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

Abstract

The invention discloses a large-scale complex scene 5G heterogeneous network deployment method and a related device, and belongs to the technical field of network deployment. According to the method, an energy consumption evaluation model is established according to actual deployment requirements in an existing network, maximization of the overall energy efficiency ratio of the network is taken as a target, and constraints related to the method comprise regional user rate requirement constraints, downlink channel interference constraints, regional coverage rate 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. In addition, the model considers an operation link in a planning stage, predicts the traffic demand of each region in the next few years based on a data-driven region traffic prediction model, and takes the traffic demand as a related region user and rate demand constraint threshold. Through a method of combining ordinal optimization solution and network planning software simulation, a high-feasibility regional optimal energy efficiency network deployment scheme is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of network deployment and relates to a 5G heterogeneous network deployment method and related devices for large-scale complex scenarios. Background Art

[0002] With the rapid development of mobile communication technology, 5G technology has been widely used in industrial, commercial, and civil fields with its advantages of large bandwidth, high speed, and low latency. However, its energy consumption has also increased significantly compared to 4G. According to statistics, the current power consumption of 5G base stations is 3-4 times that of 4G base stations, and the energy efficiency is 12 times that of 4G base stations. For a single base station, taking a single-frequency three-fan communication main device as an example, its radio frequency unit energy consumption can account for more than 75% of the total energy consumption of the site. It is estimated that during the air interface propagation process, about 60% of the signal strength is attenuated due to path loss. Therefore, in order to ensure the receiving signal strength of downlink users, the downlink base station must increase the transmission power to compensate for the signal attenuation, which leads to an increase in the RF energy consumption overhead. 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, as communication technology evolves to 5G-A / 6G technology, network deployment is believed to be realized in high-frequency bands such as millimeter waves, thereby achieving higher transmission rates. Correspondingly, the path loss phenomenon will be more obvious, and the energy consumption challenges faced by the communication field will be more severe.

[0003] To solve the above problems, the current solution is to encrypt sites and reduce the distance between base stations and users in the area through dense networks, thereby reducing signal attenuation during propagation. However, in the same frequency network, increasing the base station deployment density will also lead to greater interference in neighboring areas. Therefore, a reasonable base station deployment strategy is of great significance to maintaining service quality and reducing regional energy consumption.

[0004] At present, the research on heterogeneous network deployment mainly focuses on two aspects. The first is the small-scale base station deployment strategy, which mainly considers 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 an effective reference for large-scale networking. The second is the network deployment strategy involving multiple heterogeneous base stations. In order to reduce the difficulty and time cost of optimization solution, this type of research often only uses the total rate factor as a constraint. Although this method can improve the solution efficiency by simplifying the model, it does not fully consider the actual QoS requirements (quality of service), so it also cannot provide an effective reference for large-scale networking.

[0005] Deploying 5G networks in large-scale and complex scenarios involves complex communication mechanisms, and there are multiple couplings between decision variables. If the base station transmission power is too small, the coverage will be insufficient due to path loss, and if the power is too large, it will interfere with users in neighboring areas, causing their speed to decrease. Professional network simulation software can accurately simulate the effect of network deployment, but as the scale of the deployment area increases, the simulation overhead cost also gradually increases, making it difficult to accurately obtain the optimal deployment plan. Summary of the invention

[0006] The purpose of the present invention is to provide a large-scale and complex scenario 5G heterogeneous network deployment method and related devices to solve the technical problems of the prior art in which the model solving efficiency is low and it is difficult to obtain the optimal solution when the deployment area is large and the scenario is complex.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

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

[0009] 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;

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

[0011] Based on the macro base station parameter combination Calculate the sub-area coverage, combine it with the obstacle coordinates, and filter out the macro base station sample space Ω 0 ;

[0012] The macro base station sample space Ω 0 The sample space of small-scale macro base stations is reduced to Ω 1 , based on the small-scale macro base station sample space Ω 1 Deploy micro base stations of equal power at different densities in each sub-area;

[0013] Collect the number and spatial coordinates of micro base stations in each sub-area and compare them with the small-scale macro base station sample space Ω 1 Merge to get the complete sample space Ω 2 ;

[0014] The energy consumption evaluation model is used to evaluate the complete sample space Ω 2 Screening to obtain the rough sample set Ω 3 , for the coarsely selected sample set Ω 3 Sort the samples in to form a carefully selected sample set Ω 4 ;

[0015] Use network simulation software to select sample set Ω 4 Perform network simulation and output the optimal network deployment solution U for the area to be deployed * .

