A large-scale complex scene 5G heterogeneous network deployment method and related device

Abstract

The application discloses a large-scale complex scene 5G heterogeneous network deployment method and related device, belongs to network deployment technical field; the method establishes energy consumption evaluation model according to actual deployment demand in present network, and the maximum network overall energy efficiency ratio is taken as target, and the constraints involved include regional user rate demand constraint, downlink channel interference limit, regional coverage rate constraint, radio frequency power upper limit constraint, frequency domain resource allocation constraint.The model considers actual geographical factors, including mountains, rivers, buildings and other obstacles.In addition, the model considers the operation link in the planning stage, predicts the traffic demand of each region in the next few years based on a data-driven regional traffic prediction model, and uses the above-mentioned regional user and rate demand constraint threshold value.A high feasibility regional optimal energy efficiency network deployment scheme is obtained by combining the method of sequential optimization solution and network planning software simulation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of network deployment, and relates to a large-scale complex scene 5G heterogeneous network deployment method and related device. BACKGROUND

[0002] With the rapid development of mobile communication technology, 5G technology has been widely used in industry, commerce, civil and other fields due to its advantages of large bandwidth, high speed, low delay and the like. However, its energy consumption is also greatly increased compared with 4G. According to statistics, the power consumption of a 5G base station is 3-4 times that of a 4G base station, and the energy efficiency is 12 times that of a 4G base station. For a single base station, for example, a single-frequency three-fan communication main device, the energy consumption of the radio frequency unit can account for more than 75% of the total energy consumption of the site. It is estimated that about 60% of the signal strength is attenuated due to path loss during the propagation of the signal in the air interface. Therefore, in order to ensure the received signal strength of the downlink user, it is necessary to increase the downlink base station transmission power to compensate for signal attenuation, thereby increasing the radio frequency energy consumption. When the base station transmission power increases by 1db, the site power consumption increases by about 10%, so the regional network energy consumption will be greatly increased. In the future, with the evolution of communication technology to 5G-A / 6G technology, network deployment is considered to be implemented on high-frequency bands such as millimeter waves, thereby achieving higher transmission rates, and the corresponding path loss phenomenon will be more obvious, and the energy consumption challenge faced by the communication field will be more severe.

[0003] To solve the above problems, the current solution is to encrypt the site, reduce the distance between the base station and the user in the region by means of dense network, and thereby reduce the attenuation of the signal during propagation. However, under the same frequency networking, increasing the deployment density of base stations will also cause greater interference in the adjacent area. Therefore, a reasonable base station deployment strategy is of great significance for maintaining the quality of service and reducing the regional energy consumption.

[0004] Currently, the research on heterogeneous network deployment mainly focuses on two aspects. First, small-scale base station deployment strategy, mainly considering the heterogeneous network deployment under a single macro base station and multiple micro base stations. This type of research involves a small number of macro and micro base stations and cannot provide effective reference for large-scale networking. Second, network deployment strategy involving multiple heterogeneous base stations. In order to reduce the optimization solving difficulty and time cost, this type of research often only considers the total rate factor as a constraint. This method can improve the solving efficiency by simplifying the model, but it does not fully consider the actual QoS demand (quality of service), so it also cannot provide effective reference for large-scale networking.

[0005] The deployment of 5G network in large-scale complex scenarios involves complex communication mechanism, and there is multi-coupling between decision variables. If the base station transmission power is too small, the coverage range will be insufficient due to path loss, and if the power is too large, it will interfere with the users in the adjacent area, resulting in a decrease in the rate. Professional network simulation software can accurately simulate the network deployment effect, but as the scale of the deployment area increases, the simulation overhead cost also gradually increases, and it is difficult to accurately obtain the optimal deployment scheme. SUMMARY

[0006] The purpose of the present application is to provide a large-scale complex scenario 5G heterogeneous network deployment method and related device, to solve the technical problems of low model solving efficiency and difficulty in obtaining the optimal scheme for the large-scale deployment area and complex scenario in the prior art.

[0007] To achieve the above purpose, the following technical solutions are adopted:

[0008] In a first aspect, the present application provides a large-scale complex scenario 5G heterogeneous network deployment method, comprising the following steps:

[0009] Obtain the 3D map of the to-be-deployed area, construct the regional three-dimensional space coordinates, and mark the obstacle coordinates;

[0010] Divide the to-be-deployed area into several equal-area sub-regions, set up a macro base station at the center of each sub-region, and generate a macro base station parameter combination

[0011] According to the macro base station parameter combination Calculate the sub-region coverage rate, combine the obstacle coordinates, and screen to obtain the macro base station sample space Ω 0 ;

[0012] Reduce the macro base station sample space Ω 0 to obtain a small-scale macro base station sample space Ω 1 , based on the small-scale macro base station sample space Ω 1 Deploy micro base stations with different densities in each sub-region;

[0013] Collect the number and spatial coordinates of the micro base stations in each sub-region, and merge the small-scale macro base station sample space Ω 1 to obtain a complete sample space Ω 2 ;

[0014] Screen the complete sample space Ω 2 through an energy consumption evaluation model to obtain a rough selection sample set Ω 3 , and sort the samples in the rough selection sample set Ω 3 to form a fine selection sample set Ω 4 ;

[0015] Using network simulation software to simulate the fine selection sample set Ω 4 Perform network simulation, output the optimal heterogeneous network deployment scheme U of the to-be-deployed area * .

