A 5G heterogeneous network base station deployment method for distribution network

Abstract

The present invention discloses a 5G heterogeneous network base station deployment method for distribution network, comprising the following steps: S1, constructing distribution network service set, distribution network terminal set and distribution network service bearing matrix according to the collected original data of distribution network service communication demand and distribution network service distribution; S2, digitizing and normalizing the original demand of distribution network service for different communication indicators, obtaining the normalized demand value of distribution network service for each communication indicator, and calculating the importance of each distribution network service according to the normalized demand value; S3, calculating the importance of distribution network terminal; S4, constructing an optimization objective function based on network deployment cost, network coverage overlap and terminal weighted signal-to-noise ratio; S5, according to the optimization objective function, using the existing communication-related infrastructure in the distribution network, and adopting a genetic algorithm to obtain a 5G heterogeneous network deployment scheme. The present invention can significantly improve the signal-to-noise ratio of important terminals and reduce deployment costs.

Description

Technical Field

[0001] The present invention belongs to the field of power communication technology, and specifically relates to a 5G heterogeneous network base station deployment method for a distribution network. Background Art

[0002] With the development of energy internet and the proposal of "dual carbon" goals, a large number of distributed power sources will be connected, a number of new businesses will emerge in the distribution network, and the number, distribution and communication needs of existing businesses will also change. 5G communication technology has the outstanding advantages of large bandwidth, low latency and wide connection, which is highly consistent with the development strategy of energy internet and the business needs of distribution network, and is the best choice to support distribution network communication.

[0003] Due to the high operating frequency band of 5G base stations, the coverage of 5G macro base stations is smaller than that of traditional 3G / 4G base stations, and network coverage needs to be achieved through large-scale deployment of micro base stations. In the existing base station deployment strategy research, most of them use classic algorithms such as genetic algorithms, immune algorithms, greedy algorithms, or improved classic algorithms to optimize the deployment strategy of base stations. The above studies mostly use network capacity, network energy efficiency, and network deployment cost as the main optimization objectives. During the optimization process, the terminals are regarded as homogeneous nodes, ignoring the diversity of terminals. In addition, the above base station deployment strategies are mostly studies on the deployment strategies of the same type of base stations, and the joint deployment of multiple base stations has not been realized. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a 5G heterogeneous network base station deployment method for distribution networks. The 5G heterogeneous network base station deployment method can significantly improve the weighted signal-to-noise ratio and reduce deployment costs.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A 5G heterogeneous network base station deployment method for a distribution network comprises the following steps:

[0007] Step S1: construct a distribution network service set, a distribution network terminal set and a distribution network service bearing matrix based on the collected original data of distribution network service communication requirements and the distribution status of distribution network services;

[0008] Step S2: digitize and normalize the original demands of each distribution network service for different communication indicators in the distribution network service set to obtain the normalized demand value of the distribution network service for each communication indicator, and calculate the importance of each distribution network service according to the normalized demand value;

[0009] Step S3, calculating the importance of distribution network terminals based on the distribution network service carrying matrix described in step S1 and the importance of each distribution network service described in step S2;

[0010] Step S4: construct an optimization objective function based on network deployment cost, network coverage overlap and terminal weighted signal-to-noise ratio;

[0011] Step S5: According to the optimization objective function constructed in step S4, the existing communication-related infrastructure of the distribution network is utilized and a genetic algorithm is used to obtain a 5G heterogeneous network deployment solution.

[0012] Furthermore, the distribution network service carrying matrix in step S1 represents the type of distribution service carried by each distribution network terminal.