[0016] Furthermore, the area to be deployed is divided into a number of sub-areas of equal area, a macro base station is set up in the center of each sub-area, and a macro base station parameter combination is generated. The steps include:

[0017] Based on the 3D map of the area to be deployed, a 2D area plan is formed and gridded to divide the entire area into C sub-areas of equal size. Each sub-area is divided into squares with a side length of L.

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

[0019] Deploy macro base stations at the center of each sub-region;

[0020] Collect the number of all deployed macro base stations N M , combined with the 2D map and coordinates, collect the coordinates (X M ,Y M );

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

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

[0023] Further, the macro base station parameter combination Calculate the sub-area coverage, combine it with the obstacle coordinates, and filter out the macro base station sample space Ω 0 The steps include:

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

[0025] P = Prob {SINR d′ >θ}

[0026] Where d' is the distance between the current downlink receiving device and the macro base station in the sub-area center; SINR represents the signal-to-interference-to-noise ratio; P is the sub-area coverage rate;

[0027] Compare the coordinates of each macro base station in the macro base station parameter combination with the coordinates of the obstacle. If there is any overlap in position, remove it from the macro base station sample space;

[0028] After two rounds of screening and elimination, we get the macro base station sample space Ω 0 , the space size is β, where β<α, and the macro base station sample space is rewritten as

[0029] Furthermore, the macro base station sample space Ω 0 The sample space of small-scale macro base stations is reduced to Ω 1 , based on the small-scale macro base station sample space Ω 1 The steps of deploying equal-power micro base stations of different densities in each sub-area specifically include:

[0030] Through a simple random sampling method, in the macro base station sample space Ω 0 Randomly select t% of the samples from the sample space to obtain the small-scale macro base station sample space Ω 1 , the sample space size is [a,b], where a≥0, b≤β, and the sample space of small-scale macro base stations is written as in

[0031] Extract small-scale macro base station sample space Ω 1 For different macro base station samples in , in each set of macro base station parameter combinations, different distribution density coefficients are used to deploy micro base stations for each sub-area, where the distribution of micro base stations follows the PPP distribution. The expression of the PPP distribution model is:

[0032]

[0033] In the formula, Q c is the area to be deployed, ρ c is the distribution density coefficient, N(Q c ) = n is the number of random deployment points, and each point in the area conforms to the Poisson distribution;

[0034] Finally, for each set of macro base station parameter combinations, there are j I The micro base station parameter combination of the group corresponds to it, where I is Ω 1 The macro base station sample number in , I∈[1,ba].

[0035] Furthermore, the number and spatial coordinates of the micro base stations in each sub-area are collected and compared with the small-scale macro base station sample space Ω 1The steps of merging to obtain a complete sample space include:

[0036] Determine the coordinates of the micro base station in each sub-area and save the number of micro base stations in each sub-area to obtain a complete combination of micro base station parameters The number of combinations is The subscript m indicates the micro base station, and the superscript C indicates the sub-region number; represents the vector containing the coordinates of all micro base stations in each sub-area, represents a vector containing the total number of micro base stations in each sub-area;

[0037] The micro base station parameter combination is combined with the corresponding macro base station parameter combination to form a complete sample space Ω 2 .

[0038] Furthermore, the energy consumption evaluation model is used to evaluate the complete sample space Ω 2 Screening to obtain the rough sample set Ω 3 , for the coarsely selected sample set Ω 3 Sort the samples in to form a carefully selected sample set Ω 4 The steps include:

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

[0040] EE=R / P

[0041]

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

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

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

[0045]

[0046] Where P M,Max Indicates the upper limit of RF transmission power. represents the predicted total regional rate demand, S cov represents the total coverage area of ​​the region, represents the coverage provided by the macro base station in area c, represents the coverage provided by the micro base station in area c, represents the minimum coverage area constraint of the region, Indicates the overlapping coverage area of ​​macro and micro base stations in a heterogeneous network;

[0047] Through the constraints of the energy consumption evaluation model, the complete sample space Ω 2 Screening to obtain the rough sample set Ω 3 ;

[0048] Through the evaluation function of the energy consumption evaluation model, the rough selection sample set Ω 3 Each sample in the EE is evaluated, and the samples are sorted according to the energy consumption EE value, and the top 5% are selected to form a finely selected sample set Ω 4 .