[0016] Further, the step of dividing the to-be-deployed area into a plurality of equal-area sub-areas, setting up a macro base station at the center of each sub-area, and generating a macro base station parameter combination , specifically includes:

[0017] According to the 3D map of the to-be-deployed area, a 2D area plan is formed, and grid division is performed to divide the entire area into C sub-areas of equal size, and each sub-area is divided according to a square with a side length of L;

[0018] Label each grid, and distinguish using C(w, d), where w represents the row number of the current sub-area, and d represents the column number of the current sub-area;

[0019] Deploy a macro base station at the center of each sub-area;

[0020] Collect the number N of all deployed macro base stations M , combined with the 2D map and coordinates, collect the coordinates (X M , Y M ) of the macro base station deployed in each sub-area C(w, d);

[0021] According to the upper limit of the transmission power of each macro base station radio frequency device, configure the corresponding transmission power P M for each macro base station;

[0022] Merge the macro base station coordinate space and the macro base station transmission power space to obtain the complete macro base station parameter combination U M = [X M , Y M , N M , P M ], and the combination number is α; rewrite the macro base station parameter combination as

[0023] Further, the step of calculating the sub-area coverage rate according to the macro base station parameter combination , combined with the obstacle coordinates, and screening to obtain the macro base station sample space Ω 0 , specifically includes:

[0024] Calculate the sub-area coverage rate under each parameter combination respectively, and screen the macro base station parameter combination to remove parameter combinations that do not meet the coverage requirements; the specific calculation formula is:

[0025] P = P rob{SINR d′ > θ}

[0026] In the formula, d' is the distance between the current downlink receiving device and the central macro base station of the sub-region; SINR represents the signal-to-interference-to-noise ratio; and P is the sub-region coverage rate.

[0027] The coordinates of each macro base station in the macro base station parameter combination are compared with the coordinates of the obstacle. If there is a location overlap, the obstacle is removed from the macro base station sample space.

[0028] After two rounds of screening and elimination, the macro base station sample space Ω was obtained. 0 The space size is β, where β < α. The macro base station sample space is rewritten as follows:

[0029] Furthermore, the macro base station sample space Ω 0 The sample space Ω of a small-scale macro base station is obtained by narrowing it down. 1 Based on the sample space Ω of small-scale macro base stations 1 The steps for deploying equal-power micro base stations at different densities in each sub-region specifically include:

[0030] By using a simple random sampling method, in the macro base station sample space Ω 0 A sample of size t% is randomly selected from the sample to obtain the small-scale macro base station sample space Ω. 1 The sample space size is [a, b], where a ≥ 0 and b ≤ β. The sample space of a small-scale macro base station is written as... in

[0031] Extracting the sample space Ω of small-scale macro base stations 1 For different macro base station samples, micro base stations are deployed using different distribution density coefficients for each sub-region within each macro base station parameter combination. The distribution of micro base stations follows the PPP distribution, and the expression for the PPP distribution model is as follows:

[0032]

[0033] In the formula, Q c ρ represents the area to be deployed. c N(Q) is the distribution density coefficient. c ) = n is the number of randomly deployed points, and the points in the region follow a Poisson distribution;

[0034] Finally, for each set of macro base station parameter combinations, j I The group of micro base station parameters are combined and correspond to each other, where I is Ω. 1 The macro base station sample number in the data is I∈[1,ba].

[0035] Furthermore, the number of micro base stations and their spatial coordinates in each sub-region are collected, and the sample space Ω of the small-scale macro base stations is compared with that of the micro base stations. 1The steps to merge samples to obtain the complete sample space include:

[0036] The coordinates of the micro base stations in each sub-region are determined, and the number of micro base stations in each sub-region is saved, thus obtaining the complete combination of micro base station parameters. The number of combinations is Where the subscript m represents a micro base station and the superscript C is the sub-region number; This represents a vector containing the coordinates of all micro base stations within each sub-region. This represents a vector containing the total number of micro base stations in each sub-region;

[0037] The micro base station parameter combinations are merged with the corresponding macro base station parameter combinations to form a complete sample space Ω. 2 .