[0013] Furthermore, in step S2, the original requirements of each distribution network service for different communication indicators are digitized into the following three situations:

[0014] (1) When the communication index is a non-numerical index, the original demand of each distribution network service for the non-numerical communication index is quantified using formula (1):

[0015]

[0016] Wherein, k is the communication index, and k∈K, K is the communication index set, the service set is sorted in ascending order according to the requirements of the index, the service with higher index requirements has a larger serial number, and the services with the same requirements are sorted randomly; s is the service, and s∈S, S is the distribution network service set; q(k,s) is the serial number of service s after sorting under the communication index k; s(k,q) is the service corresponding to serial number q under the communication index k; the symbol f indicates that the service on the left side of the symbol f is more important than the service on the right side; pf indicates that the services on both sides are equally important;

[0017] (2) When the communication index is an index with a small numerical difference, formula (2) is used to digitize the original demand of each distribution network service for the non-numerical communication index:

[0018]

[0019] Among them, k min , k max are the minimum and maximum values ​​of the communication index k, respectively; X is the adjustment coefficient, which takes the value of (0,1);

[0020] (3) When the communication index is an index with large numerical differences, formula (3) is used to quantify the original demand of each distribution network service for non-numerical communication index:

[0021] f3(k)=logk (3)

[0022] Among them, k is the communication index.

[0023] Furthermore, after the original demands of the distribution network services for different communication indicators are digitized in step S2, if the digitized communication indicators are benefit-type, the calculation formula of formula (2) is used to normalize the digitized communication indicators; if the digitized communication indicators are cost-type, the calculation formula of formula (4) is used to normalize the digitized communication indicators:

[0024]

[0025] Among them, k min , k max are the minimum and maximum values ​​of the communication index k respectively; X is the adjustment coefficient, and its value is (0,1).

[0026] Furthermore, the specific method for calculating the importance of each distribution network service in step S2 is:

[0027] First, the weight W(S) of the communication index of each distribution service is calculated, and its calculation formula is shown in formula (5):

[0028] W(S)=W1(S)*W2(S) (5)

[0029] Among them, W1(S)*W2(S) is expressed as the product of the corresponding elements of the matrices W1(S) and W2(S). W1(S) is the weight of each communication indicator under the service set S obtained by the entropy weight method, which is an n-dimensional column vector, and n is the number of distribution terminals in the network; W2(S) is the weight of each communication indicator under the service set S obtained by the hierarchical analysis method, which is an n-dimensional column vector;

[0030] Secondly, calculate the importance of each distribution network business I j , and its calculation formula is shown in formula (6):

[0031] I j =b j W(S) (6)

[0032] Where, j is the distribution network business type; b j is the result of digitization and normalization of the communication index of distribution network service j, which is a t-dimensional column vector, and t is the number of communication index types in the communication index set K.

[0033] Furthermore, the calculation formula of the importance of the distribution network terminal in step S3 is shown in formula (7):

[0034] W T =AI (7)

[0035] Among them, I is the service importance vector, which is an m-dimensional column vector, and m is the number of service types; A is the distribution of services in the terminal, which is an n×m-dimensional matrix, and the element a in A is ijThe status of distribution network terminal i carrying distribution network service j is as follows:

[0036]

[0037] Furthermore, the optimization objective function constructed in step S4 is as shown in formula (8):

[0038]

[0039] Among them, F1 is the network deployment cost; F2 is the network coverage overlap; F3 is the terminal weighted signal-to-noise ratio;

[0040] The calculation formula of the network deployment cost is shown in formula (9):

[0041] F1=L0·η0+L1·η1 (9)

[0042] Wherein, L0 is the macro base station deployment plan, which is an N-dimensional row vector, N is the number of candidate base station locations, and each element in the row vector corresponds to a base station candidate location. If a macro base station is deployed at the candidate location, the element is set to 1, otherwise it is 0; η0 is the macro base station deployment cost, which is an N-dimensional column vector, and each element represents the unitized cost of deploying a macro base station at the candidate location, which needs to be determined based on whether there is communication-related infrastructure at the candidate location and the type of the infrastructure; L1 is the micro base station deployment plan, η1 is the micro base station deployment cost, and the definition method is the same as that of the macro base station;

[0043] The calculation formula of the coverage overlap is shown in formula (10):

[0044]

[0045] Where n is the number of terminals, u i represents the coverage overlap of terminal i, and u i The calculation formula of is (11):

[0046]

[0047] Among them, q 0 iv Indicates the deployment of a macro base station at candidate location v and its coverage of terminal i. If a macro base station is deployed at candidate location v and the base station covers terminal i, then q 0 iv =1, otherwise q 0 iv =0;q 1 iv Indicates the deployment of micro base stations and the coverage of terminal i, with the same definition as q 0 iv .

