A discrete simulator for wireless channel attenuation in 5G non-orthogonal mobile communication networks

Abstract

The present invention provides a discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network, comprising the following steps: Step 1, establishing a first-order finite-state Markov chain according to the input of the simulator; Step 2, constructing a new Markov chain based on the first-order finite-state Markov chain obtained in Step 1 according to the number of users in a single user group in the set 5G non-orthogonal mobile communication network, and obtaining a state space and a transition probability matrix; Step 3, generating and outputting a simulation result according to the state space and transition probability matrix obtained in Step 2 and the length of a time series output by the simulator, thereby completing a discrete simulation of wireless channel attenuation in a 5G non-orthogonal mobile communication network. The present invention has the advantages of clear structure, strong adaptability, and high execution efficiency, and can better meet the needs of 5G network construction, operation, and maintenance and academic research, and has high practical value and generalizability.

Description

Technical Field

[0001] The present invention relates to a simulator for wireless channel attenuation, in particular to a discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network. Background Art

[0002] This section merely provides background information related to the present disclosure and is not necessarily prior art.

[0003] Compared to 4G networks, 5G networks serve more users, support higher speeds, enable unlimited connections, and offer personalized experiences, opening the door to the Internet of Everything. Wireless non-orthogonal multiple access (WNOMA) is a new multiple access technology in the physical layer of 5G mobile communications. It utilizes joint superposition coding at the transmitter and serial interference cancellation at the receiver to provide services to different users within the same resource block. In 5G non-orthogonal mobile communication networks, the data transmission rate of each user within the same user group is affected by the order of the wireless channel attenuation within the group.

[0004] Currently, both the practical construction and operation of 5G networks and theoretical research require single-user wireless channel attenuation simulators to support related work. Simulators based on random variables with discrete finite states are particularly popular. Specifically, a simulator automatically and continuously outputs a series of random states indicating the magnitude of wireless channel attenuation SNR based on the probability density function of the instantaneous signal-to-noise ratio (SNR) at the receiving end and a manually set number of N states.

[0005] Given the technical characteristics of wireless non-orthogonal multiple access (WNMA), a single traditional simulator cannot meet the wireless channel testing and simulation requirements of a single user in a 5G non-orthogonal mobile communication network. Therefore, multiple traditional simulators with the same number of users as the user group can be configured for parallel output. The output results can then be further compared, sorted, and calculated to determine the discrete state of the target user's wireless channel attenuation within the corresponding WNMA environment. While this approach is intuitive, it consumes a lot of resources, has slow output, and is inefficient, limiting its application. Therefore, there is currently a lack of a simple and practical single discrete simulator that can match the wireless channel attenuation in 5G non-orthogonal mobile communication networks.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0007] Purpose of the invention: The technical problem to be solved by the present invention is to provide a discrete simulator for wireless channel attenuation in 5G non-orthogonal mobile communication networks in response to the shortcomings of the existing technology.

[0008] In order to solve the above technical problems, the present invention discloses a discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network, comprising the following steps:

[0009] Step 1, establishing a first-order finite state Markov chain according to the input of the simulator;

[0010] Step 2: According to the number of users in a single user group in the set 5G non-orthogonal mobile communication network , based on the first-order finite state Markov chain obtained in step 1, construct a new Markov chain , get the state space and transition probability matrix;

[0011] Step 3: Generate and output simulation results based on the state space and transition probability matrix obtained in step 2 and the length of the time series output by the set simulator, and complete the discrete simulation of wireless channel attenuation in the 5G non-orthogonal mobile communication network.

[0012] Furthermore, the input of the simulator described in step 1, namely the probability density function of the instantaneous signal-to-noise ratio SNR at the receiving end and the set single wireless channel Number of states, time interval unit The numerical value of .

[0013] Furthermore, the simulation result described in step 3 is a time series including a state identifier and a corresponding instantaneous signal-to-noise ratio (SNR) of the receiving end.