[0038] Furthermore, the energy consumption assessment model is used to evaluate the complete sample space Ω. 2 A coarse sample set Ω was obtained through screening. 3 For the coarsely selected sample set Ω 3 The samples are sorted to form a selected sample set Ω 4 The steps specifically include:

[0039] An energy consumption assessment model is established; the evaluation function of the energy consumption assessment model is:

[0040] EE = R / P

[0041]

[0042] In the formula, EE represents energy efficiency; R represents the total regional speed; P represents the total regional energy consumption; B represents the network bandwidth; and P u The downlink base station transmit power connected to user u represents the downlink user allocation search based on the minimum distance principle, d. c,u Represents the downlink transmission distance, γ represents the path loss factor, and h c,u The path loss term is represented by σ, where T represents regional co-channel interference. 2 Represents noise interference, U represents the number of users in each area, u represents the user ID, u∈[1,U], c represents the area ID, c∈[1,C];

[0043] The constraints of the energy consumption assessment model are:

[0044] 0 <P M <P M,Max

[0045]

[0046] In the formula, P M,Max Indicates the upper limit of radio frequency transmit power. S represents the predicted total regional rate demand. cov Indicates the total coverage area of ​​the region. This indicates the coverage provided by the regional C macro base station. This indicates the coverage provided by the micro base station in area c. This indicates the minimum coverage area constraint for the region. This indicates the overlapping coverage area of ​​macro and micro base stations in a heterogeneous network.

[0047] By applying the constraints of the energy consumption assessment model to the complete sample space Ω 2 A coarse sample set Ω was obtained through screening. 3 ;

[0048] The evaluation function of the energy consumption assessment model is used to evaluate the coarsely selected sample set Ω. 3 Each sample in the dataset is evaluated, and the samples are ranked according to their energy consumption (EE) values. The top 5% are selected to form a refined sample set Ω. 4 .

[0049] Furthermore, the use of network simulation software to refine the sample set Ω 4 Perform network simulation and output the optimal network deployment scheme U for the area to be deployed. * The steps specifically include:

[0050] Using network simulation software, a carefully selected sample set Ω was analyzed. 4 Simulations are performed on each sample, and the optimal 5G heterogeneous network deployment scheme suitable for the current large-scale complex areas is obtained based on the simulation results. The optimal scheme parameter combination is then output. The superscript asterisk (*) indicates the optimal parameters. It is represented as a vector containing the coordinates of all macro base stations within the entire region. This represents a vector containing the coordinates of all micro base stations within each sub-region. It is represented as a vector containing the transmit power of all macro base stations in the entire region. This represents a vector containing the total number of micro base stations in each sub-region.

[0051] Secondly, the present invention provides a large-scale, complex scenario 5G heterogeneous network deployment system, comprising:

[0052] The spatial coordinate construction module is used to obtain a 3D map of the area to be deployed, construct the three-dimensional spatial coordinates of the area, and mark the coordinates of obstacles.

[0053] The macro base station deployment module is used to divide the area to be deployed into several equal-area sub-regions, set up a macro base station at the center of each sub-region, and generate macro base station parameter combinations.

[0054] The macro base station sample space module is used to combine macro base station parameters. The coverage rate of sub-regions is calculated, and the macro base station sample space Ω is obtained by combining the coordinates of obstacles. 0 ;

[0055] The micro base station deployment module is used to deploy the macro base station sample space Ω 0 The sample space Ω of a small-scale macro base station is obtained by narrowing it down. 1 Based on the sample space Ω of small-scale macro base stations 1 Deploy equal-power micro base stations of different densities in each sub-region;

[0056] The micro base station sample space module is used to collect the number and spatial coordinates of micro base stations in each sub-region, and to compare them with the small-scale macro base station sample space Ω. 1 The complete sample space Ω is obtained by merging. 2 ;

[0057] The fine-tuning sample module is used to refine the complete sample space Ω using an energy consumption assessment model. 2 A coarse sample set Ω was obtained through screening. 3 For the coarsely selected sample set Ω 3 The samples are sorted to form a selected sample set Ω 4 ;

[0058] The simulation module is used to refine the sample set Ω using network simulation software. 4 Perform network simulation and output the optimal network deployment scheme U for the area to be deployed. * .

[0059] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0060] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

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

[0062] This invention discloses a method and related apparatus for deploying 5G heterogeneous networks in large-scale complex scenarios. Addressing the actual deployment needs of existing networks, an energy consumption assessment model is established to maximize the overall network energy efficiency ratio. Constraints include regional user rate demand constraints, downlink channel interference limits, regional coverage constraints, radio frequency power upper limit constraints, and frequency domain resource allocation constraints. The model considers actual geographical factors, including obstacles such as mountains, rivers, and buildings. Furthermore, the model considers the operational phase during the planning stage, using a data-driven regional traffic prediction model to predict the traffic demand of each region over the next few years, which serves as the threshold for the aforementioned regional user and rate demand constraints. By optimizing the above model, a highly feasible regional optimal energy-efficient network deployment scheme is obtained. This invention addresses large-scale complex scenarios, including mixed scenarios such as densely populated areas, commercial office areas, and industrial areas, establishing a highly complex and feasible network deployment model and proposing a fast and efficient optimization solution method, providing an optimal feasible solution for city-level heterogeneous network deployment.