[0048] The calculation formula of the terminal weighted signal-to-noise ratio is shown in formula (12):

[0049]

[0050] Among them, W Tmax is the maximum value of the terminal importance; ω is the n-dimensional terminal signal-to-noise ratio row vector, and the calculation formula of its elements is as follows:

[0051]

[0052] Among them, ω i is the signal-to-noise ratio of terminal i; δ is the noise power; q 0 iv P is the coverage of the macro base station to the terminal i when the macro base station is deployed at the candidate position v; 0 iv is the power at terminal i when a macro base station is deployed at candidate location v; q 1 iv , P 1 iv Definition and q 0 iv , P 0 iv The definition is the same; the signal power P of the base station at the candidate position v at the terminal i iv The calculation formula is as follows:

[0053]

[0054] Among them, G b is the power gain of the base station, P B is the static power of the base station, P t is the base station transmission power. The above parameters need to be determined according to the deployment of the base station at the candidate location v. When a macro base station is deployed, P iv Denoted as P iv 0 , when deploying micro base stations P iv Denoted as P iv 1 , when no base station is deployed, P iv =0,P iv 0 =0,P iv 1 =0;d iv is the distance from the candidate location v to the terminal i; α is the power attenuation factor.

[0055] Furthermore, the construction of the 5G heterogeneous network deployment solution in step S5 specifically includes the following steps:

[0056] 1) constructing an infrastructure set based on the locations of 4G towers and existing power distribution rooms, and constructing a candidate base station location set based on the full coverage constraint and the infrastructure set;

[0057] 2) Set the initial population size, iteration threshold, and dominant individual ratio;

[0058] 3) Randomly generate an initial g×N dimensional matrix D as the initial population, where the element D hv ∈{0, 1, 2, 3}, respectively representing that in scheme h, no base station is deployed at the candidate location v, a macro base station is deployed, a micro base station is deployed, or no base station is deployed;

[0059] 4) Calculate the objective function value of each individual, retain the dominant individuals, and generate new individuals through random single-point crossover and random single-point mutation in a roulette wheel manner. The dominant individuals and the new individuals together constitute a new population of the same size as the initial population;

[0060] 5) Update the iteration count and determine whether the iteration count meets the iteration threshold set in step 2). If the iteration count meets the iteration threshold set in step 2), the 5G heterogeneous network base station deployment is completed; if the iteration count does not meet the iteration threshold set in step 2), repeat steps 3) to 5).

[0061] Compared with the prior art, the present invention has the following positive and beneficial effects:

[0062] (1) By digitizing and normalizing the communication indicators of distribution network business, the differences of various communication indicators are shielded, making different communication indicators comparable. The comprehensive weights of communication indicators are obtained by combining the entropy weight method with the hierarchical analysis method, which can accurately quantify the importance of distribution network business.

[0063] (2) Based on the goal of full coverage of distribution network business communication needs, the 5G network is gridded, and a set of candidate base station locations is constructed in combination with the existing infrastructure. Based on the candidate base station locations, a genetic algorithm is used to solve the problem with cost, weighted signal-to-noise ratio and coverage overlap as optimization targets. This can maximize the use of infrastructure, save 5G network deployment costs, effectively improve terminal service quality, and ensure that high-importance terminals have a high signal-to-noise ratio.

