WO2024140507A1

WO2024140507A1 - Network coverage optimization method, electronic device, and storage medium

Info

Publication number: WO2024140507A1
Application number: PCT/CN2023/141252
Authority: WO
Inventors: 庞磊; 刘巧艳; 毛凯; 李建国; 王郭燕; 张劲超; 周先文
Original assignee: 中兴通讯股份有限公司
Priority date: 2022-12-28
Filing date: 2023-12-22
Publication date: 2024-07-04
Also published as: CN118265038A

Abstract

Provided are a network coverage optimization method, an electronic device, and a storage medium. The network coverage optimization method comprises: firstly, acquiring initial values of antenna parameters of a plurality of cells in a cell cluster to be optimized, and measurement information of a plurality of terminals in said cell cluster; and then on the basis of the initial values of the antenna parameters of the plurality of cells in said cell cluster, and the measurement information of the plurality of terminals, using a multi-objective optimization algorithm to solve for a multi-objective optimization problem related to network coverage to obtain a non-dominated solution set, wherein one non-dominated solution in the non-dominated solution set is used for indicating a target value of the antenna parameters of the plurality of cells in said cell cluster; and finally, selecting a target non-dominated solution from the non-dominated solution set, and setting the antenna parameters of the plurality of cells in said cell cluster on the basis of the target non-dominated solution.

Description

Network coverage optimization method, electronic device, and storage medium

This disclosure claims priority to Chinese patent application No. 202211699964.0, filed on December 28, 2022, the entire contents of which are incorporated by reference into this application.

Technical Field

The present disclosure relates to the field of communication technology, and in particular to a network coverage optimization method, an electronic device, and a storage medium.

Background technique

With the continuous development of wireless communication technology and the diversification of mobile services, users' requirements for the quality of wireless communication networks are gradually increasing. However, due to the increase in base station antennas, changes in the environment around base stations, and unreasonable planning and layout of base stations in the early stage, there will be areas with poor network coverage. Therefore, network coverage optimization is an important part of wireless communication network construction.

Summary of the invention

In one aspect, an embodiment of the present disclosure provides a network coverage optimization method. The network coverage optimization method includes:

Initial values of antenna parameters of multiple cells in the cell cluster to be optimized and measurement information of multiple terminals in the cell cluster to be optimized are obtained.

Furthermore, based on the initial values of antenna parameters of multiple cells in the cell cluster to be optimized and the measurement information of multiple terminals, a multi-objective optimization algorithm is used to solve the multi-objective optimization problem related to network coverage to obtain a non-dominated solution set, wherein a non-dominated solution in the non-dominated solution set is used to indicate the target values of antenna parameters of multiple cells in the cell cluster to be optimized.

Finally, a non-dominated solution is selected from the non-dominated solution set, and antenna parameters of multiple cells in the cell cluster to be optimized are set based on the selected non-dominated solution.

On the other hand, the embodiment of the present disclosure further provides a communication device. The communication device includes:

The acquisition module is used to acquire the initial values of antenna parameters of multiple cells in the cell cluster to be optimized and the measurement information of multiple terminals in the cell cluster to be optimized.

A processing module is used to solve a multi-objective optimization problem related to network coverage using a multi-objective optimization algorithm based on initial values of antenna parameters of multiple cells in the cell cluster to be optimized and measurement information of multiple terminals, and obtain a non-dominated solution set, wherein a non-dominated solution in the non-dominated solution set is used to indicate target values of antenna parameters of multiple cells in the cell cluster to be optimized.

The processing module is further used to select a non-dominated solution from the non-dominated solution set, and set antenna parameters of multiple cells in the cell cluster to be optimized based on the selected non-dominated solution.

On the other hand, an embodiment of the present disclosure also provides an electronic device, including: a memory and a processor; the memory and the processor are coupled; the memory is used to store a computer program; and the processor implements the network coverage optimization method described in any one of the above aspects when executing the computer program.

In another aspect, the embodiments of the present disclosure further provide a computer-readable storage medium having computer instructions stored thereon, which, when executed on an electronic device, enables the electronic device to execute the network coverage optimization method as described in any one of the above aspects.

In another aspect, the embodiments of the present disclosure further provide a computer program product, which includes computer program instructions, and when the computer program instructions are executed by a processor, the network coverage optimization method as described in any one of the above aspects is implemented.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used to provide further understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation on the technical solution of the present disclosure.

FIG. 1 is a schematic diagram of a communication system 10 according to some embodiments.

FIG2 is a schematic flow chart of a network coverage optimization method according to some embodiments.

FIG3 is a schematic flow chart of another network coverage optimization method according to some embodiments.

FIG. 4A is a schematic diagram of a multi-objective optimization process according to some embodiments.

FIG. 4B is a schematic diagram of another multi-objective optimization process according to some embodiments.

FIG. 4C is a schematic diagram of yet another multi-objective optimization process according to some embodiments.

FIG5 is a schematic flowchart of a multi-objective optimization process according to some embodiments.

FIG6 is a flowchart of another multi-objective optimization process according to some embodiments.

FIG7 is a schematic flow chart of yet another network coverage optimization method according to some embodiments.

FIG8 is a schematic diagram of the structure of a communication device according to some embodiments.

FIG. 9 is a schematic structural diagram of an electronic device according to some embodiments.

Detailed ways

In order to enable those skilled in the art to better understand the technical solutions of the embodiments of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present disclosure.

The terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present disclosure, unless otherwise specified, "plurality" means two or more.

In the embodiments of the present disclosure, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present disclosure should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

To facilitate understanding, some basic concepts of terms or technologies involved in the embodiments of the present disclosure are first briefly introduced and explained.

(1) Multi-objective optimization problem

Objective optimization problems generally refer to obtaining the optimal solution of an objective function through a certain optimization algorithm. If there is only one objective function to be optimized, the objective optimization problem in this case is called a single-objective optimization problem. If there are two or more objective functions to be optimized, the objective optimization problem in this case is called a multi-objective optimization problem.

For example, the multi-objective optimization problem can be expressed by the following formula (1):
F(x)＝((f ₁ (x)…...f _m (x)) ^TFormula (1)
subject to x∈Ω

f ₁ (x)…...f _m (x) all represent objective functions, and m is the number of objective functions to be optimized in the multi-objective optimization problem. x∈Ω represents the constraint condition, x represents the variable, and Ω represents the value space of x. F(x) represents the objective optimization result of the multi-objective optimization problem.

(2) Non-dominated solution

For multi-objective optimization problems, there is usually a solution set, and the solutions in the solution set cannot be compared in terms of all objective functions. It can be understood that one characteristic of the solution of a multi-objective optimization problem is that it is impossible to improve any objective function without weakening at least one other objective function. In other words, when improving any objective function, at least one other objective function will inevitably be weakened. Therefore, solutions with this characteristic are called non-dominated solutions.