[0063] Furthermore, to control the time cost of solving the model and reduce the simulation overhead of network simulation software, this invention proposes a network deployment method that combines ordered optimization and network planning software simulation. Ordered optimization minimizes the sample space while retaining high-quality feasible solutions, and the optimal feasible solution is obtained based on the simulation results from the network planning software. This method first addresses the analytical difficulties of traditional optimization methods when dealing with large-scale non-convex problems, and secondly solves the problems of sample redundancy and excessive overhead in large-scale network simulation, while ensuring the optimality of the final network deployment scheme. By optimizing the deployment density and transmission power of heterogeneous base stations in a region, regional energy efficiency is maximized, network deployment costs are reduced, and the network service quality and overall coverage indicators of each sub-region are guaranteed, thereby achieving energy-saving, environmentally friendly, and green deployment of large-scale networks. Moreover, this method considers various practical deployment constraints and incorporates operational prediction results during the planning stage to simulate real-world scenarios to the greatest extent possible, minimizing the use of prior knowledge. The sequential optimization method differs from traditional analytical solution methods. It narrows down the sample by arranging the samples in sequence and replaces complex quantitative analysis with qualitative analysis. In practical engineering, it is often not necessary to pursue the global optimal solution. A more reasonable goal is to obtain a sufficiently good solution from a large number of samples, and this solution has a certain probability of satisfying a sufficiently good layout planning scheme. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart of the method according to an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the system according to an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of the computer device structure according to an embodiment of the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0069] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0070] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0071] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0072] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0073] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and defined, if the terms "set", "install", "connect", and "couple" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0074] The following further describes the present invention in detail with reference to the accompanying drawings:

[0075] See Figure 1 , the embodiments of the present invention disclose a 5G heterogeneous network deployment method based on order optimization for large-scale complex scenarios, including the following steps:

[0076] S1. Input the 3D map of the area to be deployed, construct the three-dimensional space coordinates of the area, so that any position in the area corresponds to a unique three-dimensional coordinate;

[0077] Use (X, Y, Z) as the unique coordinate of any position in the area. In particular, this step focuses on marking the coordinates of all buildings and obstacles (such as mountains, rivers, etc.) in the area.

[0078] S2. Divide the spatial area into multiple sub-areas of equal size, and set up a macro base station at the center of each sub-area;

[0079] According to the 3D map of the area input in S1, form a 2D area plan view and perform grid division. Considering that a macro base station will be deployed in the center of each sub-area grid later, and the service coverage range of each macro base station can be approximated as a circular area with a radius of 300 - 400 meters, the entire area is divided into C sub-areas, and each sub-area is divided into squares with a side length of L.

[0080] Perform grid division on the 2D area from bottom to top and from left to right (from south to north, from west to east), and label each grid, using C(w, d) for distinction, where w represents the row number of the current sub-area, and d represents the column number of the current sub-area. For example, C(1, 1) represents the sub-area with a side length of L corresponding to the first row and the first column. If the remaining area cannot form a square sub-area with a side length of L when dividing the last row and the last column, the area is divided into L×H, where H < L represents a quadrilateral area with a length of L and a width of H (or a length of H and a width of L).

[0081] Deploy a macro base station at the center of each square sub-area. For the quadrilateral area with a length of L and a width of H (or a length of H and a width of L), also deploy a macro base station at its center position.

[0082] S3. Collect the total number of macro base stations and the spatial coordinates of each macro base station. Combine this with the constraints of radio frequency equipment parameters to set the transmit power of the macro base stations and generate macro base station parameter combinations.

[0083] The number N of macro base stations deployed in S2 is collected. M Since the total area of ​​the region is fixed, the number of macro base stations N deployed varies with the change of the sub-region grid side length L. M This will also change accordingly, where the subscript M represents a macro base station. Combining the 2D map and coordinates, the coordinates of the macro base stations deployed in each sub-region C(w, d) are collected. For example, the coordinates of the macro base station in C(1, 1) should be (L / 2, L / 2), thus obtaining the macro base station coordinates (X). M ,Y M As part of the macro base station parameter combination, (X) M ,Y M ) is represented as a vector containing the coordinates of all macro base stations within the entire region.

[0084] The main constraints on radio frequency (RF) equipment parameters include the maximum transmit power supported by a single RF device. Based on the transmit power limit of each macro base station's RF device, a corresponding transmit power P is configured for each macro base station. M And as part of the macro base station parameter combination, where P M It is represented as a vector containing the transmit power of all macro base stations in the entire region.