[0014] Furthermore, the first-order finite state Markov chain established in step 1 includes:

[0015] Step 1-1, use Indicates the instantaneous signal-to-noise ratio (SNR) threshold of the receiving end arranged in ascending order and meeting and Under these conditions, the entire SNR range is divided into a series of non-overlapping boundaries with boundary values of of Interval intervals; among them, the completion boundary value is calculated by the equal probability EP method choice;

[0016] Step 1-2, use Represents a stable The state space of a first-order finite state Markov chain with states is expressed as follows:

[0017] ;

[0018] in, Indicates the states, and use the The interval corresponds to status;

[0019] Steps 1-3, calculate the state transition probability, expressed as follows:

[0020] ;

[0021] in, It is from Status To Status The transition probability of

[0022] Steps 1-4: Divide the time axis into units of equal size. interval, then in discrete time On the above, a first-order finite state Markov chain is constructed to represent the time-varying characteristics of wireless channel attenuation, which is as follows:

[0023] ;

[0024] in, is the steady-state probability, is the instantaneous SNR threshold at the receiving end, Indicates The level pass rate of the SNR value at the receiving end is calculated as follows:

[0025] ;

[0026] in, represents the maximum Doppler frequency, is an exponential function; steady-state probability The calculation method is as follows:

[0027] ;

[0028] The average value of the instantaneous signal-to-noise ratio (SNR) at the receiving end is , the specific calculation method is as follows:

[0029] ;

[0030] in, It represents the instantaneous signal-to-noise ratio (SNR) at the receiving end, and its probability density function is: .

[0031] Furthermore, the construction of a new Markov chain described in step 2 includes:

[0032] Step 2-1: according to the number of users in a single user group in the set 5G non-orthogonal mobile communication network , construct a coding table and use it to represent the new Markov chain In the coding table, numbers are used to represent the corresponding states. Let the number of states be ;

[0033] Step 2-2, according to the coding table, the new Markov chain The state space , which is expressed as follows:

[0034] ;

[0035] in, Represents the new Markov chain The state space The status;

[0036] Step 2-3, set From the state To status The transition probability is calculated according to the coding table. , then the new Markov chain The transition probability matrix is expressed as .

[0037] Furthermore, the construction of the coding table described in step 2-1 includes:

[0038] Step 2-1-1, define the code table Indicates the column number, Indicates the highest digit, Indicates the lowest digit, digits from arrive gradually descending;

[0039] Step 2-1-2, set the coding table rules and generate the coding table according to the rules;

[0040] Step 2-1-3, calculate the number of states in the state space of the new Markov chain according to the coding table.

[0041] Furthermore, the coding table setting rules described in step 2-1-2 include:

[0042] The contents of the code table are set according to the following rules:

[0043] Rule 1: All columns in the table start from 1 and end with Finish;

[0044] Rule 2: In the same row of the code table, In a column of numbers, the digits in the higher digits are not greater than the digits in the lower digits;

[0045] Rule three, The columns are combined based on a Base numbering system; where the base is , the digits are from 1 to Composition, add 1 to the lowest digit one by one, and every Just carry 1 up to the next higher digit;

[0046] Rule 4: Valid numbers in each row of the coding table Satisfy rules 2 and 3;

[0047] Rule Five, List The numbers in the , starting at 1 and incrementing by 1 for each row.

[0048] Furthermore, the number of states in the state space of the new Markov chain is calculated according to the coding table as described in step 2-1-3, including:

[0049] After generating the coding table according to rules 1 to 5, the new Markov chain is calculated. In the state space of :

[0050] .