"Dominate" refers to a method of comparing solutions. If a solution A is not worse than another solution B in all objective functions, and solution A is better than solution B in at least one objective function, then it can be considered that solution A dominates solution B. If a solution cannot be dominated by any other solution, then this solution is called a non-dominated solution. If a solution is dominated by any other solution, then this solution is called a dominated solution. Therefore, the solution to a multi-objective optimization problem is called a non-dominated solution, and accordingly, the solution to a multi-objective optimization problem is called a non-dominated solution. The set can be called the non-dominated solution set.

In some embodiments, a non-dominated solution may also be referred to as a Pareto solution, an optimal solution, etc., without limitation thereto.

(3) Weighted summation method

The weighted summation method assumes that the multi-objective optimization problem to be optimized has m objective functions, and its aggregation function is expressed by a non-negative weight vector The weighting applied to each objective transforms the multi-objective optimization problem into multiple single-objective sub-problems.

Exemplarily, the aggregation function of the weighted sum method can be expressed as the following formula (2):

in, is a set of weight vectors, for all i=1,2,…,m,

(4) Chebyshev method

Exemplarily, the aggregation function of the Chebyshev method can be expressed as the following formula (3):

in, is the reference point, z ₁ is the reference point corresponding to the objective function f ₁ (x), and z _m is the reference point corresponding to the objective function f _m (x). For each i = 1,…,m, we have

λ ^j is the weight vector,

The above is an introduction to the technical terms involved in the embodiments of the present disclosure, which will not be repeated below.

At present, for areas with poor network coverage quality, in addition to adjusting the relevant parameters of the cell based on the experience of engineers, single-target optimization can be performed on coverage problems such as overlapping coverage, weak coverage, over-coverage, and poor signal to interference plus noise ratio (SINR). However, this optimization method is not perfect. Taking Massive Multiple Input Multiple Output (Massive MIMO) as an example, Massive MIMO can flexibly adjust multiple dimensions such as horizontal lobe width, vertical lobe width, beam direction angle, downtilt angle, and number of beams. In each dimension, it can also be fine-tuned by setting a reasonable step size. In this way, the network capacity and three-dimensional depth coverage in various complex scenarios can be greatly improved. However, due to factors such as the low completeness of the antenna parameter library, complex scenario requirements, and diverse building forms, there will still be network coverage problems such as weak coverage, overlapping coverage, over-coverage, and poor SINR quality. In addition, the above-mentioned coverage problems are often intertwined and coupled. Therefore, a single optimization method for network coverage cannot achieve good solutions.

In view of this, the present disclosure provides a network coverage optimization method, which uses a multi-objective evolutionary algorithm to first obtain the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized, as well as the measurement information of multiple terminals in the cell cluster to be optimized. Then, based on the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized, as well as the measurement information of multiple terminals, a multi-objective optimization algorithm is used to solve the multi-objective optimization problem related to network coverage, and a non-dominated solution set is obtained, wherein a non-dominated solution in the non-dominated solution set is used to indicate the target value of the antenna parameters of multiple cells in the cell cluster to be optimized. Finally, a non-dominated solution is selected from the non-dominated solution set, and the antenna parameters of multiple cells in the cell cluster to be optimized are set based on the selected non-dominated solution. In this way, a large amount of manpower input is reduced and the network deployment efficiency is improved.

The implementation of the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

The method provided by the embodiment of the present disclosure can be applied to various communication systems. For example, the communication system can be a long term evolution (LTE) system, a fifth generation (5G) communication system, a Wi-Fi system, a communication system related to the third generation partnership project (3GPP), a future evolving communication system (such as: a sixth generation (6G) communication system, etc.), or a system integrating multiple systems, etc., which are not limited here. The method provided by the embodiment of the present disclosure is described below by taking the communication system 10 shown in Figure 1 as an example. Figure 1 is only a schematic diagram and does not constitute a limitation on the applicable scenarios of the technical solution provided by the present disclosure.

As shown in FIG1 , it is a schematic diagram of the architecture of a communication system 10 provided in an embodiment of the present disclosure. In FIG1 , the communication system 10 may include a network Device 101, and terminal 102 and terminal 103 communicating with network device 101. In some embodiments, communication system 10 further includes computing device 104 communicating with network device 101. In some embodiments, communication system 10 further includes network device 105 communicating with network device 101 or computing device 104, and terminal 106 and terminal 107 communicating with network device 105.

In FIG1 , the network device can provide wireless access services for the terminal. In some embodiments, each network device corresponds to a service coverage area, and the terminal entering the area can communicate with the network device to receive the wireless access service provided by the network device. In some embodiments, the service coverage area may include one or more cells. For example, the service coverage area corresponding to the network device 101 includes cell 1 and cell 2, the terminal 102 accesses the network device 101 through cell 1, and the terminal 103 accesses the network device 101 through cell 2.

The network devices in the embodiments of the present disclosure, such as network device 101 or network device 105, can be any device with wireless transceiver functions, for example, a base station in LTE, a base station in new radio (NR), or a base station subsequently evolved by 3GPP.

The terminal in the embodiments of the present disclosure, for example, terminal 102, terminal 103, terminal 106 or terminal 107 is any device with wireless transceiver function. For example, the terminal is a handheld device with wireless communication function (such as a mobile phone or tablet computer, etc.), a vehicle-mounted device, a wearable device, a terminal or a computing device in an Internet of Things (IoT) system, etc. The terminal can also be called a terminal device or a user equipment (UE), which is not limited here.

The computing device 104 in Fig. 1 may be any device having communication capability and computing capability, for example, a server, a computer, or a cloud server.

The communication system 10 shown in FIG1 is only used as an example and is not used to limit the technical solution of the present disclosure. Those skilled in the art should understand that in some implementations, the communication system 10 may also include other devices, and the number of network devices, terminals or computing devices may also be determined according to some needs, which is not limited here.

The embodiment of the present disclosure also provides an electronic device, which is the executor of the above-mentioned network coverage optimization method. The electronic device is an electronic device with data processing capabilities. For example, the electronic device can be a computing device in the above-mentioned communication system 10, or the electronic device is a functional module in the computing device 104, or the electronic device can be any computing device connected to the computing device 104, etc. Of course, the electronic device can also be the above-mentioned network device, which is not limited in the embodiment of the present disclosure.

As shown in FIG. 2 , a network coverage optimization method is provided in an embodiment of the present disclosure. The method may include S101 - S103 .

S101: Acquire initial values of antenna parameters of multiple cells in a cell cluster to be optimized and measurement information of multiple terminals in the cell cluster to be optimized.

The antenna parameters of a cell are important parameters that affect the network coverage of the cell. Usually in practical applications, different cell antenna parameters can be set based on network coverage requirements of different scenarios (eg, macro coverage scenario and high-rise building coverage scenario).