[0049] Furthermore, the network simulation software is used to select the sample set Ω 4 Perform network simulation and output the optimal network deployment solution U for the area to be deployed * The steps include:

[0050] Using network simulation software, we can select a sample set Ω 4 Simulate each sample in the simulation, and obtain the optimal 5G heterogeneous network deployment solution suitable for the current large-scale and complex areas according to the simulation results, and output the optimal solution parameter combination The superscript asterisk (*) indicates the optimal parameter. Represented as a vector containing the coordinates of all macro base stations in the entire area, represents the vector containing the coordinates of all micro base stations in each sub-area, It is represented as a vector containing the transmit power of all macro base stations in the entire area, Represents a vector containing the total number of micro base stations in each sub-area.

[0051] In a second aspect, the present invention provides a large-scale complex scenario 5G heterogeneous network deployment system, including:

[0052] The spatial coordinate construction module is used to obtain the 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 establishment module is used to divide the area to be deployed into several sub-areas of equal area, establish a macro base station in the center of each sub-area, and generate a macro base station parameter combination

[0054] The macro base station sample space module is used to combine Calculate the sub-area coverage, combine it with the obstacle coordinates, and filter out the macro base station sample space Ω 0 ;

[0055] The micro base station deployment module is used to convert the macro base station sample space Ω 0 The sample space of small-scale macro base stations is reduced to Ω 1 , based on the small-scale macro base station sample space Ω 1 Deploy micro base stations of equal power at different densities in each sub-area;

[0056] The micro base station sample space module is used to collect the number and spatial coordinates of micro base stations in each sub-area and the small-scale macro base station sample space Ω 1 Merge to get the complete sample space Ω 2 ;

[0057] The sample selection module is used to evaluate the complete sample space Ω through the energy consumption evaluation model. 2 Screening to obtain the rough sample set Ω 3 , for the coarsely selected sample set Ω 3 Sort the samples in to form a carefully selected sample set Ω 4 ;

[0058] The simulation module is used to use network simulation software to select sample sets Ω 4 Perform network simulation and output the optimal network deployment solution U for the area to be deployed * .

[0059] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0060] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

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

[0062] The present invention discloses a large-scale complex scene 5G heterogeneous network deployment method and related devices. Aiming at the actual deployment needs in the existing network, an energy consumption evaluation model is established to maximize the overall network energy efficiency ratio. The constraints involved include regional user rate demand constraints, downlink channel interference restrictions, regional coverage constraints, radio frequency power upper limit constraints, and frequency domain resource allocation constraints. The model takes into account actual geographical factors, including obstacles such as mountains, rivers, and buildings. In addition, the model also considers the operation link in the planning stage. Based on a data-driven regional traffic prediction model, the traffic demand of each region in the next few years is predicted, and it is used as the regional user and rate demand constraint threshold involved in the above. By optimizing and solving the above optimization model, a highly feasible regional optimal energy efficiency network deployment plan is obtained. The present invention is oriented to large-scale complex scenarios, including multi-mixed scenarios such as densely populated areas, commercial office areas, and industrial areas. A highly complex and highly feasible network deployment model is established, and a fast and efficient optimization solution method is proposed to provide the best feasible solution for city-level heterogeneous network deployment.

[0063] Furthermore, in order to control the time cost of solving the model and reduce the simulation overhead of the network simulation software, the present invention proposes a network deployment method that combines sequential optimization solution with network planning software simulation, wherein the sample space is reduced as much as possible through sequential optimization, high-quality feasible solutions are retained, and the optimal feasible solution is obtained according to the simulation results of the network planning software. This method first solves the analytical difficulty of traditional optimization solution methods when facing large-scale non-convex problems, and secondly solves the sample redundancy and high overhead problems of large-scale network simulation, while ensuring the optimality of the final network deployment plan. By optimizing the deployment density and transmission power of regional heterogeneous base stations, 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 realizing energy conservation, environmental protection and green deployment of large-scale networks. In addition, this method takes into account a variety of actual deployment constraints, and combines the operation prediction results in the planning stage to simulate the real scene to the maximum extent and reduce human prior knowledge as much as possible. The ordinal optimization method is different from the traditional analytical solution method. It reduces the sample size by arranging them in order and replaces complex quantitative analysis with qualitative analysis. That is, in actual engineering, it is often not necessary to pursue the global optimal solution. A more reasonable goal is to obtain a good enough solution among many samples, and this solution has a certain probability of satisfying a good enough layout planning solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

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

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

[0067] Figure 3 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0069] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

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

[0072] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which 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 limited, if the terms "set", "install", "connected", "connected" 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 , an embodiment of the present invention discloses 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 coordinates of any position in the area. In particular, the coordinates of all buildings and obstacles (such as mountains, rivers, etc.) in the area are marked emphatically in this step.

