A network base station deployment optimization method, system, device and storage medium based on hard core distance and dynamic payment mechanism

Abstract

The present invention discloses a method for optimizing the deployment of network base stations based on hard-core distance and dynamic payment mechanism, the method comprising: obtaining a multi-cell communication network model, the multi-cell communication network model comprising a base station location distribution conforming to the HCPP process and a user location distribution conforming to the PPP process; calculating the user's intra-cell interference and inter-cell interference according to the multi-cell communication network model; calculating the system service coverage probability according to the user's intra-cell interference and inter-cell interference; optimizing the network base station deployment according to the system service coverage probability and the dynamic payment mechanism. By modeling the base station location distribution as an HCPP process, considering the dynamic user payment strategy, and using an improved particle swarm algorithm to optimize the base station deployment, while reducing the communication interference of multi-cell base stations, it can also reduce the user's communication payment according to the actual needs of the user, which can not only improve the system utilization rate, but also conform to the user-centric development concept in the new generation of wireless communication technology.

Description

Technical Field

[0001] The present invention relates to a network base station deployment optimization method, system, device and storage medium based on hard-core distance and dynamic payment mechanism, belonging to the technical field of communication system analysis. Background Art

[0002] With the construction of communication infrastructure, base station deployment planning plays an important role in improving network coverage performance. The optimization of cell base station deployment based on random geometry is the basis of actual communication engineering project planning. In actual communication systems, in order to reduce interference or expand the coverage area, it is necessary to select the location of the transmitter, and at this time, repulsion occurs between adjacent transmitters.

[0003] However, in the actual construction of communication infrastructure, there are already many methods to continuously optimize the distribution of base stations, but these methods can only reduce the interference between base stations. For service objects in the communication field, user participation is a deeper concern when deploying the system. This is a key requirement for any technology to be applied to actual engineering projects, especially in the consumer-oriented field of communications. The deployment of base stations must reduce interference between base stations while effectively reducing communication fees to truly meet user needs. However, there is no technology in the existing technology that can meet both of these requirements at the same time. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a network base station deployment optimization method, system, device and storage medium based on hard core distance and dynamic payment mechanism. The base station location distribution is modeled as an HCPP process, dynamic user payment strategy is considered, and an improved particle swarm algorithm is used to optimize the base station deployment, and the relationship between hard core parameters and optimal base station density is analyzed.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a method for optimizing network base station deployment based on hard core distance and dynamic payment mechanism, comprising:

[0007] Acquire a multi-cell communication network model, wherein the multi-cell communication network model includes a base station location distribution that complies with an HCPP process and a user location distribution that complies with a PPP process;

[0008] According to the multi-cell communication network model, calculate the user's intra-cell interference and inter-cell interference;

[0009] Calculate the system service coverage probability based on the user's intra-cell interference and inter-cell interference;

[0010] Optimize network base station deployment based on system service coverage probability and dynamic payment mechanism.

[0011] Furthermore, the calculation of intra-cell interference and inter-cell interference of a user according to a multi-cell communication network model specifically includes:

[0012] According to the multi-cell communication network model, users with a retention probability that meets the hard core point process are screened out. The retention probability is the probability that the straight-line distance between two base station sites is greater than a preset distance. The calculation formula is:

[0013]

[0014] Where d is the length of the straight-line distance between two base stations greater than the preset distance; Pr(d) is the retention probability of the hard core point process; q is the correction factor; λ b is the base station density;

[0015] According to the users with retention probability that meet the hard core point process, intra-cell interference and inter-cell interference are calculated respectively by Laplace transform;

[0016] The Laplace transform calculation formula for intra-cell interference is:

[0017]

[0018] Where, γ is the signal-to-interference ratio threshold for users to successfully access the base station; d llk is the straight-line distance between the kth user in the lth cell and the base station in the lth cell; α is the path loss factor; λ u is the user density in the cell; sin c() is the Singer function;

[0019] The Laplace transform calculation formula for inter-cell interference is:

[0020]

[0021] In the formula, when simplifying the parameters, let A is a random variable of Moment; H k Represents the square of the small-scale fading of the channel; is the mean number of users in each cell; R is the cell service radius.