In some embodiments, the antenna parameters of the above-mentioned cell may include at least one of azimuth, downtilt, horizontal beamwidth, and vertical beamwidth. The azimuth of a cell is the angle between the main network coverage direction of the cell and the horizontal normal direction of the cell. The downtilt of a cell is the angle between the main network coverage direction of the cell and the vertical normal direction of the cell. The horizontal beamwidth refers to the beamwidth of the antenna in the horizontal plane. The vertical beamwidth refers to the beamwidth of the antenna in the vertical plane.

It should be noted that the wider the horizontal beam width, the better the coverage at the sector junction, but when the antenna tilt angle increases, beam distortion is more likely to occur, resulting in cross-area coverage. The narrower the vertical beam width, the faster the signal attenuation when deviating from the main beam direction, and the easier it is to accurately control the coverage range by adjusting the antenna tilt angle.

In some embodiments, the measurement information of the terminal includes signal qualities of multiple cells in the cell cluster to be optimized measured by the terminal. For example, the signal quality can be represented by parameters such as reference signal receiving power (RSRP), which is not limited to this.

In some embodiments, the measurement information of the terminal may also include direction of the angle (DOA) information, and the DOA information may include horizontal direction of the angle (H-DOA) information and vertical direction of the angle (H-DOA) information. of the angle, V-DOA) information.

It should be understood that the measurement information of multiple terminals in the embodiments of the present disclosure can be used to calculate coverage index parameters related to multiple cells in the cell cluster to be optimized, such as heavy coverage rate, weak coverage rate, etc. These coverage index parameters can reflect the severity of the coverage problem of the cell.

S102: Based on the initial values of antenna parameters of multiple cells in the cell cluster to be optimized and the measurement information of multiple terminals, a multi-objective optimization algorithm is used to solve a multi-objective optimization problem related to network coverage to obtain a non-dominated solution set.

In some embodiments, the non-dominated solution set includes one or more non-dominated solutions, and the non-dominated solutions are used to indicate target values of antenna parameters of multiple cells in the cell cluster to be optimized.

For example, taking a cell cluster to be optimized including l cells as an example, the above non-dominated solution can be expressed as the following formula (4):
W＝[Wcell ₀ ,Wcell ₁ ,Wcell ₂ ...Wcell _l-1 ] Formula (4)

Wherein, Wcell ₀ , Wcell ₁ , Wcell ₂ , ..., Wcell _1-1 are target values of antenna parameters of each cell in l cells respectively.

In some embodiments, the multi-objective optimization problem is determined according to multiple of the following objective functions:

The objective function is to minimize the overlap coverage as the optimization goal;

The objective function is to minimize the weak coverage rate as the optimization goal;

The objective function is to minimize the signal-to-noise ratio quality difference;

The objective function is to minimize the cross-area coverage rate as the optimization goal;

The objective function is to maximize the signal-to-noise ratio;

An objective function whose optimization goal is to maximize signal quality;

The objective function is to maximize the signal-to-interference-noise ratio;

An objective function whose optimization goal is to maximize the transmission rate; and

The objective function is to maximize the split ratio.

The split ratio is used to characterize the ratio between the traffic of the target service and the traffic of all services. For example, the target service may be a 5G service.

It should be understood that the above objective function is only an example, and when performing coverage optimization on the cell cluster to be optimized, other objective functions related to coverage issues may also be considered, and there is no limitation to this.

In some embodiments, the multi-objective optimization problem can be solved by a decomposition-based multi-objective optimization algorithm, a non-dominated sorting genetic algorithm (NSGA-II), or a multi-objective evolutionary algorithm, etc., without limitation.

For example, step S102 can be implemented as follows: based on the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized and the measurement information of the multiple terminals, the multi-objective optimization problem is solved by a decomposition-based multi-objective optimization algorithm to obtain the non-dominated solution set; wherein the decomposition dimension of the decomposition-based multi-objective optimization algorithm is determined according to the number of cells included in the cell cluster to be optimized and the number of objective functions related to the multi-objective optimization problem.

S103: Select a target non-dominated solution from the set of non-dominated solutions, and set antenna parameters of multiple cells in the cell cluster to be optimized based on the target non-dominated solution.

In some embodiments, the most critical objective function can be determined based on the main cause of the coverage problem of the cell cluster to be optimized; each non-dominated solution in the non-dominated solution set is traversed to calculate the function value of the most critical objective function corresponding to each non-dominated solution; and the non-dominated solution corresponding to the optimal function value is selected from the function value of the most critical objective function corresponding to each non-dominated solution as the target non-dominated solution.

For example, the main reason for the coverage problem of the cell cluster to be optimized is that the overlapping coverage ratio is too high, so the most critical objective function is the objective function that takes minimizing the overlapping coverage ratio as the optimization target.

In actual use, based on the current network coverage demand scenario (for example, a high-rise building scenario), a target non-dominated solution can be selected from the determined non-dominated solution set to make the network coverage optimization result adapt to the scenario to meet the network coverage requirements in different user scenarios.

In the disclosed embodiment, the technical solution combines the optimization requirements of the actual wireless network environment to perform multi-objective optimization on the actual network coverage problem, which can obtain optimization results that are more suitable for the environment more quickly. Moreover, compared with the traditional method of adjusting based on expert experience, it reduces a lot of manpower investment and improves network deployment efficiency.

In some embodiments, when a decomposition-based multi-objective optimization algorithm is used to solve a multi-objective optimization problem, as shown in FIG. 3 , step S102 may be implemented as the following S1021 - S1022 .

S1021. Decompose the multi-objective optimization problem into multiple single-objective sub-problems, and configure a corresponding population for each of the multiple single-objective sub-problems.

In some embodiments, multiple weight vectors are initialized and set, and the multi-objective optimization problem can be decomposed into multiple single-objective sub-problems according to the multiple weight vectors. Each weight vector corresponds to a single-objective sub-problem.

As an example, based on the number of optimization targets S, an S-dimensional space is formed. Uniform sampling is performed on the S-dimensional space, and the number of samples H in each target direction is determined. 1/H is defined as the sampling step length. Then the number of S-dimensional uniform sampling weight vectors is

In some embodiments, for each weight vector, the distance between the weight vector and other weight vectors can be calculated, and then the T weight vectors with the closest distance are selected as the adjacent weight vectors of the weight vector. Thus, the single-objective subproblems corresponding to the T weight vectors are the adjacent subproblems of the single-objective subproblem corresponding to the weight vector. It should be noted that only adjacent subproblems can be used to optimize each other.