[0085] By merging the macro base station coordinate space and the macro base station transmit power space, a complete macro base station parameter combination U is obtained. M =[X M ,Y M N M ,P M The number of combinations is α, therefore the macro base station parameter combinations can be rewritten as follows: like This represents the coordinates of all macro base stations, the number of macro base stations in the area, and the corresponding transmission power corresponding to the first parameter combination.

[0086] S4. Calculate the current area coverage rate, and filter the macro base station parameter combinations based on the coverage rate index and obstacle coordinates to generate the macro base station sample space Ω. 0 ;

[0087] Through P = Ρrob{SINR d′The sub-region coverage rate is calculated for each parameter combination, where SINR represents the Signal-to-Interference-plus-Noise Ratio, and d' is the distance between the current downlink receiving device and the macro base station at the center of the sub-region. The formula states that if the SINR value received by a user at the current location d' is greater than a certain threshold θ, then coverage is considered to be achieved within the circular area of ​​radius d' centered on the macro base station in the current sub-region. The macro base station parameter combinations are then filtered, and parameter combinations α that do not meet the coverage requirements are eliminated.

[0088] The coordinates of buildings and obstacles (such as mountains and rivers) in the area collected by S1 are compared with the coordinates of each macro base station in the macro base station parameter combination obtained in S3. If there is a location overlap, it is determined that the current location is subject to force majeure and cannot deploy a macro base station. The corresponding combination scheme is unreasonable and is removed from the macro base station sample space.

[0089] This completes the selection of macro base station parameter combinations, resulting in the macro base station sample space Ω. 0 The space size is β, where β < α. Therefore, the macro base station sample space can be rewritten as follows:

[0090] S5. The sample space is reduced by a simple random sampling method to obtain the small-scale macro base station sample space Ω. 1 ;

[0091] By using a simple random sampling method, a sample of size t% is randomly drawn from the macro base station sample space to obtain the small-scale macro base station sample space Ω. 1 The sample space size is [a, b], where a ≥ 0 and b ≤ β. Therefore, the small sample space of the macro base station can be rewritten as follows: in

[0092] S6. Set different distribution density coefficients and adopt PPP distribution to deploy equal-power micro base stations with different densities in each sub-region;

[0093] Extract Ω 1 For different macro base station samples, repeat the following steps for each macro base station parameter combination:

[0094] For each sub-region, micro base stations are deployed using different distribution density coefficients. The distribution of micro base stations follows a PPP (Poisson Point Process) distribution model. Among them |Q c | represents the area to be deployed. ρ c N(Q) is the distribution density coefficient. c) = n is the number of randomly deployed points, and the points in the region follow a Poisson distribution.

[0095] At this point, for each set of macro base station parameter combinations, there will be j I The group of micro base station parameters are combined and correspond to each other, where I is Ω. 1 The macro base station sample number in the data is I∈[1,ba].

[0096] S7. Collect the number of micro base stations and spatial coordinates of each sub-region, and compare them with Ω. 1 Merged into a complete sample space Ω 2 ;

[0097] Based on the 2D map constructed in S1, the coordinates of micro base stations in each region of S6 are determined, and the number of micro base stations in each sub-region is saved, thus obtaining the complete combination of micro base station parameters. The number of combinations is The subscript 'm' indicates a micro base station, and the superscript 'C' corresponds to the sub-region number in S2. This represents a vector containing the coordinates of all micro base stations within each sub-region. This represents a vector containing the total number of micro base stations in each sub-region. Unlike the macro base station parameter combination, since it is assumed that the transmit power of each micro base station is fixed, the transmit power of the micro base station is not included in the micro base station parameter combination.

[0098] The micro base station parameter combinations are merged with the corresponding macro base station parameter combinations to form a complete sample space Ω. 2 .

[0099] S8. Based on the model constraints, filter the complete sample space to obtain the coarse sample set Ω. 3 ;

[0100] An energy consumption assessment model is established, which consists of two parts:

[0101] (1) Evaluation function: The sole criterion for evaluating the quality of samples in the sample space and ranking the samples. The model is as follows:

[0102] EE = R / P

[0103]

[0104] Where EE represents energy efficiency, R represents the total regional speed, P represents the total regional energy consumption, B represents network bandwidth, and P u The downlink base station transmit power connected to user u represents the downlink user allocation search based on the minimum distance principle, d. c,u Represents the downlink transmission distance, γ represents the path loss factor, and h c,uThe path loss term is represented by σ, where T represents regional co-channel interference. 2 The noise interference is represented by U, the number of users in each area, u represents the user ID, u∈[1,U], and c represents the area ID, c∈[1,C].