[0064] (3) By using the distribution network service carrying matrix and the importance of each distribution network service described in step S2 to calculate the importance of the distribution network terminal, the importance of the distribution network terminal can be effectively evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a distribution diagram of distribution network terminals and infrastructure in an embodiment of the present invention;

[0066] Figure 2This is a diagram of 5G network base station deployment results based on the method of the present invention (TI-BDSDS);

[0067] Figure 3 This is the result diagram of 5G network base station deployment based on the IGBSL algorithm;

[0068] Figure 4 This is a diagram of 5G network base station deployment results based on the CFSSA algorithm;

[0069] Figure 5 It is a terminal group signal-to-noise ratio diagram based on the method of the present invention (TI-BDSDS), IGBSL algorithm and CFSSA algorithm;

[0070] Figure 6 It is a normalized comparison chart of network deployment performance based on the method of the present invention (TI-BDSDS), IGBSL algorithm and CFSSA algorithm. DETAILED DESCRIPTION

[0071] In order to make the technical solution of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0072] Example

[0073] A 5G heterogeneous network base station deployment method for a distribution network comprises the following steps:

[0074] Step S1: construct a distribution network service set, a distribution network terminal set and a distribution network service bearing matrix based on the collected original data of distribution network service communication requirements and the distribution network service distribution status. The distribution network service bearing matrix represents the type of distribution service carried by each distribution network terminal.

[0075] Step S2, digitizing and normalizing the original demands of each distribution network service for different communication indicators in the distribution network service set to obtain the normalized demand value of the distribution network service for each communication indicator, calculating the first weight by the entropy weight method according to the normalized demand value, calculating the second weight by the hierarchical analysis method, multiplying the first weight by the second weight to obtain the comprehensive weight of each communication indicator, and based on the comprehensive weight, performing weighted summation on the normalized demand values ​​of each distribution network service under each communication indicator to obtain the importance of each power grid service;

[0076] The original requirements of each distribution network business for different communication indicators are digitized and divided into the following three cases:

[0077] (1) When the communication index is a non-numerical index, the original demand of each distribution network service for the non-numerical communication index is quantified using formula (1):

[0078]

[0079] Wherein, k is the communication index, and k∈K, K is the communication index set, the service set is sorted in ascending order according to the requirements of the index, the service with higher index requirements has a larger serial number, and the services with the same requirements are sorted randomly; s is the service, and s∈S, S is the distribution network service set; q(k,s) is the serial number of service s after sorting under the communication index k; s(k,q) is the service corresponding to serial number q under the communication index k; the symbol f indicates that the service on the left side of the symbol f is more important than the service on the right side; pf indicates that the services on both sides are equally important;

[0080] (2) When the communication index is an index with a small numerical difference, formula (2) is used to digitize the original demand of each distribution network service for the non-numerical communication index:

[0081]

[0082] Among them, k min , k max are the minimum and maximum values ​​of the communication index k, respectively; X is the adjustment coefficient, which takes the value of (0,1);

[0083] (3) When the communication index is an index with large numerical differences, formula (3) is used to quantify the original demand of each distribution network service for non-numerical communication index:

[0084] f3(k)=logk (3)

[0085] Among them, k is the communication index.

[0086] After the original requirements of the distribution network services for different communication indicators are digitized, if the indicator is benefit-based, the calculation formula of formula (2) is used to normalize the digitized indicators. If the indicator is cost-based, the normalization method is:

[0087]

[0088] The specific method for calculating the importance of each distribution network service is as follows:

[0089] First, the weight W(S) of the communication index of each distribution service is calculated, and its calculation formula is shown in formula (5):

[0090] W(S)=W1(S)*W2(S) (5)

[0091] Among them, W1(S)*W2(S) is expressed as the product of the corresponding elements of the matrices W1(S) and W2(S). W1(S) is the weight of each communication indicator under the service set S obtained by the entropy weight method, which is an n-dimensional column vector, and n is the number of distribution terminals in the network; W2(S) is the weight of each communication indicator under the service set S obtained by the hierarchical analysis method, which is an n-dimensional column vector;

[0092] Secondly, calculate the importance of each distribution network business I j , and its calculation formula is shown in formula (6):

[0093] I j =b j W(S) (6)

[0094] Where, j is the distribution network business type; b j is the result of digitization and normalization of the communication index of distribution network service j, which is a t-dimensional column vector, and t is the number of communication index types in the communication index set K.