S1022. Based on the initial values of antenna parameters of multiple cells in the cell cluster to be optimized, the measurement information of multiple terminals, and the aggregation function of the decomposition-based multi-objective optimization algorithm, iteratively optimize the population corresponding to each single-objective sub-problem in multiple single-objective sub-problems until the iteration stopping condition is met and stop and output a non-dominated solution set.

Exemplarily, the iteration stopping condition may be that the number of iterations reaches a preset number.

In some embodiments, during the iterative optimization process, an external population is provided to store non-dominated solutions. Therefore, a non-dominated solution set can be extracted from the external population when the iteration stops.

In some embodiments, as shown in FIG4A , the space spanned by the multi-objective optimization problem can first be uniformly sampled with a certain sampling step size, so as to facilitate the parallel decomposition of the multi-objective optimization problem into the sum of single-objective sub-problems corresponding to multiple weight vectors. In FIG4A , F1 and F2 represent the above-mentioned objective functions, and w1, w2... and w9 represent 9 sampling points. Secondly, as shown in FIG4B , the problem of finding an approximate solution to the Pareto frontier can be converted into a set of scalar optimization problems based on an aggregation function. Finally, as shown in FIG4C , a new solution in the neighborhood is used for iterative optimization, and finally converges to the Pareto frontier.

In some embodiments, as shown in FIG5 , an iterative optimization process of a population corresponding to a single-objective subproblem includes S201 - S205 .

S201. Based on the candidate value set of antenna parameters, a genetic variation mechanism is used to generate offspring individuals in the population corresponding to the single-objective subproblem.

In some embodiments, a sequence number is randomly selected, a genetic variation operation is performed on the sequence number to generate a new sequence number, a candidate value corresponding to the new sequence number is extracted from a candidate value set, and the candidate value corresponding to the new sequence number is used as the offspring individual y′.

S202: Determine m objective function values corresponding to the offspring individuals based on initial values of antenna parameters of multiple cells in the cell cluster to be optimized, measurement information of multiple terminals, and offspring individuals.

In some embodiments, based on the initial values of antenna parameters of multiple cells in the cell cluster to be optimized and the offspring individuals, the measurement information of multiple terminals is updated respectively to obtain updated measurement information of the multiple terminals. Then, based on the updated measurement information of the multiple terminals, the function values of the m objective functions corresponding to the offspring individuals are determined.

The updated measurement information of the terminal includes updated signal qualities of multiple cells in the cell cluster to be optimized.

In some embodiments, for the measurement information of each terminal, the signal quality of the target cell in the cell cluster to be optimized is updated in the following manner:

According to the initial value of the antenna parameter of the target cell, the initial antenna gain corresponding to the target cell is determined; according to the target value of the antenna parameter of the target cell indicated by the child individual, the target antenna gain corresponding to the target cell is determined; according to the signal quality of the target cell and the corresponding The initial antenna gain and the target antenna gain corresponding to the target cell determine the updated signal quality of the target cell.

Exemplarily, assuming that the signal quality report of the serving cell corresponding to the antenna parameter k of UEi is counted as RSRPi,k, and the DOA angle corresponding to the UE is (h, v), then when the new antenna parameter j is selected, the calculation method of RSRPi,j of the UE is as shown in the following formula (5):
RSRPi,j＝RSRPi,k+AntGainTbl[j][h][v]-AntGainTbl[k][h][v] Formula (5)

AntGainTbl is the saved 3D antenna gain table.

S203. Determine the aggregate function value corresponding to the offspring individual based on the m objective function values corresponding to the offspring individual and the aggregate function of the decomposition-based multi-objective optimization algorithm.

In some embodiments, the aggregation function of the decomposition-based multi-objective optimization algorithm can be determined according to the aggregation function of the weighted sum method and the aggregation function of the Chebyshev method. In this way, the aggregation function of the decomposition-based multi-objective optimization algorithm can have the characteristics of fast convergence speed of the weighted sum method and good distribution of the Chebyshev method.

Exemplarily, the aggregation function of the decomposition-based multi-objective optimization algorithm can be expressed as:

Among them, x∈Ω, As the reference point, for each i＝1,…,m, there is is a set of weight vectors, for all i=1,2,…,m, P is a preset constant.

S204. Based on the aggregation function value corresponding to the offspring individual, determine whether to replace the individual in the population corresponding to the adjacent subproblem of the single-objective subproblem with the offspring individual.

In some embodiments, if the aggregation function value corresponding to the offspring individual is less than or equal to the aggregation function value corresponding to the individual in the population corresponding to the adjacent subproblem, the offspring individual is used to replace the individual in the population corresponding to the adjacent subproblem. Alternatively, if the aggregation function value corresponding to the offspring individual is greater than the aggregation function value corresponding to the individual in the population corresponding to the adjacent subproblem, the offspring individual is not used to replace the individual in the population corresponding to the adjacent subproblem.

S205. Based on the m objective function values corresponding to the offspring individuals, remove the solutions dominated by the offspring individuals in the external population; and, if the offspring individuals are not dominated by the solutions in the external population, add the offspring individuals to the external population. The external population is used to store non-dominated solutions.

The following is a complete introduction to the process of solving the multi-objective optimization problem with some examples. As shown in Figure 6, the process of solving the multi-objective optimization problem may include the following S301-S309.

S301, initializing and setting N weight vectors, and decomposing the multi-objective optimization problem into N single-objective sub-problems according to the N weight vectors.

In some embodiments, the proximity between two weight vectors is measured by the Euclidean distance between the two weight vectors. Based on this, the set B(i) is defined to contain the T closest index vectors to the weight vector λ ⁱ . It should be understood that since the weight vector closest to the weight vector λ ⁱ is itself, i∈B(i). If j∈B(i), the jth subproblem can be regarded as a neighboring subproblem of the ith subproblem.

S302: Initialize and set corresponding populations for N single-objective subproblems.

In some embodiments, individuals in the population may be generated by random sampling from a set of candidate values of the antenna parameters.

S303: Initialize m reference points of the objective function.

In some embodiments, for each objective function, an optimal value may be selected from the function values of the objective functions corresponding to each individual in the population corresponding to the N single-objective sub-problems as a reference point.

S304: Initialize the external population.

In some embodiments, the external population is an empty set after initialization.

S305. Based on the candidate value set of the antenna parameters, a genetic variation mechanism is used to generate offspring individuals in the population corresponding to the single-objective subproblem.

S306: Based on the offspring individuals, determine whether to update the reference points of the m objective functions.

In some embodiments, the function values of m objective functions corresponding to the offspring individuals can be calculated first; for each objective function, if the function value of the objective function corresponding to the offspring individual is better than the reference point of the objective function, the function value of the objective function corresponding to the offspring individual is used as the new reference point of the objective function.

Exemplarily, it can be expressed in the form of a formula: for each j=1, ..., m, if z _i <f _j (y'), it can be set that z _i =f _j (y').