[0022] Further, the calculation of the system service coverage probability according to the intra-cell interference and inter-cell interference of the user specifically includes:

[0023] The signal-to-interference ratio threshold γ for users to successfully access the base station is used as the criterion for whether the coverage is obtained. The probability mother functional of the Poisson cluster process is combined, and the Laplace function of intra-cell interference and the Laplace function of inter-cell interference are substituted to calculate the system service coverage probability. The calculation formula is:

[0024]

[0025] In the formula, represents the expectation operation, and Substituting into the above formula, the system service coverage probability is:

[0026]

[0027] in

[0028]

[0029] △ is a parameter determined by system parameters.

[0030] Further, the dynamic payment mechanism includes the service provider giving an uplink communication price to a single user according to the cell load;

[0031] When the load is high, the service provider offers a higher uplink communication price to each user;

[0032] When the load is low, the service provider will reduce the price of uplink communication between the user and the base station for each user;

[0033] The dynamic payment mechanism adopts a hyperbolic growth function model, and the calculation formula is:

[0034]

[0035] Where z is the uplink communication price of a single user; p0 is the initial access price between the user and the base station; θ is the cell load threshold, n is a constant positive parameter that controls the slope, and m is the cell communication load.

[0036] Furthermore, the optimization of network base station deployment according to the system service coverage probability and the dynamic payment mechanism specifically includes:

[0037] According to the system service coverage probability and dynamic payment mechanism, an optimization function model is established, and the calculation formula is:

[0038]

[0039] Where sc is the system area radius; B is the system bandwidth; is the penalty coefficient for the occurrence of communication interruption events; a and b are the parameter conditions that the function model needs to meet respectively;

[0040] Based on the particle swarm algorithm based on the flight characteristics of wild geese, the base station density under the lowest payment condition is calculated, and the base stations are deployed according to the base station density to complete the deployment optimization of network base stations.

[0041] In a second aspect, the present invention provides a network base station deployment optimization system based on hard core distance and dynamic payment mechanism, including:

[0042] Model acquisition module: used to acquire a multi-cell communication network model, wherein the multi-cell communication network model includes a base station location distribution that complies with the HCPP process and a user location distribution that complies with the PPP process;

[0043] Cell interference calculation module: used to calculate the user's intra-cell interference and inter-cell interference according to the multi-cell communication network model;

[0044] Coverage probability calculation module: used to calculate the system service coverage probability based on the user's intra-cell interference and inter-cell interference;

[0045] Optimization module: used to optimize network base station deployment based on system service coverage probability and dynamic payment mechanism.

[0046] In a third aspect, a network base station deployment optimization device based on hardcore distance and dynamic payment mechanism includes a processor and a storage medium;

[0047] The storage medium is used to store instructions;

[0048] The processor is used to operate according to the instructions to execute the steps of any of the methods described above.

[0049] In a fourth aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the above methods.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention models the base station location distribution as an HCPP process, considers the dynamic user payment strategy, adopts an improved particle swarm algorithm to optimize the base station deployment, analyzes the relationship between the hard-core parameters and the optimal base station density, and combines the hard-core distance with the dynamic payment mechanism. While reducing the communication interference of multi-cell base stations, it can also reduce the user's communication payment according to the actual needs of the user, which can not only improve the system utilization rate, but also conform to the user-centric development concept in the new generation of wireless communication technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the optimization method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0054] The present invention uses the Poisson Point Process (PPP) which cannot accurately simulate the location distribution of actual base stations, while the Hard-Core Poisson Point Process (HCPP) is characterized in that the distance between any two points is greater than the exclusion distance, so the HCPP process is more suitable for modeling the actual base station location. On the other hand, user participation is a deeper concern when deploying the system, which is a key requirement for any technology to be applied to actual engineering projects, especially in the consumer-oriented field of communications. A suitable communication payment strategy can not only improve system utilization, but also conform to the user-centered development concept in the new generation of wireless communication technology, and is the focus of attention of service providers and users. Based on this, the present invention provides a network base station deployment optimization method, system, device and storage medium based on hard-core distance and dynamic payment mechanism.