[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 approximately 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, and set the macro base station transmit power to generate a macro base station parameter combination in combination with the RF equipment parameter constraints.

[0083] Collect the number of macro base stations N deployed in S2 M , since the total area of ​​the region is fixed, as the length of the sub-region grid L changes, the number of deployed macro base stations N M The subscript M represents the macro base station. Combining the 2D map and the coordinates, the coordinates of the macro base station deployed in each sub-area C(w, d) are collected. For example, the coordinates of the macro base station in C(1, 1) should be (L / 2, L / 2), and then the coordinates of the macro base station (X M ,Y M ) as part of the macro base station parameter combination, where (X M ,Y M ) is represented as a vector containing the coordinates of all macro base stations in the entire area.

[0084] The RF device parameter constraints mainly include the maximum transmit power supported by a single RF device. According to the transmit power upper limit of each macro base station RF device, the 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 area.

[0085] Combine the macro base station coordinate space and the macro base station transmit power space to obtain a complete macro base station parameter combination U M =[X M ,Y M ,N M ,P M ], the number of combinations is α, so the macro base station parameter combination can be rewritten as like Indicates the location 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 coverage rate of the current area, select the macro base station parameter combination according to the coverage rate index and the obstacle coordinates, and generate the macro base station sample space Ω 0 ;

[0087] By P = Prob {SINR d′>θ} respectively calculate the sub-area coverage under each parameter combination, where SINR represents the signal to interference plus noise ratio (Signal to Interference plus Noise Ratio), d' is the distance between the current downlink receiving device and the macro base station in the center of the sub-area, and the above formula means that if the SINR value received by the user at the current d' position is greater than a certain threshold θ, it is considered that the current sub-area can be covered within the circular area with a radius of d' centered on the macro base station. The macro base station parameter combinations are screened, and the parameter combination α that does not meet the coverage requirements will be eliminated.

[0088] The coordinates of regional buildings and obstacles (such as mountains, rivers, etc.) 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 position overlap, it is determined that the current location is subject to force majeure and a macro base station cannot be deployed. The corresponding combination plan is unreasonable and is eliminated from the macro base station sample space.

[0089] At this point, the screening of macro base station parameter combinations is completed, and the macro base station sample space Ω is obtained. 0 , the space size is β, where β<α, so the macro base station sample space can be rewritten as

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

[0091] By using a simple random sampling method, we randomly select t% of the samples in 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, b≤β, so the small sample space of the macro base station can be rewritten as in

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

[0093] Extraction of Ω 1 For different macro base station samples in the example, repeat the following steps for each set of macro base station parameter combinations:

[0094] For each sub-area, different distribution density coefficients are used to deploy micro base stations, where the distribution of micro base stations follows the PPP (Poisson point process) distribution. The PPP distribution model is: Where |Q c | is the area to be deployed, ρ c is the distribution density coefficient, N(Q c) = n is the number of random deployment points, and each point in the area conforms to the Poisson distribution.

[0095] At this time, for each set of macro base station parameter combinations, there will be j I The micro base station parameter combination of the group corresponds to it, where I is Ω 1 The macro base station sample number in , I∈[1,ba].

[0096] S7, collect the number and spatial coordinates of micro base stations in each sub-area, and Ω 1 Merge into the complete sample space Ω 2 ;

[0097] Based on the 2D map constructed in S1, the coordinates of the micro base stations in each area in S6 are determined, and the number of micro base stations in each sub-area is saved, so as to obtain a complete combination of micro base station parameters. The number of combinations is The subscript m represents a micro base station, and the superscript C corresponds to the sub-area number in S2. represents the vector containing the coordinates of all micro base stations in each sub-area, Represents a vector containing the total number of micro base stations in each sub-area. Different from the macro base station parameter combination, since it is assumed that the transmission power of each micro base station is fixed, the micro base station transmission power is not included in the micro base station parameter combination.