S307, updating the neighborhood solution (ie, updating the individuals in the population corresponding to the adjacent subproblems).

In some embodiments, the neighborhood solution is updated based on the Chebyshev criterion: for each j∈B(i), if ^gAT (y′| ^λj ,z) ^≤gAT ( ^xj | ^λj ,z), let ^xj =y′, ^FVj =F(y′).

Where FV ⁱ is the objective function value of ^xi , FV ⁱ = F( ^xi ), in a population of size N, ^x1 ,…, ^xN∈Ω , where ^xi is the current solution to the i-th subproblem.

In some embodiments, the penalty-based boundary crossing method updates the neighborhood solution: for each j∈B(i), if ^gpbi (y′| ^λj ,z) ^≤gpbi ( ^xj | ^λj ,z), let ^xj =y′, ^FVj =F(y′)
ming ^pbi (x|λ,z ^* )＝ _d1 + _θd2

d ₂ = |F(x)-(z ^* -d ₁ λ)|

Among them, θ is the preset penalty parameter.

S308: Update the external population.

S309, repeat steps S305-S308 until the iteration stop condition is met.

In some embodiments, as shown in FIG. 7 , the present disclosure also provides a flow chart of another network coverage optimization method, which further includes S401 - S402 based on the method described in FIG. 1 .

S401. Obtain coverage problem attribute parameters and location information of each cell among multiple cells.

The coverage problem attribute parameters of a cell are used to characterize the severity of the network coverage problem existing in the cell.

In some embodiments, the coverage problem attribute parameter of the cell is determined according to at least one of the overlapping coverage rate, weak coverage rate, signal-to-noise ratio quality difference and cross-area coverage rate of the cell.

In some embodiments, the problem attribute parameters of a cell may include the severity ratio of each problem of the cell, such as the severity ratio of overlapping coverage problems, the severity ratio of weak coverage problems, etc. Of course, the problem corresponding to the largest severity ratio is the network coverage problem that needs to be optimized most in the cell.

The severity ratio of the overlapping coverage problem can be determined based on the ratio of the overlapping coverage rate of the cell to the sum of the overlapping coverage rate, weak coverage rate, signal-to-noise ratio quality difference and cross-area coverage rate of the cell. For example, the severity ratio of the overlapping coverage problem can be expressed as: overlapping coverage rate/(overlapping coverage rate+weak coverage rate+signal-to-noise ratio quality difference+cross-area coverage rate).

Accordingly, the severity ratio of the weak coverage problem can be determined based on the ratio of the weak coverage rate of the cell to the sum of the overlapping coverage rate, weak coverage rate, signal-to-noise ratio quality difference and cross-area coverage rate of the cell. For example, the severity ratio of the weak coverage problem can be expressed as: weak coverage rate/(overlapping coverage rate+weak coverage rate+signal-to-noise ratio quality difference+cross-area coverage rate).

The severity ratio of the signal-to-noise ratio quality problem can be determined based on the ratio of the signal-to-noise ratio quality difference of the cell to the sum of the overlapping coverage rate, weak coverage rate, signal-to-noise ratio quality difference and cross-area coverage rate of the cell. For example, the severity ratio of the signal-to-noise ratio quality problem can be expressed as: signal-to-noise ratio quality difference/(overlapping coverage rate+weak coverage rate+signal-to-noise ratio quality difference+cross-area coverage rate).

The severity of the cross-area coverage problem can be based on the cross-area coverage rate of the cell to the overlapping coverage rate, weak coverage rate, poor signal-to-noise ratio of the cell. For example, the severity ratio of the cross-area coverage problem can be expressed as: cross-area coverage ratio/(overlapping coverage ratio+weak coverage ratio+signal-to-noise ratio quality difference+cross-area coverage ratio).

The following is an introduction to the overlapping coverage rate, weak coverage rate, signal-to-noise ratio quality difference and cross-area coverage rate of the cell:

Overlap coverage

The overlapping coverage of a cell is determined according to the ratio between the number of over-covered sampling points in the cell and the total number of sampling points in the cell.

Exemplarily, the overlapping coverage ratio α of the cell can be expressed as the following formula (6):

In some embodiments, the re-covered sampling point is a sampling point that satisfies the following conditions:

The measured signal quality of the cell is greater than or equal to a first signal quality threshold;

The measured signal quality of the neighboring cell of the cell is greater than or equal to the second signal quality threshold; and

A difference between the measured signal quality of the cell and the measured signal quality of a neighboring cell of the cell is greater than or equal to a third signal quality threshold.

Exemplarily, the value range of the first signal quality threshold and the second signal quality threshold may be [-156dBm, -31dBm].

If the number of overlapping coverage sampling points is not less than the overlapping coverage sampling point threshold and the overlapping coverage sampling point ratio is not less than the overlapping coverage sampling point ratio threshold, the current serving cell can be marked as a primary overlapping coverage cell, and its neighboring cell can be marked as a secondary overlapping coverage cell.

(2) Weak coverage

The weak coverage rate of a cell is determined according to the ratio between the number of weak coverage sampling points in the cell and the total number of sampling points in the cell.

Exemplarily, the weak coverage rate β of a cell can be expressed as the following formula (7):

In some embodiments, the weak coverage sampling point is a sampling point at which the measured signal quality of the cell is less than or equal to a fourth signal quality threshold.

Exemplarily, the value range of the fourth signal quality threshold may be [-156dBm, -31dBm].

If the number of weak coverage sampling points is not less than the weak coverage sampling point threshold and the proportion of weak coverage sampling points is not less than the weak coverage sampling point proportion threshold, the cell is marked as a weak coverage cell, indicating that the network coverage problem that most needs to be optimized in this cell is weak coverage.

(3) Signal-to-noise ratio quality

The signal-to-noise ratio quality difference of a cell is determined according to the ratio between the number of signal-to-noise ratio quality difference sampling points in the cell and the total number of sampling points in the cell.

Exemplarily, the signal-to-noise ratio quality difference γ of the cell can be expressed as the following formula (8):

In some embodiments, the signal-to-noise ratio poor quality sampling point is a sampling point at which the measured signal-to-noise ratio of the cell is less than or equal to a signal-to-noise ratio poor quality threshold.

If the number of poor signal-to-noise ratio sampling points is not less than the poor signal-to-noise ratio sampling point threshold and the proportion of poor signal-to-noise ratio sampling points is not less than the poor signal-to-noise ratio sampling point proportion threshold, the cell is marked as a poor signal-to-noise ratio cell, indicating that the network coverage problem that most needs to be optimized in this cell is the poor signal-to-noise ratio problem.

In some embodiments, the signal-to-noise ratio of the serving cell at the sampling point may be obtained by offline calculation. Exemplarily, the UE SINR calculation method may be expressed as the following formula (9):

Based on the noise coefficient of the user equipment (UE) receiver, the white noise power can be determined to be -125dBm. In addition, RSRP needs to be converted into a linear value before calculation.