[0095] Step S3, calculating the importance of distribution network terminals based on the distribution network service carrying matrix described in step S1 and the importance of each distribution network service described in step S2;

[0096] The calculation formula of the distribution network terminal importance is shown in formula (7):

[0097] W T =AI (7)

[0098] Among them, I is the service importance vector, which is an m-dimensional column vector, and m is the number of service types; A is the distribution of services in the terminal, which is an n×m-dimensional matrix, and the element a in A is ij The status of distribution network terminal i carrying distribution network service j is as follows:

[0099]

[0100] Step S4: construct an optimization objective function based on the network deployment cost, network coverage overlap and terminal weighted signal-to-noise ratio; the optimization objective function is shown in formula (8):

[0101]

[0102] Among them, F1 is the network deployment cost; F2 is the network coverage overlap; F3 is the terminal weighted signal-to-noise ratio;

[0103] The calculation formula of the network deployment cost is shown in formula (9):

[0104] F1=L0·η0+L1·η1 (9)

[0105] Wherein, L0 is the macro base station deployment plan, which is an N-dimensional row vector, N is the number of candidate base station locations, and if the candidate location deploys a macro base station, the element is set to 1, otherwise it is 0; η0 is the unitized cost of macro base station deployment, which is an N-dimensional column vector, and each element represents the unitized cost of deploying a macro base station at the candidate location, which needs to be determined based on whether there is infrastructure at the candidate location and the type of infrastructure; L1 is the micro base station deployment plan, and η1 is the unitized cost of micro base station deployment, which is defined in the same way as the macro base station;

[0106] The calculation formula of the coverage overlap is shown in formula (10):

[0107]

[0108] Where n is the number of terminals, u i Indicates the coverage overlap of terminal i:

[0109]

[0110] Among them, q 0 iv Indicates the deployment of a macro base station at candidate location v and its coverage of terminal i. If a macro base station is deployed at candidate location v and the base station covers terminal i, then q 0 iv =1, otherwise q 0 iv =0;q 1 iv Indicates the deployment of micro base stations and the coverage of terminal i, with the same definition as q 0 iv .

[0111] The calculation formula of the terminal weighted signal-to-noise ratio is shown in formula (12):

[0112]

[0113] Among them, W Tmax is the maximum value of the terminal importance; ω is the n-dimensional terminal signal-to-noise ratio row vector, and the calculation formula of its elements is as follows:

[0114]

[0115] Among them, ω i is the signal-to-noise ratio of terminal i; δ is the noise power; q 0 iv When a macro base station is deployed at position v, the coverage of the macro base station to terminal i; P 0 iv is the power at terminal i when a macro base station is deployed at candidate location v; q 1 iv , P 1iv Definition and q 0 iv , P 0 iv The definition is the same; the signal power P of the base station at the candidate position v at the terminal i iv The calculation formula is as follows:

[0116]

[0117] Among them, G b is the power gain of the base station, P B is the static power of the base station, P t is the base station transmission power. The above parameters need to be determined according to the deployment status of the base station at the candidate location v. When a macro base station is deployed, P iv Denoted as P 0 iv , when deploying micro base stations, P iv Denoted as P 1 iv , when no base station is deployed, P iv =0,P 0 iv =0, P 1 iv =0;d iv is the distance from the candidate location v to the terminal i; α is the power attenuation factor.