[0051] Furthermore, the transition probability is calculated according to the coding table described in step 2-3 ,include:

[0052] Step 2-3-1, set status Expressed as ,state Expressed as , and the status , build a collection , for the status , build a collection ;

[0053] Step 2-3-2, list the collection All possible permutations of the elements in are represented as follows:

[0054] ;

[0055] List Collections All possible permutations of the elements in are represented as follows:

[0056] ;

[0057] Step 2-3-3, define a mapping function , which is expressed as follows:

[0058] ;

[0059] According to the above mapping function, in the arrangement Mapping on it, we get a new set , which is expressed as follows:

[0060] ;

[0061] Step 2-3-4, based on the collection Construct a target set, including:

[0062] Pair Collection Any element in , from indivual Each of Take a number from it and group the selected numbers together. For this element, list all possible groups, recorded as , record collection Elements in With group The corresponding relationship information is recorded as ;

[0063] Store all groups and correspondence information into a collection In the target set ;

[0064] Step 2-3-5, collect As an arrow set, and check The results are as follows:

[0065] if ,but ;

[0066] if , then calculate , the specific method is as follows:

[0067] ;

[0068] Among them, the collection ,set up is a collection An element of , then according to the corresponding relationship information, the element in the set The corresponding elements in , represents the first-order finite state Markov chain established in step 1 from its Status To Status The transition probability.

[0069] Furthermore, the generation and output of simulation results described in step 3 include:

[0070] Step 3-1, determine the initial state, that is, from the state space Choose one state as the initial state ;

[0071] Step 3-2, generate the subsequent state, that is, set the current moment to , the current status is , according to the transition probability matrix Corresponding status The rows of , and calculate a series of intervals 、 、 、…、 , the number of intervals is ;

[0072] Generate a uniform random number ,Will With this interval Compare, then The sequence number that falls into the interval is the next state sequence number, thus determining the next state ;

[0073] Step 3-3, repeat the generation, and change the status generated in step 3-2 to Add to the time series and set it as the new current state , repeat steps 3-2 and 3-3 until the generated time series meets the set length of the simulator output time series;

[0074] Step 3-4, sort out the state sequence , the output includes a time series of state identifiers and the corresponding receiving end instantaneous signal-to-noise ratio (SNR) size intervals.

[0075] Beneficial effects

[0076] 1. The simulator proposed in this invention meets the needs of network construction, operation and maintenance, and academic research. The design has the advantages of clear structure, strong adaptability, and high execution efficiency, as follows:

[0077] 2. The simulator of the present invention has relatively independent parts and simple logic.

[0078] 3. The discrete simulator of the present invention requires only five input quantities, including: the probability density function of the instantaneous signal-to-noise ratio (SNR) of the receiving end, the number of states of a single wireless channel, the unit value of the time interval, the number of users in a single user group, and the length of the required simulator output time series. All of these are universal variables and functions, and in particular, there is no limitation on the specific type and expression of the probability density function, which makes the present invention highly adaptable.

[0079] 4. The present invention satisfies the wireless channel test simulation requirements of a single user in a 5G non-orthogonal multi-user access mobile communication network using only a single discrete simulator, and only one time series generation process is used in the design, so the execution efficiency is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more apparent.

[0081] Figure 1 Schematic diagram of the first-order FSMC model.

[0082] Figure 2 Schematic representation of the state space encoding of the present invention.

[0083] Figure 3 Schematic diagram of the overall architecture of the present invention.

[0084] Figure 4 Schematic diagram of an example of coding table rules in an embodiment. DETAILED DESCRIPTION

[0085] The present invention proposes a discrete simulator for wireless channel attenuation in 5G non-orthogonal mobile communication networks, such as Figure 3 Shown, including:

[0086] Step 1: Based on the probability density function of the input instantaneous signal-to-noise ratio (SNR) of the receiving end and the artificially set single wireless channel Number of states, time interval unit The numerical value of is used to establish a first-order finite state Markov chain FSMC, such as Figure 1 As shown, the specific process includes:

[0087] use It represents the instantaneous signal-to-noise ratio (SNR) at the receiving end, and its probability density function is: ; Given by external input The specific expression of Average value for:

[0088]

[0089] use Represents a stable, The state space of the first-order FSMC with states. The state transition of this FSMC only occurs between adjacent states. Therefore, we can get:

[0090]

[0091] in From the state To status Based on the properties of the first-order FSMC, for ,have In addition, there are and .use represents the steady-state probability.