In addition, SINR calculation needs to be protected by maximum/minimum values, and its value range can be set to SINR∈[-20dBm,40dBm].

(4) Cross-area coverage

The cross-area coverage rate of a cell is determined according to the ratio between the cross-area coverage sampling points of the cell and the total number of sampling points of the cell.

Exemplarily, the cross-cell coverage rate φ of a cell can be expressed as the following formula (10):

In some embodiments, the out-of-coverage sampling point is a sampling point located outside the planned coverage of the cell and where the signal quality of the cell is measured to be greater than a fifth signal quality threshold.

In addition, the above-mentioned location information may be the longitude and latitude of each cell. The longitude and latitude of each cell may be determined from the public reference information of each cell.

S402: Cluster the multiple cells according to the coverage problem attribute parameters and location information of each cell in the multiple cells to obtain one or more cell clusters to be optimized.

In some embodiments, a plurality of cells may be clustered using density-based spatial clustering of applications with noise (DBSCAN). This clustering method can define a cluster as the largest set of density-connected points, can divide areas with sufficiently high density into clusters, and can find clusters of arbitrary shapes in a spatial database of noise.

DBSCAN needs to determine E neighborhoods and core objects.

E neighborhood: The area within a radius E of a given object is called the E neighborhood of the object. The neighborhood radius can be expressed as Eps, and the neighborhood radius can be a preset radius value set manually or determined by other possible methods.

Core object: If the number of sample points in the neighborhood of a given object E is greater than or equal to MinPts, then the object is called a core object. MinPts can be a value set manually or determined by other possible methods.

Step 1. Input data: cell problem attribute dataset D, MinPts and Eps.

Step 2: Define core samples, boundary samples and noise samples.

In some embodiments, sampling points whose number of sampling points in the neighborhood is greater than MinPts can be defined as core samples. Sampling points whose number of sampling points in the neighborhood is less than MinPts can be defined as boundary samples. Samples in the neighborhood that do not belong to any core sample are defined as noise samples.

Step 3: Mark all sample points as unvisited, randomly select an unvisited sample p, and mark it as visited.

In some embodiments, it can be determined that the E neighborhood of sample p has at least MinPts samples.

If the E neighborhood of sample p has at least MinPts samples, execute the following step 4.

Otherwise, if the E neighborhood of sample p does not have MinPts samples, execute the following step 5.

Step 4: Create a new cluster C and add p to C. Let N be the set of samples in the E neighborhood of p, traverse all samples p ^' in the set, and if p ^' has not been visited and there are at least MinPts samples in the E neighborhood of p ^' , add these objects to N; if p ^' is not yet a member of any cluster, add p ^' to C.

Step 5: Mark sample p as a noise sample.

Step 6: Traverse all objects until there are no objects marked as unvisited.

In this way, one or more problem area clusters (ie, cell clusters to be optimized) are obtained based on the coverage problem density, and the above network coverage optimization method can use the cluster as a basic optimization unit.

In some embodiments, in addition to the DBSCAN clustering algorithm, other possible clustering methods may be used, such as the K-means clustering (Kmeans) algorithm, which is not limited thereto.

In the disclosed embodiment, a plurality of cells having coverage problems and being geographically adjacent can be divided into the same cell cluster to be optimized by clustering method, so as to facilitate the subsequent use of a multi-objective optimization algorithm to optimize the coverage problem of the cell cluster to be optimized and improve the optimization efficiency.

It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and/or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the algorithm steps of each example described in the embodiments of the present disclosure, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present disclosure.

The disclosed embodiment can divide the electronic device into functional modules according to the above method embodiment. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one functional module. The above integrated module can be implemented in the form of hardware or software. It should be noted that the division of modules in the disclosed embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation. The following is an example of dividing each functional module corresponding to each function.

FIG8 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure, and the electronic device can execute the network coverage optimization method provided by the above method embodiment. As shown in FIG8 , a communication device 800 includes an acquisition module 801 and a processing module 802 .

The acquisition module 801 is used to acquire initial values of antenna parameters of multiple cells in the cell cluster to be optimized and measurement information of multiple terminals in the cell cluster to be optimized.

The processing module 802 is used to solve the multi-objective optimization problem related to network coverage based on the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized and the measurement information of multiple terminals using a multi-objective optimization algorithm to obtain a non-dominated solution set, where a non-dominated solution in the non-dominated solution set is used to indicate the target values of the antenna parameters of multiple cells in the cell cluster to be optimized.

The processing module 802 is further configured to select a non-dominated solution from the non-dominated solution set, and set antenna parameters of multiple cells in the cell cluster to be optimized based on the selected non-dominated solution.

In some embodiments, the above-mentioned multi-objective optimization problem is determined according to multiple items of the following objective functions: an objective function with minimizing overlapping coverage as an optimization target; an objective function with minimizing weak coverage as an optimization target; an objective function with minimizing signal-to-noise ratio quality difference as an optimization target; an objective function with minimizing cross-zone coverage as an optimization target; an objective function with maximizing signal-to-noise ratio as an optimization target; an objective function with maximizing signal quality as an optimization target; an objective function with maximizing signal-to-interference-plus-noise ratio as an optimization target; an objective function with maximizing transmission rate as an optimization target; and an objective function with maximizing the diversion ratio as an optimization target, wherein the diversion ratio is used to characterize the ratio between the traffic of the target service and the traffic of all services.

In some embodiments, the processing module 802 is used to: solve the multi-objective optimization problem based on the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized and the measurement information of multiple terminals using a decomposition-based multi-objective optimization algorithm to obtain a non-dominated solution set; wherein the decomposition dimension of the decomposition-based multi-objective optimization algorithm is determined according to the number of cells included in the cell cluster to be optimized and the number of objective functions related to the multi-objective optimization problem.

In some embodiments, the processing module 802 is used to: decompose the multi-objective optimization problem into multiple single-objective sub-problems, configure a corresponding population for each of the multiple single-objective sub-problems; based on the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized, the measurement information of multiple terminals, and the aggregation function of the decomposed multi-objective optimization algorithm, for each of the multiple single-objective sub-problems corresponding to the population; The population is iterated and optimized until the iteration stopping condition is met and the non-dominated solution set is output.

In some embodiments, the aggregation function of the decomposition-based multi-objective optimization algorithm is determined according to the aggregation function of the weighted sum method and the aggregation function of the Chebyshev method.