[0098] The micro base station parameter combination is combined with the corresponding macro base station parameter combination to form a complete sample space Ω 2 .

[0099] S8. Filter the complete sample space according to the model constraints to obtain the rough sample set Ω 3 ;

[0100] Establish an energy consumption assessment model, which consists of two parts:

[0101] (1) Evaluation function: the only 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] EE stands for energy efficiency, R stands for the total rate of the region, P stands for the total energy consumption of the region, B stands for network bandwidth, and P u represents the downlink base station transmission power connected to user u. The downlink user allocation search is based on the minimum distance principle. c,u represents the downlink transmission distance, γ represents the path loss factor, and h c,urepresents the path loss term, T represents the regional co-channel interference, σ 2 represents noise interference, U represents the number of users in each area, u represents the user number, u∈[1,U], and c represents the area number, c∈[1,C].

[0105] (2) Constraints: QoS constraints that each sample must meet. If not, the sample is selected from the coarse sample set Ω. 3 The model is as follows:

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

[0107]

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

[0109] Based on the constraints of the energy consumption evaluation model, the complete sample space Ω 2 Each sample in is screened to obtain the rough 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 finely selected sample set Ω 4 ;

[0111] Based on the evaluation function of the energy consumption evaluation model in S8, the rough selection sample set Ω 3 Each sample in the EE is evaluated, and the samples are sorted according to the energy consumption EE value, and the top 5% are selected to form a finely selected sample set Ω 4 .

[0112] S10, using network simulation software to perform network simulation on the fine selection set samples;

[0113] Using professional network planning simulation software, carefully selected sample sets Ω 4 Each sample in is simulated.

[0114] S11. Output the optimal regional network deployment solution U according to the simulation results. * ;

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

[0116] See also Figure 2 An embodiment of the present invention discloses a large-scale complex scenario 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 sample selection module and a simulation module.

[0117] It should be noted that 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 establishment module is used to divide the area to be deployed into several sub-areas of equal area, establish a macro base station in the center of each sub-area, and generate a macro base station parameter combination. The macro base station sample space module is used to combine Calculate the sub-area coverage, combine it with the obstacle coordinates, and filter out the macro base station sample space Ω 0 ; Micro base station deployment module, used to convert the macro base station sample space Ω 0 The sample space of small-scale macro base stations is reduced to Ω 1 , based on the small-scale macro base station sample space Ω 1 Deploy micro base stations of equal power with different densities in each sub-area; the micro base station sample space module is used to collect the number and spatial coordinates of micro base stations in each sub-area and compare them with the small-scale macro base station sample space Ω 1 Merge to get the complete sample space Ω 2 ; Select sample module, used to evaluate the complete sample space Ω through the energy consumption evaluation model 2 Screening to obtain the rough sample set Ω 3 , for the coarsely selected sample set Ω 3 Sort the samples in to form a carefully selected sample set Ω 4 ; Simulation module, used to use network simulation software to select sample set Ω 4 Perform network simulation and output the optimal network deployment solution U for the area to be deployed * .

[0118] The embodiment of the present invention can fully and accurately grasp the terrain and obstacle distribution of the area to be deployed by obtaining a 3D map of the area to be deployed and constructing the three-dimensional spatial coordinates of the area. On this basis, the area is divided into several sub-areas, and a macro base station is set up in the center of each sub-area, which greatly simplifies the deployment process and improves the deployment efficiency. By calculating the sub-area coverage rate and combining the obstacle coordinates, the macro base station sample space is screened to ensure that each sub-area can obtain good network coverage. At the same time, by reducing the macro base station sample space and deploying equal-power micro base stations, the coverage range and signal strength of the network are further enhanced, and the overall performance of the network is improved. Then, the complete sample space is screened through the energy consumption evaluation model, and the sample space is reduced as much as possible through sequential optimization, retaining high-quality feasible solutions, and obtaining the optimal feasible solution according to the simulation results of the network planning software. This method first solves the analytical difficulty of the traditional optimization solution method when facing large-scale non-convex problems, and secondly solves the problem of redundant samples and excessive overhead in large-scale network simulation, while ensuring the optimality of the final network deployment solution.

[0119] In one embodiment of the present invention, see Figure 3 , a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute 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 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the 5G heterogeneous network deployment method in large-scale complex scenarios.

[0120] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here may include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the large-scale complex scenario 5G heterogeneous network deployment method in the above-mentioned embodiment.