[0118] Furthermore, the construction of the 5G heterogeneous network deployment solution in step S5 specifically includes the following steps:

[0119] 1) constructing an infrastructure set based on the locations of 4G towers and existing power distribution rooms, and constructing a candidate base station location set based on the full coverage constraint and the infrastructure set;

[0120] 2) Set the initial population size, iteration threshold, and dominant individual ratio;

[0121] 3) Randomly generate an initial g×N dimensional matrix D as the initial population, where the element D hv ∈{0, 1, 2, 3}, respectively representing that in scheme h, no base station is deployed at the candidate location v, a macro base station is deployed, a micro base station is deployed, or no base station is deployed;

[0122] 4) Calculate the objective function value of each individual, retain the dominant individuals, and generate new individuals through random single-point crossover and random single-point mutation in a roulette wheel manner. The dominant individuals and the new individuals together constitute a new population of the same size as the initial population;

[0123] 5) Update the iteration count and determine whether the iteration count meets the iteration threshold set in step 2). If the iteration count meets the iteration threshold set in step 2), the 5G heterogeneous network base station deployment is completed; if the iteration count does not meet the iteration threshold set in step 2), repeat steps 3) to 5).

[0124] The following is an analysis of the advantages of the method (TI-BDSDS) proposed in the present invention compared with the IGBSL algorithm and the CFSSA algorithm in the deployment of 5G heterogeneous network base stations for distribution networks, using actual examples.

[0125] The IGBSL algorithm is based on the 5G base station site selection strategy of the immune genetic algorithm, and optimizes the deployment strategies of macro base stations and micro base stations respectively. The deployment of micro base stations mainly achieves full network coverage to make up for the shortcomings of the algorithm itself that the network cannot achieve full coverage. The CFSSA algorithm is a wireless access station site selection algorithm based on the competitive function. This algorithm is used for macro base station deployment to achieve full network coverage.

[0126] Taking the 2km×2km distribution network coverage as an example, a simulation environment is established, and the distribution terminals and infrastructure are distributed as follows: Figure 1 The business distribution of distribution terminals is shown in Table 1, and other relevant simulation parameters are shown in Table 2.

[0127] Table 1 Distribution of power distribution terminal services

[0128] Business Type bandwidth Latency reliability telemetry 64kbps 30s Very high Distribution Transformer Video Surveillance 24kbps 0.2s high Power quality monitoring 4kbps 0.5s high Power equipment operation status monitoring 4kbps 1s high Power dispatch telephone 64kbps <0.15s Very high

[0129] Table 2 Simulation parameters

[0130]

[0131]

[0132] The 5G network base station deployment results based on the method proposed in this invention (TI-BDSDS), IGBSL algorithm and CFSSA algorithm are as follows: Figure 2 , Figure 3 and Figure 4 As shown. Figure 2 and Figure 3 , Figure 4 By comparison, it can be seen that the method proposed in the present invention realizes the joint deployment of macro base stations and micro base stations. The micro base stations achieve full network coverage while ensuring the signal-to-noise ratio of terminals with higher importance; under the IBGSL algorithm, the number of terminals with overlapping coverage is small, and most terminals are covered with fewer macro base stations. For a few sparsely distributed uncovered terminals, more micro base stations need to be deployed to achieve full coverage of the 5G network; the CFSSA algorithm achieves full network coverage through more macro base stations, and there are more distribution network terminals with overlapping coverage.

[0133] In order to accurately measure the signal-to-noise ratio of terminals with different importance, the importance of distribution network terminals is sorted in descending order and evenly divided into 10 groups according to the arrangement order. The importance levels of distribution network terminals are defined as 1-10 in sequence, where level 1 is the most important and level 10 is the least important. The mean signal-to-noise ratio of the terminals in each group is taken as the signal-to-noise ratio of the group. The normalized results of the signal-to-noise ratio of the terminal grouping under the method proposed in this invention, the IGBSL algorithm and the CFSSA algorithm are as follows: Figure 5 The method proposed in the present invention takes into account the weighted signal-to-noise ratio, so the signal-to-noise ratio of a terminal with a greater importance is generally greater; the importance of the terminal is not defined in the IBSL algorithm and the CFSSA algorithm, and the packet signal-to-noise ratio is not related to the importance.

[0134] In order to quantify the investment benefits of 5G network deployment, the 5G network deployment benefit calculation formula defined in the present invention is as follows:

[0135]

[0136] The meaning of each letter symbol has been described in the above formula.