In some embodiments, the processing module 802 is used to: generate offspring individuals in a population corresponding to a single-objective subproblem based on a set of candidate values of antenna parameters using a genetic variation mechanism; determine m objective function values corresponding to the offspring individuals based on the initial values of antenna parameters of multiple cells in the cell cluster to be optimized, measurement information of multiple terminals, and offspring individuals; determine the aggregate function value corresponding to the offspring individuals based on the m objective function values corresponding to the offspring individuals and the aggregate function of the decomposed multi-objective optimization algorithm; determine whether to replace individuals in a population corresponding to a neighboring subproblem of the single-objective subproblem with offspring individuals based on the aggregate function value corresponding to the offspring individuals; remove solutions dominated by offspring individuals in an external population based on the m objective function values corresponding to the offspring individuals; and, if the offspring individuals are not dominated by solutions in an external population, add the offspring individuals to the external population; wherein the external population is used to store non-dominated solutions.

In some embodiments, the processing module 802 is used to: update the measurement information of multiple terminals respectively according to the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized and the offspring individuals to obtain updated measurement information of the multiple terminals; determine the function values of m objective functions corresponding to the offspring individuals according to the updated measurement information of the multiple terminals.

In some embodiments, the measurement information of the terminal includes signal qualities of multiple cells in the cell cluster to be optimized; and the updated measurement information of the terminal includes updated signal qualities of multiple cells in the cell cluster to be optimized.

In some embodiments, for the measurement information of each terminal, the signal quality of the target cell in the cell cluster to be optimized is updated in the following manner: based on the initial value of the antenna parameter of the target cell, the initial antenna gain corresponding to the target cell is determined; based on the target value of the antenna parameter of the target cell indicated by the descendant individual, the target antenna gain corresponding to the target cell is determined; based on the signal quality of the target cell, the initial antenna gain corresponding to the target cell, and the target antenna gain corresponding to the target cell, the updated signal quality of the target cell is determined.

In some embodiments, the antenna parameters include at least one of an azimuth angle, a downtilt angle, a horizontal beamwidth, and a vertical beamwidth.

In some embodiments, the processing module 802 is also used to: obtain coverage problem attribute parameters and location information of each cell in a plurality of cells, where the coverage problem attribute parameters of the cell are used to characterize the severity of the network coverage problem existing in the cell; cluster the plurality of cells according to the coverage problem attribute parameters and location information of each cell in the plurality of cells to obtain one or more cell clusters to be optimized.

In some embodiments, the overlapping coverage rate of a cell is determined based on the ratio between the number of heavy coverage sampling points in the cell and the total number of sampling points in the cell; wherein the heavy coverage sampling points are sampling points that satisfy the following conditions: the measured signal quality of the cell is greater than or equal to a first signal quality threshold; the measured signal quality of a neighboring cell of the cell is greater than or equal to a second signal quality threshold; and the difference between the measured signal quality of the cell and the measured signal quality of the neighboring cell of the cell is greater than or equal to a third signal quality threshold.

In some embodiments, the weak coverage rate of a cell is determined based on the ratio between the number of weak coverage sampling points in the cell and the total number of sampling points in the cell; wherein the weak coverage sampling points are sampling points where the measured signal quality of the cell is less than or equal to a fourth signal quality threshold.

In some embodiments, the signal-to-noise ratio quality difference of a cell is determined based on the ratio between the number of signal-to-noise ratio quality difference sampling points in the cell and the total number of sampling points in the cell; the signal-to-noise ratio quality difference sampling points are sampling points where the measured signal-to-noise ratio of the cell is less than or equal to the signal-to-noise ratio quality difference threshold.

In some embodiments, the cross-cell coverage rate of a cell is determined based on the ratio between the cross-cell coverage sampling points of the cell and the total number of sampling points of the cell; wherein the cross-cell coverage sampling points are sampling points located outside the planned coverage range of the cell and at which the signal quality of the cell is measured to be greater than a fifth signal quality threshold.

In the case of implementing the functions of the above integrated modules in the form of hardware, the embodiment of the present disclosure provides a structural diagram of an electronic device. As shown in FIG9 , the electronic device 900 includes: a memory 901 , a processor 902 , a communication interface 903 , and a bus 904 .

The memory 901 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

The processor 902 may be a device that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of the present disclosure. The processor 902 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The processor 902 may be a device that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of the present disclosure. The processor 902 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

The communication interface 903 is used to connect with other devices through a communication network. The communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

In some embodiments, the memory 901 may exist independently of the processor 902, and the memory 901 may be connected to the processor 902 via a bus 904 to store instructions or program codes. When the processor 902 calls and executes the instructions or program codes stored in the memory 901, the network coverage optimization method provided in the embodiment of the present disclosure can be implemented.

In some embodiments, the memory 901 may also be integrated with the processor 902 .

The bus 904 may be an extended industry standard architecture (EISA) bus, etc. The bus 904 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, FIG. 9 only uses one thick solid line, but does not mean that there is only one bus or one type of bus.

Some embodiments of the present disclosure provide a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium), which stores computer program instructions. When the computer program instructions are executed on a computer, the computer executes the network coverage optimization method as described in any of the above embodiments.

Exemplarily, the above-mentioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks or magnetic tapes, etc.), optical disks (e.g., Compact Disks (CDs), Digital Versatile Disks (DVDs), etc.), smart cards and flash memory devices (e.g., Erasable Programmable Read-Only Memory (EPROMs), cards, sticks or key drives, etc.). The various computer-readable storage media described in the present disclosure may represent one or more devices and/or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and/or carrying instructions and/or data.

An embodiment of the present disclosure provides a computer program product including instructions. When the computer program product is run on a computer, the computer is enabled to execute the network coverage optimization method described in any one of the above embodiments.

The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

A network coverage optimization method, wherein the method comprises:

Acquire initial values of antenna parameters of multiple cells in the cell cluster to be optimized, and measurement information of multiple terminals in the cell cluster to be optimized;

Based on the initial values of the antenna parameters of the multiple cells in the cell cluster to be optimized and the measurement information of the multiple terminals, a multi-objective optimization algorithm is used to solve a multi-objective optimization problem related to network coverage to obtain a non-dominated solution set, where the non-dominated solution set includes one or more non-dominated solutions, and the non-dominated solutions are used to indicate target values of the antenna parameters of the multiple cells in the cell cluster to be optimized;

A target non-dominated solution is selected from the non-dominated solution set, and antenna parameters of multiple cells in the cell cluster to be optimized are set based on the target non-dominated solution.
The method according to claim 1, wherein the multi-objective optimization problem is determined according to multiple of the following objective functions:

The objective function is to minimize the overlap coverage as the optimization goal;

The objective function is to minimize the weak coverage rate as the optimization goal;

The objective function is to minimize the signal-to-noise ratio quality difference;

The objective function is to minimize the cross-area coverage rate as the optimization goal;

The objective function is to maximize the signal-to-noise ratio;

An objective function with the optimization goal of maximizing signal quality;

The objective function is to maximize the signal-to-interference-noise ratio;