[0121] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A large-scale complex scenario 5G heterogeneous network deployment method, characterized in that: The following steps are involved: 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; Divide the area to be deployed into several sub-areas of equal area, set up a macro base station in the center of each sub-area, and generate a macro base station parameter combination Based on the macro base station parameter combination Calculate the sub-area coverage, combine it with the obstacle coordinates, and filter out the macro base station sample space Ω 0 ; The macro base station sample space Ω 0 The small-scale macro base station sample space Ω is reduced 1 , based on the small-scale macro base station sample space Ω 1 Deploy micro base stations of equal power at different densities in each sub-area; Collect the number and spatial coordinates of micro base stations in each sub-area and compare them with the small-scale macro base station sample space Ω 1 Merge to get the complete sample space Ω 2 ; The energy consumption evaluation model is used to evaluate the complete sample space Ω 2 Screening to obtain the rough sample set Ω 3 , for the coarsely selected sample set Ω 3 Sort the samples in to form a carefully selected sample set Ω 4 ; Use network simulation software to select sample set Ω 4 Perform network simulation and output the optimal network deployment solution U for the area to be deployed * .

2. According to a large-scale complex scenario 5G heterogeneous network deployment method according to claim 1, it is characterized in that: The area to be deployed is divided into several sub-areas of equal area, a macro base station is set up in the center of each sub-area, and a macro base station parameter combination is generated. The steps include: Based on the 3D map of the area to be deployed, a 2D area plan is formed and gridded to divide the entire area into C sub-areas of equal size. Each sub-area is divided into squares with a side length of L. Label each grid and use C(w, d) to distinguish them, 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 at the center of each sub-region; Collect the number of all deployed macro base stations N M , combined with the 2D map and coordinates, collect the coordinates (X M ,Y M ); According to the upper limit of the transmission power of each macro base station radio equipment, configure the corresponding transmission power P for each macro base station. M ; Combine the macro base station coordinate space and the macro base station transmit power space to obtain a complete macro base station parameter combination U M =[X M ,Y M ,N M ,P M ], the number of combinations is α; rewrite the macro base station parameter combination as 3. According to the large-scale complex scenario 5G heterogeneous network deployment method of claim 1, it is characterized in that: The macro base station parameter combination Calculate the sub-area coverage, combine it with the obstacle coordinates, and filter out the macro base station sample space Ω 0 The steps include: Calculate the sub-area coverage under each parameter combination respectively, screen the macro base station parameter combinations, and eliminate the parameter combinations that do not meet the coverage requirements; the specific calculation formula is: P=Prob{SINR d′ >θ} Where d' is the distance between the current downlink receiving device and the macro base station in the sub-area center; SINR represents the signal-to-interference-to-noise ratio; P is the sub-area coverage rate; Compare the coordinates of each macro base station in the macro base station parameter combination with the coordinates of the obstacle. If there is any overlap in position, remove it from the macro base station sample space; After two rounds of screening and elimination, we get the macro base station sample space Ω 0 , the space size is β, where β<α, and the macro base station sample space is rewritten as 4. According to the large-scale complex scenario 5G heterogeneous network deployment method of claim 1, it is characterized in that: The macro base station sample space Ω 0 The small-scale macro base station sample space Ω is reduced 1 , based on the small-scale macro base station sample space Ω 1 The steps of deploying equal-power micro base stations of different densities in each sub-area specifically include: Through a simple random sampling method, in the macro base station sample space Ω 0 Randomly select t% of the samples from the sample space to obtain the small-scale macro base station sample space Ω 1 , the sample space size is [a,b], where a≥0, b≤β, and the sample space of small-scale macro base stations is written as in Extract small-scale macro base station sample space Ω 1 For different macro base station samples in , in each set of macro base station parameter combinations, different distribution density coefficients are used to deploy micro base stations for each sub-area, where the distribution of micro base stations follows the PPP distribution. The expression of the PPP distribution model is: In the formula, Q c is the area to be deployed, ρ c is the distribution density coefficient, N(Q c ) = n is the number of random deployment points, and each point in the area conforms to the Poisson distribution; Finally, for each set of macro base station parameter combinations, there are j I The micro base station parameter combination of the group corresponds to it, where I is Ω 1 The macro base station sample number in , I∈[1,ba].