[0055] Embodiment 1:

[0056] like Figure 1 As shown, this embodiment provides a network base station deployment optimization method based on hard core distance and dynamic payment mechanism, and the specific steps include:

[0057] S1: Acquire a multi-cell communication network model, where the multi-cell communication network model includes a base station location distribution that complies with an HCPP process and a user location distribution that complies with a PPP process;

[0058] S2: Calculate the user's intra-cell interference and inter-cell interference according to the multi-cell communication network model;

[0059] S3: Calculate the system service coverage probability based on the user's intra-cell interference and inter-cell interference;

[0060] S4: Optimize network base station deployment based on system service coverage probability and dynamic payment mechanism.

[0061] In step S1, for a communication network composed of multiple cells, each cell is generally provided with one base station. The base station in the cell is responsible for providing communication services to users in the cell and can establish communication connections with base stations in other cells. For this purpose, a hard-core Poisson point process (HCPP) is used to accurately simulate the location distribution of actual base stations. The characteristic of the HCPP process is that the distance between any two points is greater than the exclusion distance. Therefore, the HCPP process is more suitable for modeling the actual base station location. For the location distribution of users, only the Poisson point process (PPP) is needed to meet the method requirements of this embodiment.

[0062] In step S2, after the multi-cell communication network model is established, in order to effectively calculate the intra-cell interference and inter-cell interference, this embodiment uses the Laplace transform method to calculate and establish a connection between the user's location distribution and the deployment of the base station.

[0063] First, the Laplace transform calculation formula of a function f(x) in the hard core point process is defined as:

[0064]

[0065] Where Pr(d) is the retention probability of the hard core point process, d is the length value of the straight line distance between two base stations greater than the preset distance; λ b is the base station density.

[0066] In the above formula, the retention probability is the probability that the straight-line distance between two base station sites is greater than the preset distance. According to the multi-cell communication network model, users with the retention probability that meet the hard core point process are screened out. The retention probability calculation formula is:

[0067]

[0068] Where q is the correction factor.

[0069] Then, the intra-cell interference and inter-cell interference are calculated respectively according to the definition formula of Laplace transform. The Laplace transform calculation formula of intra-cell interference is:

[0070]

[0071] Where, γ is the signal-to-interference ratio threshold for users to successfully access the base station; d llk is the straight-line distance between the kth user in the lth cell and the base station in the lth cell; α is the path loss factor; λ u is the user density in the cell; sin c() is the Singer function;

[0072] The Laplace transform calculation formula for inter-cell interference is:

[0073]

[0074] In the formula, when simplifying the parameters, for the convenience of expression, let A is a random variable of Moment; H k Represents the square of the small-scale fading of the channel; is the mean number of users in each cell; R is the cell service radius.

[0075] In step S3, the signal-to-interference ratio threshold γ of the user successfully accessing the base station is used as the criterion for whether it is covered. The probability mother functional of the Poisson cluster process is combined, and the Laplace function of the intra-cell interference and the Laplace function of the inter-cell interference are substituted to calculate the system service coverage probability. The calculation formula is:

[0076]

[0077] In the formula, represents the expectation operation, and Substituting into the above formula, the system service coverage probability is:

[0078]

[0079] in

[0080]

[0081] △ is a parameter determined by system parameters.

[0082] In step S4, when optimizing the base station deployment, the user's payment mechanism needs to be taken into consideration. The traditional static payment strategy does not guarantee the maximum use value of resources in the resource demand process. Therefore, the user's dynamic payment mechanism is considered. The dynamic payment mechanism described in this embodiment includes the service provider giving a single user uplink communication price according to the cell load situation;

[0083] When the load is high, the service provider offers a higher uplink communication price to each user;

[0084] When the load is low, the service provider will reduce the price of uplink communication between the user and the base station for each user;

[0085] The dynamic payment mechanism adopts a hyperbolic growth function model, and the calculation formula is:

[0086]

[0087] Where z is the uplink communication price of a single user; p0 is the initial access price between the user and the base station; θ is the cell load threshold, n is a constant positive parameter that controls the slope, and m is the cell communication load.

[0088] Therefore, we can directly establish an optimization function model based on the system service coverage probability and dynamic payment mechanism. The calculation formula of the model is:

[0089]

[0090] sta:0≤η≤1

[0091] b:

[0092] Where sc is the system area radius; B is the system bandwidth; is the penalty coefficient for the communication interruption event; a and b are the parameter conditions that the function model needs to meet respectively.