An objective function whose optimization goal is to maximize the transmission rate; and

The objective function takes maximizing the diversion ratio as the optimization goal, and the diversion ratio is used to characterize the ratio between the flow of the target business and the flow of all businesses.
The method according to claim 1, wherein the step of solving a multi-objective optimization problem related to network coverage using a multi-objective optimization algorithm based on the initial values of antenna parameters of multiple cells in the cell cluster to be optimized and the measurement information of the multiple terminals to obtain a non-dominated solution set comprises:

Based on the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized and the measurement information of the multiple terminals, the multi-objective optimization problem is solved by a decomposition-based multi-objective optimization algorithm to obtain the non-dominated solution set; wherein the decomposition dimension of the decomposition-based multi-objective optimization algorithm is determined according to the number of cells included in the cell cluster to be optimized and the number of objective functions related to the multi-objective optimization problem.
The method according to claim 3, wherein the step of solving the multi-objective optimization problem based on the initial values of the antenna parameters of the multiple cells in the cell cluster to be optimized and the measurement information of the multiple terminals using a decomposition-based multi-objective optimization algorithm to obtain the non-dominated solution set comprises:

Decomposing the multi-objective optimization problem into a plurality of single-objective sub-problems, and configuring a corresponding population for each of the plurality of single-objective sub-problems;

Based on the initial values of the antenna parameters of multiple cells in the cell cluster to be optimized, the measurement information of the multiple terminals, and the aggregation function of the decomposition-based multi-objective optimization algorithm, the population corresponding to each of the multiple single-objective sub-problems is iteratively optimized until the iterative stopping condition is met and the non-dominated solution set is output.
The method according to claim 4, wherein the aggregation function of the decomposition-based multi-objective optimization algorithm is determined according to the aggregation function of the weighted summation method and the aggregation function of the Chebyshev method.
The method according to claim 4 or 5, wherein an iterative optimization process of the population corresponding to the single-objective subproblem comprises:

Based on the candidate value set of the antenna parameter, using a genetic variation mechanism to generate offspring individuals in the population corresponding to the single-objective subproblem;

Based on the initial values of antenna parameters of multiple cells in the cell cluster to be optimized, the measurement information of the multiple terminals and the child individuals, Determine m objective function values corresponding to the offspring individuals;

Determine the aggregate function value corresponding to the offspring individual based on the m objective function values corresponding to the offspring individual and the aggregate function of the decomposition-based multi-objective optimization algorithm;

Based on the aggregation function value corresponding to the offspring individual, determining whether to replace the individual in the population corresponding to the adjacent subproblem of the single-objective subproblem with the offspring individual;

Based on the m objective function values corresponding to the offspring individuals, solutions dominated by the offspring individuals in the external population are removed; and, if the offspring individuals are not dominated by the solutions in the external population, the offspring individuals are added to the external population; wherein the external population is used to store non-dominated solutions.
The method according to claim 6, wherein the determining the function values of the m objective functions corresponding to the offspring individuals based on the initial values of the antenna parameters of the multiple cells in the cell cluster to be optimized, the measurement information of the multiple terminals and the offspring individuals comprises:

According to the initial values of the antenna parameters of the multiple cells in the cell cluster to be optimized and the child individuals, respectively updating the measurement information of the multiple terminals to obtain updated measurement information of the multiple terminals;

According to the updated measurement information of the multiple terminals, function values of the m objective functions corresponding to the offspring individuals are determined.
The method according to claim 7, wherein:

The measurement information of the terminal includes signal qualities of multiple cells in the cell cluster to be optimized;

The updated measurement information of the terminal includes updated signal qualities of multiple cells in the cell cluster to be optimized.
The method according to claim 8, wherein, for the measurement information of each terminal, the signal quality of the target cell in the cell cluster to be optimized is updated in the following manner:

Determining an initial antenna gain corresponding to the target cell according to an initial value of an antenna parameter of the target cell;

Determining a target antenna gain corresponding to the target cell according to a target value of an antenna parameter of the target cell indicated by the child individual;

An updated signal quality of the target cell is determined according to the signal quality of the target cell, the initial antenna gain corresponding to the target cell, and the target antenna gain corresponding to the target cell.
The method according to claim 1, wherein the antenna parameters include at least one of an azimuth angle, a downtilt angle, a horizontal beamwidth, and a vertical beamwidth.
The method according to claim 1, wherein the method further comprises:

Acquire coverage problem attribute parameters and location information of each cell among the multiple cells, wherein the coverage problem attribute parameters of the cell are used to characterize the severity of the network coverage problem existing in the cell;

The multiple cells are clustered according to the coverage problem attribute parameters and location information of each cell in the multiple cells to obtain one or more cell clusters to be optimized.
The method according to claim 11, wherein the coverage problem attribute parameter of the cell is determined based on at least one of the overlapping coverage rate, weak coverage rate, signal-to-noise ratio quality difference and cross-zone coverage rate of the cell.
The method according to claim 12, wherein the overlapping coverage rate of the cell is determined according to the ratio between the number of heavy coverage sampling points in the cell and the total number of sampling points in the cell; wherein the heavy coverage sampling points are sampling points that meet the following conditions:

The measured signal quality of the cell is greater than or equal to a first signal quality threshold;

The measured signal quality of a neighboring cell of the cell is greater than or equal to a second signal quality threshold; and

A difference between a measured signal quality of the cell and a measured signal quality of a neighboring cell of the cell is greater than or equal to a third signal quality threshold.
The method according to claim 12, wherein the weak coverage rate of the cell is calculated based on the number of weak coverage sampling points in the cell and The weak coverage sampling point is a sampling point at which the measured signal quality of the cell is less than or equal to a fourth signal quality threshold.
The method according to claim 12, wherein the signal-to-noise ratio quality difference of the cell is determined according to the ratio between the number of signal-to-noise ratio quality difference sampling points in the cell and the total number of sampling points in the cell; the signal-to-noise ratio quality difference sampling points are sampling points at which the measured signal-to-noise ratio of the cell is less than or equal to a signal-to-noise ratio quality difference threshold.
The method according to claim 12, wherein the cross-cell coverage rate of the cell is determined according to the ratio between the cross-cell coverage sampling points of the cell and the total number of sampling points of the cell; wherein the cross-cell coverage sampling points are sampling points located outside the planned coverage range of the cell and at which the signal quality of the cell is measured to be greater than a fifth signal quality threshold.
An electronic device, comprising: a processor and a memory for storing instructions executable by the processor;

The processor is configured to execute the instructions so that the electronic device performs the method according to any one of claims 1-16.
A computer-readable storage medium, wherein computer instructions are stored on the computer-readable storage medium, and when the computer instructions are executed on an electronic device, the electronic device executes the method according to any one of claims 1-16.