5. According to a large-scale complex scenario 5G heterogeneous network deployment method according to claim 1, it is characterized in that: The number and spatial coordinates of the micro base stations in each sub-area are collected, and the small-scale macro base station sample space Ω 1 The steps of merging to obtain a complete sample space include: Determine the coordinates of the micro base station in each sub-area and save the number of micro base stations in each sub-area to obtain a complete combination of micro base station parameters The number of combinations is The subscript m indicates the micro base station, and the superscript C indicates the sub-region number; represents the vector containing the coordinates of all micro base stations in each sub-area, represents a vector containing the total number of micro base stations in each sub-area; The micro base station parameter combination is combined with the corresponding macro base station parameter combination to form a complete sample space Ω 2 .

6. According to a large-scale complex scenario 5G heterogeneous network deployment method according to claim 1, it is characterized in that: The energy consumption evaluation model is used to evaluate the complete sample space Ω 2 Screening to obtain the rough sample set Ω 3 , for the coarsely selected sample set Ω 3 Sort the samples in to form a carefully selected sample set Ω 4 The steps include: An energy consumption evaluation model is established; the evaluation function of the energy consumption evaluation model is: EE=R / P In the formula, EE represents energy efficiency; R represents the total rate of the region, P represents the total energy consumption of the region, B represents the network bandwidth, and P u represents the downlink base station transmission power connected to user u. The downlink user allocation search is based on the minimum distance principle. c,u represents the downlink transmission distance, γ represents the path loss factor, and h c,u represents the path loss term, T represents the regional co-channel interference, σ 2 represents noise interference, U represents the number of users in each area, u represents the user number, u∈[1,U], c represents the area number, c∈[1,C]; The constraints of the energy consumption assessment model are: 0<P M <P M,Max Where P M,Max Indicates the upper limit of RF transmission power. represents the predicted total regional rate demand, S cov represents the total coverage area of ​​the region, represents the coverage provided by the macro base station in area c, represents the coverage provided by the micro base station in area c, represents the minimum coverage area constraint of the region, Indicates the overlapping coverage area of ​​macro and micro base stations in a heterogeneous network; Through the constraints of the energy consumption evaluation model, the complete sample space Ω 2 Screening to obtain the rough sample set Ω 3 ; Through the evaluation function of the energy consumption evaluation model, the rough selection sample set Ω 3 Each sample in the EE is evaluated, and the samples are sorted according to the energy consumption EE value, and the top 5% are selected to form a finely selected sample set Ω 4 .

7. According to the large-scale complex scenario 5G heterogeneous network deployment method of claim 1, it is characterized by: The network simulation software is used to select the sample set Ω 4 Perform network simulation and output the optimal network deployment solution U for the area to be deployed * The steps include: Using network simulation software, we can select a sample set Ω 4 Simulate each sample in the simulation, and obtain the optimal 5G heterogeneous network deployment solution suitable for the current large-scale and complex areas according to the simulation results, and output the optimal solution parameter combination The superscript asterisk (*) indicates the optimal parameter. Represented as a vector containing the coordinates of all macro base stations in the entire area, represents the vector containing the coordinates of all micro base stations in each sub-area, It is represented as a vector containing the transmit power of all macro base stations in the entire area, Represents a vector containing the total number of micro base stations in each sub-area.

8. A large-scale complex scenario 5G heterogeneous network deployment system, characterized by: include: The spatial coordinate construction module is used to obtain the 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 establishment module is used to divide the area to be deployed into several sub-areas of equal area, establish a macro base station in the center of each sub-area, and generate a macro base station parameter combination The macro base station sample space module is used to combine Calculate the sub-area coverage, combine it with the obstacle coordinates, and filter out the macro base station sample space Ω 0 ; The micro base station deployment module is used to convert the macro base station sample space Ω 0 The small-scale macro base station sample space Ω is reduced 1 , based on the small-scale macro base station sample space Ω 1 Deploy micro base stations of equal power at different densities in each sub-area; The micro base station sample space module is used to collect the number and spatial coordinates of micro base stations in each sub-area and the small-scale macro base station sample space Ω 1 Merge to get the complete sample space Ω 2 ; The sample selection module is used to evaluate the complete sample space Ω through the energy consumption evaluation model. 2 Screening to obtain the rough sample set Ω 3 , for the coarsely selected sample set Ω 3 Sort the samples in to form a carefully selected sample set Ω 4 ; The simulation module is used to use network simulation software to select sample sets Ω 4 Perform network simulation and output the optimal network deployment solution 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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