[0093] To this end, it is necessary to calculate the optimal solution of the optimization function model formula. In this embodiment, a particle swarm algorithm based on the flight of wild geese is used to find the optimal solution of the optimization function model.

[0094] In the later stage of particle search, particle convergence limits the search range of particles. Therefore, how to search for the optimal solution in the solution space more effectively is the focus of the particle swarm algorithm. In order to expand the search range, the number of particles in the particle swarm must be increased, otherwise the global optimal solution cannot be obtained. Too many particles will increase the computational complexity of the algorithm, and too few particles will make it difficult for the algorithm to converge when solving the global optimal solution. Therefore, the IMPSO algorithm refers to the flight characteristics of geese. On the one hand, the IMPSO algorithm replaces the global extreme value with the individual extreme value of the front-end optimal particle to avoid particle convergence and maintain particle diversity; on the other hand, the IMPSO algorithm enables each particle to use the useful information of other particles, and uses the weighted average of the individual extreme value multiplied by its current fitness value instead of the individual extreme value, thereby increasing the incentive of individual particles. The algorithm strengthens the cooperation and competition between particles. The above two improvements solve the contradiction between the algorithm search speed and algorithm accuracy.

[0095] In the IMPSO algorithm, the flight intensity of geese is regarded as the quality of particles, that is, the historical optimal fitness of particles. All particles are sorted according to the historical optimal fitness, and the historical optimal particle with the best fitness value is selected as the leading particle. After each iteration, the optimal fitness value of each particle is updated, and then all particles are re-sorted. All particles are arranged from front to back according to the historical optimal fitness value. The individual extreme value of the previous particle is used as the global extreme value of the next particle, and the global extreme value of the leading particle is used as its own individual extreme value. The parameter base station density λ to be optimized in the optimization function model formula is used as the optimization function model formula. b Considered as randomly generated particles, the velocity update formula of the ith particle at any k+1 time is expressed as:

[0096]

[0097] Where ω is the inertia weight; represents the velocity of the i-th particle at the k-th moment; represents the position of the i-th particle at the k-th moment; rand is a uniform random value between [0,1]; c1 and c2 represent learning factors; P i is the individual extreme value.

[0098] During the flight of wild geese, a leader goose in the front row leads the geese in the back row. The leader goose only relies on its own experience to make decisions, while the geese in the back row not only rely on their own experience, but also need to learn from the experience of other geese. Therefore, the IMPSO algorithm uses the individual extreme value of the front-end optimal particle to replace the global extreme value based on the flight law of wild geese. The individual extreme value of each particle except the leading particle is converted into the product of the weighted average of the individual extreme value and its current fitness value, that is, the updated individual extreme value of the particle, which can be expressed as:

[0099]

[0100] Where P i is the individual extreme value of the particle, P a is the updated individual extreme value of the particle; X i represents the position of the randomly generated i-th particle, f(X i ) represents its fitness value.

[0101] In the above formula, P i Use P a Substitution allows particles to use more information to make decisions, further preventing the algorithm from falling into a local optimum, while individuals gain more incentives, strengthening cooperation and competition among particles, and accelerating the convergence of the algorithm. From this, we can get the position update formula:

[0102]

[0103] After obtaining the updated position and velocity of each particle, exhaustively iterate all particles to obtain the final updated individual extreme value P a , which is the final optimized base station density λ in the optimization function model of base station deployment b . And according to the final optimized base station density λ b To deploy base stations, the network base station deployment optimization based on hard-core distance and dynamic payment mechanism in this embodiment is completed.

[0104] In this embodiment, the specific steps of the IMPSO algorithm are:

[0105] (1) Initialize the particle swarm;

[0106] (2) Calculate the current particle swarm algorithm fitness value f(X) of each particle;

[0107] (3) Update individual extreme values: Calculate the individual extreme value P of each particle i , compare the current fitness value f(X i ) and the particle individual extreme value P i The fitness value of P is greater than that of i , otherwise P iconstant;

[0108] (4) Particle swarm sorting: All particles are sorted according to P i Sort by the fitness values, select the particle with the largest fitness value as the leading particle, and return the other particles in turn;

[0109] (5) Calculate and update individual extreme values: Keep the individual extreme value of the leading particle unchanged, and calculate the updated individual extreme values ​​P of other particles a ;

[0110] (6) Calculate and update the global extreme value: keep the global extreme value of the leading particle unchanged, and the following particles take the updated individual extreme value of the previous particle as the updated global extreme value;

[0111] (7) Update the particle's velocity and position;

[0112] (8) Check whether the iteration stop condition is met: if it is met, exit; otherwise, go to step (2).