[0105] (2) Constraints: Each sample must meet QoS restrictions; if not, the sample set Ω will be selected from the initial sample set. 3 The model for removing middleware is as follows:

[0106] 0 <P M <P M,Max

[0107]

[0108] Among them, P M,Max Indicates the upper limit of radio frequency transmit power. S represents the predicted total regional rate demand. cov Indicates the total coverage area of ​​the region. This indicates the coverage provided by the regional C macro base station. This indicates the coverage provided by the micro base station in area c. This indicates the minimum coverage area constraint for the region. This indicates the overlapping coverage area of ​​macro and micro base stations in a heterogeneous network.

[0109] Based on the constraints of the energy consumption assessment model, the complete sample space Ω is... 2 Each sample in the dataset is filtered to obtain a coarse sample set Ω. 3 .

[0110] S9. Sort the samples according to the objective function, and take the top 5% of the samples to obtain the selected sample set Ω. 4 ;

[0111] Based on the evaluation function of the energy consumption assessment model in S8, the coarsely selected sample set Ω is evaluated. 3 Each sample in the dataset is evaluated, and the samples are ranked according to their energy consumption (EE) values. The top 5% are selected to form a refined sample set Ω. 4 .

[0112] S10. Use network simulation software to perform network simulation on the fine selection set of samples;

[0113] Using professional network planning simulation software, a refined sample set Ω was selected. 4 Each sample in the simulation is performed.

[0114] S11. Output the optimal network deployment scheme U based on the simulation results. * ;

[0115] Based on the simulation results, the optimal 5G heterogeneous network deployment scheme suitable for current large-scale complex areas is obtained, and the optimal scheme parameter combination is output. The superscript asterisk (*) indicates the optimal parameters. It is represented as a vector containing the coordinates of all macro base stations within the entire region. This represents a vector containing the coordinates of all micro base stations within each sub-region. It is represented as a vector containing the transmit power of all macro base stations in the entire region. This represents a vector containing the total number of micro base stations in each sub-region.

[0116] See Figure 2 This invention discloses a large-scale complex 5G heterogeneous network deployment system, including a spatial coordinate construction module, a macro base station establishment module, a macro base station sample space module, a micro base station deployment module, a micro base station sample space module, a fine selection sample module, and a simulation module.

[0117] It should be noted that the spatial coordinate construction module is used to acquire a 3D map of the area to be deployed, construct the three-dimensional spatial coordinates of the area, and mark the coordinates of obstacles; the macro base station setting module is used to divide the area to be deployed into several equal-area sub-regions, set up a macro base station at the center of each sub-region, and generate macro base station parameter combinations. The macro base station sample space module is used to combine macro base station parameters. The coverage rate of sub-regions is calculated, and the macro base station sample space Ω is obtained by combining the coordinates of obstacles. 0 The micro base station deployment module is used to deploy the macro base station sample space Ω. 0 The sample space Ω of a small-scale macro base station is obtained by narrowing it down. 1 Based on the sample space Ω of small-scale macro base stations 1 Deploy equal-power micro base stations at different densities in each sub-region; a micro base station sample space module is used to collect the number and spatial coordinates of micro base stations in each sub-region, and compare them with the small-scale macro base station sample space Ω. 1 The complete sample space Ω is obtained by merging. 2 The sample selection module is used to refine the complete sample space Ω using an energy consumption assessment model. 2 A coarse sample set Ω was obtained through screening. 3 For the coarsely selected sample set Ω 3 The samples are sorted to form a selected sample set Ω 4 The simulation module is used to refine the sample set Ω using network simulation software. 4 Perform network simulation and output the optimal network deployment scheme U for the area to be deployed. * .

[0118] This invention, through acquiring a 3D map of the area to be deployed and constructing its three-dimensional spatial coordinates, comprehensively and accurately grasps the terrain and obstacle distribution of the area. Based on this, the area is divided into several sub-regions, and a macro base station is established at the center of each sub-region, greatly simplifying the deployment process and improving deployment efficiency. By calculating the coverage rate of the sub-regions and combining it with obstacle coordinates, a macro base station sample space is selected to ensure good network coverage for each sub-region. Simultaneously, by reducing the macro base station sample space and deploying equal-power micro base stations, the network coverage and signal strength are further enhanced, improving the overall network performance. Then, the complete sample space is filtered using an energy consumption assessment model, and the sample space is reduced as much as possible through order optimization, retaining high-quality feasible solutions. The optimal feasible solution is obtained based on the simulation results of network planning software. This method first solves the analytical difficulties of traditional optimization methods when facing large-scale non-convex problems, and secondly, it solves the problems of sample redundancy and excessive overhead in large-scale network simulation, while ensuring the optimality of the final network deployment scheme.

[0119] In one embodiment of the invention, see [link to embodiment]. Figure 3 A computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used in the operation of 5G heterogeneous network deployment methods in large-scale complex scenarios.

[0120] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the 5G heterogeneous network deployment method for large-scale complex scenarios described in the above embodiments.