[0137] The normalized comparison results of 5G network deployment performance based on the method proposed in this invention (TI-BDSDS) and the IBSL algorithm and CFSSA algorithm are as follows: Figure 6 As shown. Figure 6 It can be seen that the present invention has the lowest deployment cost, the highest weighted signal-to-noise ratio, and the best network deployment benefit.

[0138] The present invention is attached Figure 2-6 It is obtained based on different algorithms under the simulation parameters in Table 2.

[0139] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A 5G heterogeneous network base station deployment method for distribution network, characterized in that: The following steps are involved: Step S1: construct a distribution network service set, a distribution network terminal set and a distribution network service bearing matrix based on the collected original data of distribution network service communication requirements and the distribution status of distribution network services; Step S2: digitize and normalize the original demands of each distribution network service for different communication indicators in the distribution network service set to obtain the normalized demand value of the distribution network service for each communication indicator, and calculate the importance of each distribution network service according to the normalized demand value; Step S3, calculating the importance of distribution network terminals based on the distribution network service carrying matrix described in step S1 and the importance of each distribution network service described in step S2; Step S4: construct an optimization objective function based on network deployment cost, network coverage overlap and terminal weighted signal-to-noise ratio; Step S5: According to the optimization objective function constructed in step S4, the existing communication-related infrastructure of the distribution network is used to obtain a 5G heterogeneous network deployment solution using a genetic algorithm; The distribution network service carrying matrix in step S1 represents the type of distribution service carried by each distribution network terminal; In step S2, the original requirements of each distribution network service for different communication indicators are digitized into the following three situations: (1) When the communication index is a non-numerical index, the original demand of each distribution network service for the non-numerical communication index is quantified using formula (1): Wherein, k is the communication index, and k∈K, K is the communication index set, the service set is sorted in ascending order according to the requirements of the index, the service with higher index requirements has a larger serial number, and the services with the same requirements are sorted randomly; s is the service, and s∈S, S is the distribution network service set; q(k,s) is the serial number of service s after sorting under the communication index k; s(k,q) is the service corresponding to serial number q under the communication index k; the symbol f indicates that the service on the left side of the symbol f is more important than the service on the right side; pf indicates that the services on both sides are equally important; (2) When the communication index is an index with a small numerical difference, formula (2) is used to digitize the original demand of each distribution network service for the non-numerical communication index: Among them, k min , k max are the minimum and maximum values ​​of the communication index k, respectively; X is the adjustment coefficient, which takes the value of (0,1); (3) When the communication index is an index with large numerical differences, formula (3) is used to quantify the original demand of each distribution network service for non-numerical communication index: f3(k)=log k (3) Among them, k is the communication index; After the original demands of the distribution network services for different communication indicators are digitized in step S2, if the digitized communication indicators are benefit-type, the calculation formula of formula (2) is used to normalize the digitized communication indicators; if the digitized communication indicators are cost-type, the calculation formula of formula (4) is used to normalize the digitized communication indicators: Among them, k min , k max are the minimum and maximum values ​​of the communication index k respectively; X is the adjustment coefficient, and its value is (0,1); The specific method for calculating the importance of each distribution network service in step S2 is: First, the weight W(S) of the communication index of each distribution service is calculated, and its calculation formula is shown in formula (5): W(S)=W1(S)*W2(S) (5) Among them, W1(S)*W2(S) is expressed as the product of the corresponding elements of the matrices W1(S) and W2(S). W1(S) is the weight of each communication indicator under the service set S obtained by the entropy weight method, which is an n-dimensional column vector, and n is the number of distribution terminals in the network; W2(S) is the weight of each communication indicator under the service set S obtained by the hierarchical analysis method, which is an n-dimensional column vector; Secondly, calculate the importance of each distribution network business I j , and its calculation formula is