[0113] The present invention models the base station location distribution as an HCPP process, considers the dynamic user payment strategy, adopts an improved particle swarm algorithm to optimize the base station deployment, analyzes the relationship between the hard-core parameters and the optimal base station density, and combines the hard-core distance with the dynamic payment mechanism. While reducing the communication interference of multi-cell base stations, it can also reduce the user's communication payment according to the actual needs of the user, which can not only improve the system utilization rate, but also conform to the user-centric development concept in the new generation of wireless communication technology.

[0114] Embodiment 2:

[0115] A network base station deployment optimization system based on hard core distance and dynamic payment mechanism, comprising:

[0116] Model acquisition module: used to acquire a multi-cell communication network model, wherein the multi-cell communication network model includes a base station location distribution that complies with the HCPP process and a user location distribution that complies with the PPP process;

[0117] Cell interference calculation module: used to calculate the user's intra-cell interference and inter-cell interference according to the multi-cell communication network model;

[0118] Coverage probability calculation module: used to calculate the system service coverage probability based on the user's intra-cell interference and inter-cell interference;

[0119] Optimization module: used to optimize network base station deployment based on system service coverage probability and dynamic payment mechanism.

[0120] Embodiment three:

[0121] The embodiment of the present invention also provides a network base station deployment optimization device based on hard core distance and dynamic payment mechanism, including a processor and a storage medium;

[0122] The storage medium is used to store instructions;

[0123] The processor is used to operate according to the instructions to perform the steps of the following method:

[0124] Acquire a multi-cell communication network model, wherein the multi-cell communication network model includes a base station location distribution that complies with an HCPP process and a user location distribution that complies with a PPP process;

[0125] According to the multi-cell communication network model, calculate the user's intra-cell interference and inter-cell interference;

[0126] Calculate the system service coverage probability based on the user's intra-cell interference and inter-cell interference;

[0127] Optimize network base station deployment based on system service coverage probability and dynamic payment mechanism.

[0128] Embodiment 4:

[0129] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the following method are implemented:

[0130] Acquire a multi-cell communication network model, wherein the multi-cell communication network model includes a base station location distribution that complies with an HCPP process and a user location distribution that complies with a PPP process;

[0131] According to the multi-cell communication network model, calculate the user's intra-cell interference and inter-cell interference;

[0132] Calculate the system service coverage probability based on the user's intra-cell interference and inter-cell interference;

[0133] Optimize network base station deployment based on system service coverage probability and dynamic payment mechanism.

[0134] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0135] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A network base station deployment optimization method based on hard core distance and dynamic payment mechanism, characterized in that: include: Acquire a multi-cell communication network model, wherein the multi-cell communication network model includes a base station location distribution that complies with an HCPP process and a user location distribution that complies with a PPP process; According to the multi-cell communication network model, calculate the user's intra-cell interference and inter-cell interference; Calculate the system service coverage probability based on the user's intra-cell interference and inter-cell interference; Optimize network base station deployment based on system service coverage probability and dynamic payment mechanism; The step of calculating the intra-cell interference and inter-cell interference of a user according to a multi-cell communication network model specifically includes: According to the multi-cell communication network model, users with a retention probability that meets the hard core point process are screened out. The retention probability is the probability that the straight-line distance between two base station sites is greater than a preset distance. The calculation formula is: Where d is the length of the straight-line distance between two base stations greater than the preset distance; Pr(d) is the retention probability of the hard core point process; q is the correction factor; λ b is the base station density; According to the users with retention probability that meet the hard core point process, intra-cell interference and inter-cell interference are calculated respectively by Laplace transform; The Laplace transform calculation formula for intra-cell interference is: Where, γ is the signal-to-interference ratio threshold for users to successfully access the base station; d llk is the straight-line distance between the kth user in the lth cell and the base station in the lth cell; α is the path loss factor; λ u is the user density in the cell; sin c() is the Singer function; The Laplace transform calculation formula for inter-cell interference is: In the formula, when simplifying the parameters, let A is a random variable of Moment; H k Represents the square of the small-scale fading of the channel; is the mean number of users in each cell; R is the cell service radius; The calculating of the system service coverage probability according to the intra-cell interference and inter-cell interference of the user specifically includes: The signal-to-interference ratio threshold γ for users to successfully access the base station is used as the criterion for whether the coverage is obtained. The probability mother functional of the Poisson cluster process is combined, and the Laplace function of intra-cell interference and the Laplace function of inter-cell interference are substituted to calculate the system service coverage probability. The calculation formula is: In the formula, represents the expectation operation, and Substituting into the above formula, the system service coverage probability is: in: △ is a parameter determined by system parameters.