[0121] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for deploying 5G heterogeneous networks in large-scale complex scenarios, characterized in that, Includes the following steps: Obtain a 3D map of the area to be deployed, construct the area's three-dimensional spatial coordinates, and label the coordinates of obstacles; The area to be deployed is divided into several sub-regions of equal area. A macro base station is set up in the center of each sub-region, and the macro base station parameter combination is generated. Based on macro base station parameter combinations The coverage rate of sub-regions is calculated, and the macro base station sample space Ω is obtained by combining the coordinates of obstacles. 0 ; Macro base station sample space Ω 0 The sample space Ω of a small-scale macro base station is obtained by narrowing it down. 1 Based on the sample space Ω of small-scale macro base stations 1 Deploy equal-power micro base stations of different densities in each sub-region; Collect the number and spatial coordinates of micro base stations in each sub-region, and compare them with the sample space Ω of small-scale macro base stations. 1 The complete sample space Ω is obtained by merging. 2 ; The complete sample space Ω was analyzed using an energy consumption assessment model. 2 A coarse sample set Ω was obtained through screening. 3 For the coarsely selected sample set Ω 3 The samples are sorted to form a selected sample set Ω 4 ; Network simulation software was used to refine the sample set Ω 4 Perform network simulation and output the optimal network deployment scheme U for the area to be deployed. * .

2. The method for deploying 5G heterogeneous networks in large-scale complex scenarios according to claim 1, characterized in that, The deployment area is divided into several equal-area sub-regions, a macro base station is set up at the center of each sub-region, and a macro base station parameter combination is generated. The steps specifically include: Based on the 3D map of the area to be deployed, a 2D area plan is generated and gridded, dividing the entire area into C equal-sized sub-areas, each sub-area being divided into squares with a side length of L; Each grid cell is labeled and distinguished using C(w, d), where w represents the row number of the current sub-region and d represents the column number of the current sub-region. Deploy macro base stations in the center of each sub-region; The total number of macro base stations deployed, N, is collected. M By combining 2D maps and coordinates, the coordinates (X, y) of macro base stations deployed in each sub-region C(w, d) are collected. M ,Y M ); Based on the maximum transmit power of each macro base station's radio frequency equipment, configure a corresponding transmit power P for each macro base station. M ; By merging the macro base station coordinate space and the macro base station transmit power space, a complete macro base station parameter combination U is obtained. M =[X M ,Y M N M ,P M The number of combinations is α; the macro base station parameter combinations are rewritten as follows:

3. The method for deploying 5G heterogeneous networks in large-scale complex scenarios according to claim 1, characterized in that, The combination of macro base station parameters The coverage rate of sub-regions is calculated, and the macro base station sample space Ω is obtained by combining the coordinates of obstacles. 0 The steps specifically include: Calculate the sub-region coverage rate for each parameter combination, and filter the macro base station parameter combinations, eliminating those that do not meet coverage requirements; the specific calculation formula is as follows: P=Prob{SINR d′ >θ} In the formula, d' is the distance between the current downlink receiving device and the macro base station in the center of the sub-region; SINR represents the signal-to-interference-to-noise ratio; and P is the sub-region coverage rate. The coordinates of each macro base station in the macro base station parameter combination are compared with the coordinates of the obstacle. If there is a location overlap, the obstacle is removed from the macro base station sample space. After two rounds of screening and elimination, the macro base station sample space Ω was obtained. 0 The space size is β, where β < α. The macro base station sample space is rewritten as follows:

4. The method for deploying 5G heterogeneous networks in large-scale complex scenarios according to claim 1, characterized in that, The macro base station sample space Ω 0 The sample space Ω of a small-scale macro base station is obtained by narrowing it down. 1 Based on the sample space Ω of small-scale macro base stations 1 The steps for deploying equal-power micro base stations at different densities in each sub-region specifically include: By using a simple random sampling method, in the macro base station sample space Ω 0 A sample of size t% is randomly selected from the sample to obtain the small-scale macro base station sample space Ω. 1 The sample space size is [a, b], where a ≥ 0 and b ≤ β. The sample space of a small-scale macro base station is written as... in Extracting the sample space Ω of small-scale macro base stations 1 For different macro base station samples, micro base stations are deployed using different distribution density coefficients for each sub-region within each macro base station parameter combination. The distribution of micro base stations follows the PPP distribution, and the expression for the PPP distribution model is as follows: In the formula, Q c ρ represents the area to be deployed. c N(Q) is the distribution density coefficient. c ) = n is the number of randomly deployed points, and the points in the region follow a Poisson distribution; Finally, for each set of macro base station parameter combinations, j I The group of micro base station parameter combinations corresponds to it, where I is Ω. 1 The macro base station sample number in the data is I∈[1,ba].