[0092] The time axis is divided into equal-sized units. interval, then in discrete time A first-order FSMC can be constructed to represent the time-varying characteristics of wireless channel attenuation. represents the instantaneous SNR threshold of the receiving end in ascending order, and and The entire SNR range is divided into a series of non-overlapping boundaries with boundary values of If the interval is The instantaneous SNR value of the receiving end is in the interval Then the channel The state is called the state That is, in short, In wireless networks, when a mobile terminal moves at a certain speed, it will cause the Doppler frequency effect. It determines the fading characteristics of the signal envelope in the channel. Represents the maximum Doppler frequency, then the boundary value of the given fading channel is , transition probability and It can be approximated into the following formula:

[0093]

[0094] in, Indicates The level passing rate of the SNR value at the receiving end is derived as In addition, through the formula , we can get the steady-state probability . The completion boundary value is calculated by the equal probability EP method choice.

[0095] Step 2: Based on the number of users in a single user group in the artificially set 5G non-orthogonal mobile communication network , construct a new Markov chain based on the FSMC obtained in step 1 , giving its state space and transition probability matrix, the specific process includes:

[0096] For the number of users in a single user group in an artificially set 5G non-orthogonal mobile communication network , The state space is represented as Figure 2 . Use a coding table to represent For the sake of brevity, the state space of and of" ", and use numbers to directly represent the corresponding status. In this coding table, is set to the highest digit, is set to the lowest digit. arrive Decrease gradually.

[0097] The rules of the coding table are as follows:

[0098] 1) All columns in the table start at 1. When the entire table ends, the columns in the table At the same time Finish.

[0099] 2) In the same row of the coding table, for , high digit The upper digit should not be greater than the lower digit On the numbers.

[0100] 3) List Taken together, it is based on a The base number is , the digits are from 1 to The counting rule is to use the digits Add 1 each time, and every To the next level Progress 1, among which .

[0101] 4) Valid digits in each row of the coding table The above rules 2) and 3) must be met.

[0102] 5) Columns Each increment of is 1.

[0103] Then, in the state space The number of states is , which is a multi-level series summation , we can get:

[0104]

[0105] structure The transition probability matrix is as follows:

[0106] use represent The state space of .here Indicates the number of elements in a collection. From the state To status The transition probability is The transition probability matrix is . Corresponding to Figure 2 In the coding table , set the state yes ,state yes Then, through the following steps, we can get ,Right now:

[0107] 1) Status , build a collection . About status , build a collection .

[0108] 2) List all possible permutations of the elements in set A:

[0109]

[0110] List all possible permutations of the elements in set B:

[0111]

[0112] It is worth noting that the arrangements obtained from the same set are distinguished from each other by specific numbers, rather than by the abstract symbols in step 2).

[0113] 3) Define a mapping function ,Right now:

[0114]

[0115] This means that about , generate a new set. Here for , is An element of In the whole Mapping the above, we get a new set:

[0116]

[0117] Obviously, the collection is nested in .

[0118] 4) From Construct a set called the target set .

[0119] Specifically, consider the set An element of Each of Take a number from it and form these selected numbers into a group. For this element, list all possible groups, that is, , and record middle and Correspondence information .

[0120] Finally, the collection Contains from All possible forms of . In addition, all the corresponding relationship information is recorded in the form of It is worth noting that Duplicate data is allowed in the are different from each other.

[0121] 5) Name Set for the arrows and check If , then it is impossible to go from state Transition to state ,Right now .if , then we can calculate . Further assume that , is a collection An element of . According to the table , you get The corresponding elements in .

[0122] In other words, there is .for , export ,in In the steady-state first-order FSMC, the To status Then, when hour, You can start from Obtain.

[0123] Step 3, based on the result from step 2 Based on the state space and transition probability matrix, and according to the manually set length of the required simulator output time series, a time series containing state identifiers and the corresponding instantaneous signal-to-noise ratio (SNR) of the receiving end is generated and output. The specific process includes:

[0124] 1) Determine the initial state, that is, from the state space Choose one state as the initial state .

