, the network is valued 30x8760=262800 times, Figure 3 Showed that the batch processing method according to the patent shown in the number of assignments required in each cycle of the network, calculated using the method of the patent will make the number of assignments to 30x320.76 times = 9622.8 times, the calculation efficiency is about 26 times the original.

[0062] In one embodiment, the technical scheme of the application relates to the characteristics of the cellular network as follows: (1) the cellular network is composed of N base stations built in the service area, the network coverage rate P0 under the condition that all the base stations are in normal operation; (2) the cellular network operation time sequence is composed of a plurality of consecutive basic time units△t, if all the N base stations are in normal operation state, the network has service capability in the time unit; if part of the base stations are out of order and temporarily unable to provide services for the area covered by them, the network coverage rate will decrease, and when the network coverage rate decreases to a specified value P L , that is, the life value T of the cellular network is reached; (3) considering the failure rate of the base station increases with time, and the strong expansion of Weibull distribution, the performance degradation of the base station in the network satisfies the two-parameter Weibull distribution, wherein β i (β i >1) is the shape parameter, η i is the scale parameter, i∈[1-N].

[0063] In one embodiment, the application provides a life evaluation method for a dynamic operation state cellular network, which fully considers the characteristics that the failure rate of the network basic unit, the base station, increases with time, starts from the base station failure interval time, dynamically simulates the performance of the cellular network at different times by using the combination of analytical method and Monte Carlo method, proposes a life evaluation method for the dynamic operation cellular network under a given confidence, and forms specific implementation steps. The steps are as follows: 1. Set the initial value; 2. Determine the life distribution parameters of each base station; 3. Obtain the failure interval of each base station by taking the inverse function of the life distribution function of the base station; 4. Obtain the operation state of each base station at different times in batches based on the failure interval; 5. Obtain the ratio of the network coverage rate at each time to the initial coverage rate based on the operation state of each base station at different times; 6. Compare the ratio with the specified value to determine whether the life value is reached; 7. Obtain the life confidence lower limit under a given confidence.

[0064] The application is directed to a dynamic operation cellular network which is common in engineering practice and is difficult to handle, and takes the fault interval time of each base station in the network as the breakthrough point, realizes batch processing of the operation state of each base station in the network, and improves the calculation efficiency of the dynamic simulation cellular network. The two-parameter Weibull distribution used in the application is an extended exponential distribution, which is a generalization of the exponential distribution. The method proposed in the application has clear calculation idea, simple steps and is easy to implement, and is convenient for engineering and technical personnel to apply, so it has good practical value.

[0065] As shown in Figure 4 The computer system suitable for realizing the life evaluation method of the dynamic operation cellular network provided by the above-mentioned embodiments includes a central processing module (CPU) which can perform various appropriate actions and processes according to the programs stored in the read-only memory (ROM) or the programs loaded from the storage part to the random access memory (RAM). In the RAM, various programs and data required for the operation of the computer system are also stored. The CPU, the ROM and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0066] The following components are connected to the I / O interface: an input part including a keyboard, a mouse and the like; an output part including a liquid crystal display (LCD) and the like, and a speaker and the like; a storage part including a hard disk and the like; and a communication part including a network interface card such as a LAN card, a modem and the like. The communication part performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. A removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory and the like is mounted on the drive as needed, so that the computer program read therefrom is installed in the storage part as needed.

[0067] In particular, according to the present embodiment, the process described in the above flowchart can be realized as a computer software program. For example, the present embodiment includes a computer program product including a computer program tangibly embodied on a computer readable medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from the removable medium.

[0068] The flowcharts and diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of the systems, methods and computer program products of the present embodiments. In this regard, each block in the flowcharts or diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the diagram and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0069] As another aspect, the present embodiments also provide a non-transitory computer storage medium, which can be the non-transitory computer storage medium contained in the apparatus in the above embodiments, or can exist separately from the terminal and not be assembled into the terminal. The non-transitory computer storage medium stores one or more programs, which, when executed by a device, cause the device to perform the following: S1, setting an initial value, setting a base station timer t i , i ∈ [1-N], the failure interval order j of the base station, j = 1, the number of cycles k, k = 1, and turning to S2; S2, obtaining the first lifetime distribution parameter and the second lifetime distribution parameter of each base station, wherein the first lifetime distribution parameter of the i-th base station is β i , and the second lifetime parameter is η i ; i ∈ [1-N], N is the number of base stations, and turning to S3; S3, obtaining the j-th failure interval time τ i,j of the i-th base station, and turning to S4; S4, obtaining the working states of the base stations at different times t i in batches and turning to S5; S5, obtaining the ratio δ T of the network coverage rate to the initial coverage rate at each time based on the working states of the base stations at different times, and turning to S6; S6, comparing the ratio δ T with a specified value P L , and determining whether the lifetime time is reached: if δ T ≤ P L , obtaining the lifetime value T, k = k + 1, and turning to S7; otherwise, j = j + 1, and turning to S3; S7, comparing the number of cycles k of the lifetime value T with the number of cycles n, and determining: if k < n, turning to S1; if k ≥ n, obtaining the lower limit of the lifetime confidence under a given confidence level.

[0070] In the description of the present application, it should be noted that the terms "upper", "lower", and the like are used to indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise expressly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0071] It should also be noted that in the description of the present application, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0072] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not limitations on the embodiments of the present application. For those of ordinary skill in the art, other different forms of changes or variations can be made on the basis of the above description, and it is impossible to enumerate all the embodiments here. Any obvious changes or variations derived from the technical solutions of the present application are still within the protection scope of the present application.

Claims

1. A method for assessing the lifetime of a dynamically operated cellular network, characterized in that, The method includes, S1. Set initial values, set base station timer. For i ∈ [1-N], the fault interval order of the base station j=1, loop count k, k=1; S2. Obtain the first lifetime distribution parameter and the second lifetime distribution parameter for each base station, where the first lifetime distribution parameter for the i-th base station is: The second lifetime parameter is i∈[1-N], where N is the number of base stations; S3. Obtain the j-th fault interval time of the i-th base station. ; S4. Batch acquisition of data from each base station at different times work status ; S5. Based on the operating status of each base station at different times, obtain the ratio of network coverage rate to initial coverage rate at each time point. ; S6, This ratio and specified value Compare and determine whether the lifespan has been reached: If Then the lifespan value T is obtained, k=k+1, and proceed to step S7; if , Proceed to step S3; S7. Compare and determine the number of cycles k and the number of cycles n for the lifespan value T: If Proceed to S1; if k≥n, obtain the lower confidence limit of lifetime at a given confidence level; S2 further includes establishing a lifetime distribution function for the base station. , ； S3 further includes a lifetime distribution function for the base station. Find the inverse function to obtain the j-th fault interval time of the i-th base station in batches. , i∈[1-N], in, Let be the Weibull distribution parameters of the i-th base station; For those from a uniform distribution Random numbers; S4 further includes the working state. A value of 1 indicates normal operation, which is the working status. A value of 0 indicates a fault, and S4 further includes: exist hour, ; exist hour, in, express Round down to the nearest integer. This indicates the time required for a base station to return to normal operation after repair from a faulty state. .

2. The method according to claim 1, characterized in that, S5 further includes obtaining the ratio of network coverage to initial coverage at each time point. : 。 3. The method according to claim 1, characterized in that, The step of obtaining the lower confidence limit of lifetime at a given confidence level further includes obtaining... n Group life value Arranged from largest to smallest To obtain the lower confidence limit of lifetime at a given confidence level.

4. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-3.

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