5. A method for deploying 5G heterogeneous networks in large-scale complex scenarios according to claim 1, characterized in that, The number and spatial coordinates of micro base stations in each sub-region are collected, and the sample space Ω of small-scale macro base stations is compared with that of the sample space Ω. 1 The steps to merge samples to obtain the complete sample space include: The coordinates of the micro base stations in each sub-region are determined, and the number of micro base stations in each sub-region is saved, thus obtaining the complete combination of micro base station parameters. The number of combinations is Where the subscript m represents a micro base station and the superscript C is the sub-region number; This represents a vector containing the coordinates of all micro base stations within each sub-region. This represents a vector containing the total number of micro base stations in each sub-region; The micro base station parameter combinations are merged with the corresponding macro base station parameter combinations to form a complete sample space Ω. 2 .

6. The method for deploying 5G heterogeneous networks in large-scale complex scenarios according to claim 1, characterized in that, The energy consumption assessment model is used to evaluate the complete sample space Ω. 2 A coarse sample set Ω was obtained through screening. 3 For the coarsely selected sample set Ω 3 The samples are sorted to form a selected sample set Ω 4 The steps specifically include: An energy consumption assessment model is established; the evaluation function of the energy consumption assessment model is: EE = R / P In the formula, EE represents energy efficiency; R represents the total regional speed; P represents the total regional energy consumption; B represents the network bandwidth; and P u The downlink base station transmit power connected to user u represents the downlink user allocation search based on the minimum distance principle, d. c,u Represents the downlink transmission distance, γ represents the path loss factor, and h c,u The path loss term is represented by σ, where T represents regional co-channel interference. 2 Represents noise interference, U represents the number of users in each area, u represents the user ID, u∈[1,U], c represents the area ID, c∈[1,C]; The constraints of the energy consumption assessment model are: 0<P M <P M,Max In the formula, P M,Max Indicates the upper limit of radio frequency transmit power. S represents the predicted total regional rate demand. cov Indicates the total coverage area of ​​the region. This indicates the coverage provided by the regional C macro base station. This indicates the coverage provided by the micro base station in area c. This indicates the minimum coverage area constraint for the region. This indicates the overlapping coverage area of ​​macro and micro base stations in a heterogeneous network. By applying the constraints of the energy consumption assessment model to the complete sample space Ω 2 A coarse sample set Ω was obtained through screening. 3 ; The evaluation function of the energy consumption assessment model is used to evaluate the coarsely selected sample set Ω. 3 Each sample in the dataset is evaluated, and the samples are ranked according to their energy consumption (EE) values. The top 5% are selected to form a refined sample set Ω. 4 .

7. A method for deploying 5G heterogeneous networks in large-scale complex scenarios according to claim 1, characterized in that, The use of network simulation software to refine the sample set Ω 4 Perform network simulation and output the optimal network deployment scheme U for the area to be deployed. * The steps specifically include: Using network simulation software, a carefully selected sample set Ω was analyzed. 4 Simulations are performed on each sample, and the optimal 5G heterogeneous network deployment scheme suitable for the current large-scale complex areas is obtained based on the simulation results. The optimal scheme parameter combination is then output. The superscript asterisk (*) indicates the optimal parameters. It is represented as a vector containing the coordinates of all macro base stations within the entire region. This represents a vector containing the coordinates of all micro base stations within each sub-region. It is represented as a vector containing the transmit power of all macro base stations in the entire region. This represents a vector containing the total number of micro base stations in each sub-region.

8. A large-scale, complex 5G heterogeneous network deployment system, characterized in that, include: The spatial coordinate construction module is used to obtain a 3D map of the area to be deployed, construct the three-dimensional spatial coordinates of the area, and mark the coordinates of obstacles. The macro base station deployment module is used to divide the area to be deployed into several equal-area sub-regions, set up a macro base station at the center of each sub-region, and generate macro base station parameter combinations. The macro base station sample space module is used to combine macro base station parameters. The coverage rate of sub-regions is calculated, and the macro base station sample space Ω is obtained by combining the coordinates of obstacles. 0 ; The micro base station deployment module is used to deploy the macro base station sample space Ω 0 The sample space Ω of a small-scale macro base station is obtained by narrowing it down. 1 Based on the sample space Ω of small-scale macro base stations 1 Deploy equal-power micro base stations of different densities in each sub-region; The micro base station sample space module is used to collect the number and spatial coordinates of micro base stations in each sub-region, and to compare them with the small-scale macro base station sample space Ω. 1 The complete sample space Ω is obtained by merging. 2 ; The fine-tuning sample module is used to refine the complete sample space Ω using an energy consumption assessment model. 2 A coarse sample set Ω was obtained through screening. 3 For the coarsely selected sample set Ω 3 The samples are sorted to form a selected sample set Ω 4 ; The simulation module is used to refine the sample set Ω using network simulation software. 4 Perform network simulation and output the optimal network deployment scheme U for the area to be deployed. * .

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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