2. The method for optimizing network base station deployment based on hard-core distance and dynamic payment mechanism according to claim 1 is characterized in that: The dynamic payment mechanism includes the service provider giving a single user an uplink communication price according to the cell load; When the load is high, the service provider offers a higher uplink communication price to each user; When the load is low, the service provider will reduce the price of uplink communication between the user and the base station for each user; The dynamic payment mechanism adopts a hyperbolic growth function model, and the calculation formula is: Where z is the uplink communication price of a single user; p0 is the initial access price between the user and the base station; θ is the cell load threshold, n is a constant positive parameter that controls the slope, and m is the cell communication load.

3. The method for optimizing network base station deployment based on hard core distance and dynamic payment mechanism according to claim 2 is characterized in that: The optimization of network base station deployment according to the system service coverage probability and dynamic payment mechanism specifically includes: According to the system service coverage probability and dynamic payment mechanism, an optimization function model is established, and the calculation formula is: Where sc is the system area radius; B is the system bandwidth; is the penalty coefficient for the communication interruption event; a and b are the parameter conditions that the function model needs to meet respectively; Based on the particle swarm algorithm based on the flight characteristics of wild geese, the base station density under the lowest payment condition is calculated, and the base stations are deployed according to the base station density to complete the deployment optimization of network base stations.

4. A network base station deployment optimization system based on hard core distance and dynamic payment mechanism, characterized in that: include: Model acquisition module: used to acquire a multi-cell communication network model, wherein the multi-cell communication network model includes a base station location distribution that complies with the HCPP process and a user location distribution that complies with the PPP process; Cell interference calculation module: used to calculate the user's intra-cell interference and inter-cell interference according to the multi-cell communication network model; Coverage probability calculation module: used to calculate the system service coverage probability based on the user's intra-cell interference and inter-cell interference; Optimization module: used to optimize network base station deployment based on system service coverage probability and dynamic payment mechanism; The step of calculating the intra-cell interference and inter-cell interference of a user according to a multi-cell communication network model specifically includes: According to the multi-cell communication network model, users with a retention probability that meets the hard core point process are screened out. The retention probability is the probability that the straight-line distance between two base station sites is greater than a preset distance. The calculation formula is: Where d is the length of the straight-line distance between two base stations greater than the preset distance; Pr(d) is the retention probability of the hard core point process; q is the correction factor; λ b is the base station density; According to the users with retention probability that meet the hard core point process, intra-cell interference and inter-cell interference are calculated respectively by Laplace transform; The Laplace transform calculation formula for intra-cell interference is: Where, γ is the signal-to-interference ratio threshold for users to successfully access the base station; d llk is the straight-line distance between the kth user in the lth cell and the base station in the lth cell; α is the path loss factor; λ u is the user density in the cell; sin c() is the Singer function; The Laplace transform calculation formula for inter-cell interference is: In the formula, when simplifying the parameters, let A is a random variable of Moment; H k Represents the square of the small-scale fading of the channel; is the mean number of users in each cell; R is the cell service radius; The calculating of the system service coverage probability according to the intra-cell interference and inter-cell interference of the user specifically includes: The signal-to-interference ratio threshold γ for users to successfully access the base station is used as the criterion for whether the coverage is obtained. The probability mother functional of the Poisson cluster process is combined, and the Laplace function of intra-cell interference and the Laplace function of inter-cell interference are substituted to calculate the system service coverage probability. The calculation formula is: In the formula, represents the expectation operation, and Substituting into the above formula, the system service coverage probability is: in: △ is a parameter determined by system parameters.

5. A network base station deployment optimization device based on hard core distance and dynamic payment mechanism, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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