[0125] 2) Generate the subsequent state, that is, set the current moment to , the status is , according to the transition probability matrix Corresponding status The rows of the transition probability distribution ;

[0126] Generate a uniform random number , calculate the cumulative probability, add up one by one until it falls into Corresponding interval, determine the next state .

[0127] 3) Repeatedly generate the new state Join the time series and use it as the new current state , repeat step 2) until a sequence of the desired simulator output length is generated.

[0128] 4) Organize , output a time series containing the state identifier and the corresponding receiving end instantaneous signal-to-noise ratio SNR size interval.

[0129] Example 1:

[0130] The embodiment of the present invention provides a discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network, which is applied to wireless channel testing of a single user in a 5G non-orthogonal multi-user access mobile communication network, supporting network construction, operation and maintenance, and academic theoretical research, including the following steps:

[0131] Step 1: Based on the input variables and functions, complete the calculation and establish the first-order finite state Markov chain FSMC as follows:

[0132] 1-1. Equal probability EP method by Determines the boundary value Taking into account , By solving the equation Come get it. Then, By solving the equation To get, and so on. Finally, unknown quantity, namely Can be solved by An equation is obtained.

[0133] 1-2. Calculation ; By calculating Get the steady-state probability .

[0134] 1-3. Calculate in sequence 、 (in ), (in ), thus obtaining the transition probability matrix of the first-order finite state Markov chain FSMC.

[0135] Step 2: Construct a new Markov chain , giving its state space and the transition probability matrix ,as follows:

[0136] 2-1. Calculation The number of states in the state space ,generate Figure 2 The state space encoding table shown.

[0137] 2-2. Calculation Each of the transition probability matrices The values are as follows:

[0138] 2-2-1. Corresponding to the coding table , when the state yes ,state yes , for the status , build a collection . About status , build a collection .

[0139] 2-2-2. List all possible permutations of the elements in set A:

[0140]

[0141] List all possible permutations of the elements in set B:

[0142]

[0143] It is worth noting that the permutations obtained from the same set are distinguished from each other by specific numbers, rather than by abstract symbols in this step. For example, when assuming Sometimes, there are and Although based on the perspective of abstract symbols, is different from However, based on the specific numbers, the two are the same, that is, Therefore, in Only one is retained .

[0144] 2-2-3. Give the mapping function .

[0145] This means that about , generate a new set. Here for , is An element of In the whole Mapping the above, we get a new set:

[0146]

[0147] Obviously, the collection is nested in For example, given , then in have .

[0148] 2-2-4. From Construct a target set .

[0149] Specifically, consider the set An element of Each of Take a number from it and form these selected numbers into a group. For this element, list all possible groups, that is, , and record middle and Correspondence information For example, given , middle ,as well as middle , then , which includes groups. At the same time, yes .

[0150] Finally, the collection Contains from All possible forms of . In addition, all the corresponding relationship information is recorded in the form of It is worth noting that Duplicate data is allowed in the are different from each other. For example, given and middle ,So contain . And there is .consider The corresponding ,but There are . And there is Therefore, although There are two , but they can be based on Correspondence in To distinguish, that is:

[0151]

[0152] 2-2-5. Weigh Set for the arrows and check If , then it is impossible to go from state To status Migration, that is .if , then we can calculate . Further assume that , is a collection An element of . According to the table , you get The corresponding elements in .

[0153] In other words, there is .for , export ,in In the steady-state first-order FSMC, the To status Then, when hour, You can start from Obtain.

[0154] Step 3: For , generates and outputs a time series containing the state identifier and the corresponding receiving end instantaneous signal-to-noise ratio SNR size interval, as follows:

[0155] 3-1. From the state space Choose one state as the initial state .

[0156] 3-2. Assume the current time is , the status is , according to the transition probability matrix Corresponding status The rows of the transition probability distribution ;

[0157] Generate a uniform random number , calculate the cumulative probability, add up one by one until it falls into Corresponding interval, determine the next state For example, for , , can be divided into 4 intervals: ,when Sometimes, there are ,So , current status The next state is .