shown in formula (6): I j =b j W(S) (6) Where, j is the distribution network business type; b j is the result of digitization and normalization of the communication index of distribution network service j, which is a t-dimensional column vector, and t is the number of communication index types in the communication index set K; The calculation formula of the importance of the distribution network terminal in step S3 is shown in formula (7): W T =AI (7) Among them, I is the service importance vector, which is an m-dimensional column vector, and m is the number of service types; A is the distribution of services in the terminal, which is an n×m-dimensional matrix, and the element a in A is ij The status of distribution network terminal i carrying distribution network service j is as follows: The optimization objective function constructed in step S4 is shown in formula (8): Among them, F1 is the network deployment cost; F2 is the network coverage overlap; F3 is the terminal weighted signal-to-noise ratio; The calculation formula of the network deployment cost is shown in formula (9): F1=L0·η0+L1·η1(9) Wherein, L0 is the macro base station deployment plan, which is an N-dimensional row vector, N is the number of candidate base station locations, and each element in the row vector corresponds to a base station candidate location. If a macro base station is deployed at the candidate location, the element is set to 1, otherwise it is 0; η0 is the macro base station deployment cost, which is an N-dimensional column vector, and each element represents the unitized cost of deploying a macro base station at the candidate location, which needs to be determined based on whether there is communication-related infrastructure at the candidate location and the type of the infrastructure; L1 is the micro base station deployment plan, and η1 is the micro base station deployment cost, which is defined in the same way as the macro base station; The calculation formula of the coverage overlap is shown in formula (10): Where n is the number of terminals, u i represents the coverage overlap of terminal i, and u i The calculation formula of is (11): Among them, q 0 iv Indicates the deployment of a macro base station at candidate location v and its coverage of terminal i. If a macro base station is deployed at candidate location v and the base station covers terminal i, then q 0 iv =1, otherwise q 0 iv =0;q 1 iv Indicates the deployment of micro base stations and the coverage of terminal i, with the same definition as q 0 iv ; The calculation formula of the terminal weighted signal-to-noise ratio is shown in formula (12): Among them, W Tmax is the maximum value of the terminal importance; ω is the n-dimensional terminal signal-to-noise ratio row vector, and the calculation formula of its elements is as follows: Among them, ω i is the signal-to-noise ratio of terminal i; δ is the noise power; q 0 iv P is the coverage of the macro base station to the terminal i when the macro base station is deployed at the candidate position v; 0 iv is the power at terminal i when a macro base station is deployed at candidate location v; q 1 iv , P 1 iv Definition and q 0 iv , P 0 iv The definition is the same; the signal power P of the base station at the candidate position v at the terminal i iv The calculation formula is as follows: Among them, G b is the power gain of the base station, P B is the static power of the base station, P t is the base station transmission power. The above parameters need to be determined according to the deployment of the base station at the candidate location v. When a macro base station is deployed, P iv Denoted as P iv 0 , when deploying micro base stations P iv Denoted as P iv 1 , when no base station is deployed, P iv =0,P iv 0 =0,P iv 1 =0;d iv is the distance from the candidate location v to the terminal i; α is the power attenuation factor; The step S5 of constructing the 5G heterogeneous network deployment solution specifically includes the following steps: 1) constructing an infrastructure set based on the locations of 4G towers and existing power distribution rooms, and constructing a candidate base station location set based on the full coverage constraint and the infrastructure set; 2) Set the initial population size, iteration threshold, and dominant individual ratio; 3) Randomly generate an initial g×N dimensional matrix D as the initial population, where the element D hv ∈{0, 1, 2, 3}, respectively representing that in scheme h, no base station is deployed at the candidate location v, a macro base station is deployed, a micro base station is deployed, or no base station is deployed; 4) Calculate the objective function value of each individual, retain the dominant individuals, and generate new individuals through random single-point crossover and random single-point mutation in a roulette wheel manner. The dominant individuals and the new individuals together constitute a new population of the same size as the initial population; 5) Update the iteration count and determine whether the iteration count meets the iteration threshold set in step 2). If the iteration count meets the iteration threshold set in step 2), the 5G heterogeneous network base station deployment is completed; if the iteration count does not meet the iteration threshold set in step 2), repeat steps 3) to 5).

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