[0158] 3-3. New state Join the time series and use it as the new current state , repeat steps 3-2 until a sequence of the desired simulator output length is generated.

[0159] 3-4. Arrangement , output a time series containing the state identifier and the corresponding receiving end instantaneous signal-to-noise ratio SNR size interval.

[0160] Example 2:

[0161] In one embodiment, Figure 4 As shown, , , as an example, the coding table rules are explained in detail.

[0162] Example 3:

[0163] In the case where the coding table rules are as in Example 2, it is assumed that after step 3-1, step 3-2, and step 3-3, a state sequence is obtained. Right now, .

[0164] At time t=2, according to the coding table shown in Example 2, Then, according to the first-order finite state Markov chain described in step 1, the entire SNR range is divided into a series of non-overlapping boundary values The interval interval, know The corresponding value for "2" is SNR interval 2, and the corresponding value for "4" is SNR interval 4. Therefore, the output at time t=2 is "( ) status identification 16", "receiving end instantaneous signal-to-noise ratio SNR size interval: SNR interval interval 2, SNR interval interval 4, SNR interval interval 4".

[0165] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a computer program, i.e., a software product. This computer program software product can be stored in a storage medium and includes instructions for enabling a device including a data processing unit (such as a personal computer, server, single-chip microcomputer, MCU, or network device) to execute the methods described in various embodiments of the present invention or certain portions of these embodiments.

[0166] The present invention provides a concept and method for a discrete simulator of wireless channel attenuation in a 5G non-orthogonal mobile communication network. There are many methods and approaches to implement this technical solution. The above is only a preferred embodiment of the present invention. It should be noted that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be considered within the scope of protection of the present invention. All components not specified in this embodiment can be implemented using existing technologies.

Claims

1. A discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network, characterized in that: The following steps are involved: Step 1, establishing a first-order finite state Markov chain according to the input of the simulator; Step 2: According to the number of users in a single user group in the set 5G non-orthogonal mobile communication network , based on the first-order finite state Markov chain obtained in step 1, construct a new Markov chain , get the state space and transition probability matrix; Step 3: Generate and output simulation results based on the state space and transition probability matrix obtained in step 2 and the length of the time series output by the set simulator, completing the discrete simulation of wireless channel attenuation in the 5G non-orthogonal mobile communication network; The step 1 of establishing a first-order finite state Markov chain includes: Step 1-1, use Indicates the instantaneous signal-to-noise ratio (SNR) threshold of the receiving end arranged in ascending order and meeting and Under these conditions, the entire SNR range is divided into a series of non-overlapping boundaries with boundary values of of Interval intervals; among them, the completion boundary value is calculated by the equal probability EP method choice; Step 1-2, use Represents a stable The state space of a first-order finite state Markov chain with states is expressed as follows: ； in, Indicates the states, and use the The interval corresponds to status; Steps 1-3, calculate the state transition probability, expressed as follows: ； in, It is from Status To Status The transition probability of Steps 1-4: Divide the time axis into units of equal size. interval, then in discrete time On the above, a first-order finite state Markov chain is constructed to represent the time-varying characteristics of wireless channel attenuation, which is as follows: ； in, is the steady-state probability, is the instantaneous SNR threshold at the receiving end, Indicates The level pass rate of the SNR value at the receiving end is calculated as follows: ； in, represents the maximum Doppler frequency, is an exponential function; steady-state probability The calculation method is as follows: ； The average value of the instantaneous signal-to-noise ratio (SNR) at the receiving end is , the specific calculation method is as follows: ； in, It represents the instantaneous signal-to-noise ratio (SNR) at the receiving end, and its probability density function is: ; Generate and output simulation results as described in step 3, including: Step 3-1, determine the initial state, that is, from the state space Choose one state as the initial state ; Step 3-2, generate the subsequent state, that is, set the current moment to , the current status is , according to the transition probability matrix Corresponding status The rows of , and calculate a series of intervals 、 、 、…、 , the number of intervals is ; Generate a uniform random number ,Will With this interval Compare, then The sequence number that falls into the interval is the next state sequence number, thus determining the next state ; Step 3-3, repeat the generation, and change the status generated in step 3-2 to Add to the time series and set it as the new current state , repeat steps 3-2 and 3-3 until the generated time series meets the set length of the simulator output time series; Step 3-4, sort out the state sequence , the output includes a time series of state identifiers and the corresponding receiving end instantaneous signal-to-noise ratio (SNR) size intervals.

2. A discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network according to claim 1, characterized in that: The input of the simulator described in step 1 is the probability density function of the instantaneous signal-to-noise ratio (SNR) at the receiving end and the set single wireless channel. Number of states, time interval unit The numerical value of .

3. A discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network according to claim 2, characterized in that: The simulation result described in step 3 is a time series including a state identifier and the size of the instantaneous signal-to-noise ratio (SNR) of the corresponding receiving end.

4. A discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network according to claim 3, characterized in that: Construct a new Markov chain as described in step 2, including: Step 2-1: according to the number of users in a single user group in the set 5G non-orthogonal mobile communication network , construct a coding table and use it to represent the new Markov chain In the coding table, numbers are used to represent the corresponding states. Let the number of states be ; Step 2-2, according to the coding table, the new Markov chain The state space , which is expressed as follows: ； in, Represents the new Markov chain The state space The status; Step 2-3, set From the state To status The transition probability is calculated according to the coding table. , then the new Markov chain The transition probability matrix is expressed as .

5. A discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network according to claim 4, characterized in that: Construct the coding table described in step 2-1, including: Step 2-1-1, define the code table Indicates the column number, Indicates the highest digit, Indicates the lowest digit, digits from arrive gradually descending; Step 2-1-2, set the coding table rules and generate the coding table according to the rules; Step 2-1-3, calculate the number of states in the state space of the new Markov chain according to the coding table.

6. A discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network according to claim 5, characterized in that: The rules for setting the code table described in step 2-1-2 include: The contents of the code table are set according to the following rules: Rule 1: All columns in the table start from 1 and end with Finish; Rule 2: In the same row of the code table, In a column of numbers, the digits in the higher digits are not greater than the digits in the lower digits; Rule three, The columns are combined based on a Base numbering system; where the base is , the digits are from 1 to Composition, add 1 to the lowest digit one by one, and every Just carry 1 up to the next higher digit; Rule 4: Valid numbers in each row of the coding table Satisfy rules 2 and 3; Rule Five, List The numbers in the , starting at 1 and incrementing by 1 for each row.

7. A discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network according to claim 6, characterized in that: The number of states in the state space of the new Markov chain is calculated according to the encoding table described in step 2-1-3, including: After generating the coding table according to rules 1 to 5, the new Markov chain is calculated. In the state space of : 。 8. A discrete simulator for wireless channel attenuation in a 5G non-orthogonal mobile communication network according to claim 7, characterized in that: The transition probability is calculated according to the coding table described in step 2-3 ,include: Step 2-3-1, set status Expressed as ,state Expressed as , and the status , build a collection , for the status , build a collection ; Step 2-3-2, list the collection All possible permutations of the elements in are represented as follows: ； List Collections All possible permutations of the elements in are represented as follows: ； Step 2-3-3, define a mapping function , which is expressed as follows: ； According to the above mapping function, in the arrangement Mapping on it, we get a new set , which is expressed as follows: ； Step 2-3-4, based on the collection Construct a target set, including: Pair Collection Any element in , from indivual Each of Take a number from it and group the selected numbers together. For this element, list all possible groups, recorded as , record collection Elements in With group The corresponding relationship information is recorded as ; Store all groups and correspondence information into a collection In the target set ; Step 2-3-5, collect As an arrow set, and check The results are as follows: if ,but ; if , then calculate , the specific method is as follows: ； Among them, the collection ,set up is a collection An element of , then according to the corresponding relationship information, the element in the set The corresponding elements in , represents the first-order finite state Markov chain established in step 1 from its Status To Status The transition probability.

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