Ray tracing-based method for indoor multi-base station location optimization in millimeter wave frequency band

Abstract

The present invention provides a ray tracing-based method for indoor multi-base station location optimization in a millimeter wave frequency band, comprising: constructing an indoor millimeter wave network model; determining an indoor millimeter wave network model optimization constraint condition; constructing a cost function of multi-base station location deployment on the basis of the constraint condition; determining the initial location of each base station in the indoor millimeter wave network model; and by taking the initial location of each base station as a starting point, determining an optimal location of each base station by means of axial search together with pattern search. According to the present invention, while maintaining low complexity and the advantages of a global optimization algorithm, an indoor multi-base station optimization problem can be solved under the constraint condition that base station locations are necessarily within a feasible interval, and by taking the path loss and signal-to-noise ratio as optimization parameters, a good balance can be achieved between the accuracy of optimization results and the complexity of optimization algorithms for the multi-base station deployment optimization problem, so that the optimized base station locations can provide high-quality signal coverage for an indoor millimeter wave network.

Description

A ray tracing-based method for optimizing indoor multi-base station locations in the millimeter wave band Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and in particular to a method for optimizing the positions of multiple indoor base stations in a millimeter wave frequency band based on ray tracing. Background Art

[0002] With the current surge in the number of smart devices and smart applications, 5G mobile communication systems will struggle to accommodate this massive influx of mobile devices. 6G technology will be a hot topic for future research and development. Furthermore, due to user behavior, most mobile communication services are currently concentrated indoors. Millimeter wave technology, one of the core technologies of 6G, offers advantages and disadvantages such as high speed, large bandwidth, high propagation loss, and weak penetration. Due to these limitations, millimeter wave technology is primarily used in short-range communication scenarios, such as indoors. However, millimeter wave propagation is highly sensitive to obstacles along its path, resulting in significant impacts on network signal quality and coverage due to varying base station locations. Therefore, optimizing indoor multi-base station deployments in the millimeter wave band places higher demands on accuracy.

[0003] Currently, there are two main research approaches for optimizing indoor base station deployment. One involves adjusting parameters such as base station location and antenna angle, then comparing and analyzing signal changes to find a relatively optimal base station deployment solution. This approach is less complex, but only provides a feasible base station deployment solution, not a global optimal solution. The other approach involves building a mathematical model, recasting the base station deployment optimization problem as a mathematical optimization problem, and applying different optimization methods to find the optimal solution. This second approach can provide more accurate and reliable base station deployment solutions than the first. Building on the second approach, some researchers have used ray tracing to further improve the accuracy of base station deployment optimization by obtaining the required wireless channel parameters. However, due to the high complexity of ray tracing, the optimization algorithm often uses simple line search algorithms such as the steepest descent method, which can lead to local optima. This means that the optimal solution found may be a local optimum. Other researchers have used highly complex machine learning algorithms such as genetic algorithms to find the optimal base station location. This can avoid local optima and find the global optimal solution. However, due to the high complexity of the optimization algorithm, the wireless channel parameters are often derived from empirical models. In summary, existing research methods find it difficult to achieve a good trade-off between the complexity of the optimization algorithm and the accuracy of the optimization results.

[0004] Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for optimizing the positions of multiple indoor base stations in the millimeter wave frequency band based on ray tracing.

[0006] In a first aspect, the present invention provides a method for optimizing the positions of multiple indoor base stations in the millimeter wave frequency band based on ray tracing, comprising:

[0007] Build an indoor millimeter wave network model;

[0008] Determine optimization constraints for indoor millimeter wave network models;

[0009] Construct a cost function for multi-base station location deployment based on constraints;

[0010] Determine the initial location of each base station in the indoor millimeter wave network model;

[0011] Taking the initial position of each base station as the starting point, the optimal position of each base station is determined by using the axial search combined with the pattern search method.

[0012] Furthermore, the construction of the indoor millimeter wave network model includes:

[0013] The intersection of the length and width of the indoor space is taken as the origin, the length direction is the X-axis direction, the width direction is the Y-axis direction, and the Z-axis direction perpendicular to the X-axis and Y-axis directions is used to construct a global coordinate system, and the indoor hyperrectangle Q is obtained, Q = {(x, y, h T )∈R 3 |0≤x≤a,0≤y≤b}, a is the length of the indoor space, b is the width of the indoor space, x is the length of the hyperrectangle Q, y is the width of the hyperrectangle Q, h T is the height of the indoor base station, R 3 is a three-dimensional real space.

[0014] Furthermore, determining the optimization constraint conditions of the indoor millimeter wave network model includes:

[0015] The signal power P received by the receiving point i from the base station j is calculated according to the following formula ij : P ij =P T -P L,ij ;

[0016] Where i = 1, 2, 3, ..., m; m is the total number of receiving points in the indoor space; j = 1, 2, 3, ..., n; n is the total number of base stations in the indoor space; P T is the transmission power of the base station; P L,ij is the path loss between receiving point i and base station j;

[0017] The thermal noise P in the indoor millimeter wave network model is calculated according to the following formula noise : P noise =kVB;

[0018] Where k is the Boltzmann constant; V is the room temperature in Kelvin; B is the signal bandwidth;

[0019] The signal-to-noise ratio γ at the receiving point i is calculated according to the following formula i :

[0020] Among them, P i is the signal power received at receiving point i; when receiving point i is connected to base station q, the signal power P received by receiving point i from base station q iq With P i Equal, the interference power is

[0021] The path loss P at the receiving point i is calculated according to the following formula L,i :

[0022] The construction constraints are:

[0023] Among them, P L,th is the preset path loss threshold; γ th It is the preset signal-to-dryness ratio threshold.

[0024] Furthermore, constructing a cost function for multi-base station location deployment based on the constraint conditions includes:

[0025] Construct the cost function F expression:

[0026] Among them, f1 is the first objective function; f2 is the second objective function; f3 is the third objective function; is the optimization priority of the first objective function; is the optimization priority of the second objective function; The optimization priority of the third objective function is:

[0027] Among them, ω i is the weight of receiving point i; ω i The size of P represents the level of network signal quality requirement of receiving point i; i = 1, 2, 3, ..., m; m is the total number of receiving points in the indoor space; P L,i is the path loss at receiving point i; P L,th is the preset path loss threshold; γ th is the preset signal-to-dryness ratio threshold; μ i is the penalty factor at receiving point i, μ i The size of P L,i and / or γ i The severity of the result caused by not meeting the threshold; n is the total number of base stations in the indoor space; γi is the signal-to-noise ratio at receiving point i.

[0028] Furthermore, determining the initial position of each base station in the indoor millimeter wave network model includes:

[0029] Step 401: Determine the weight sum of each hyper-rectangle in the current indoor space; the weight sum of each hyper-rectangle is the sum of the weights of all receiving points in the corresponding hyper-rectangle;

[0030] Step 402: traverse the weight sums of all hyperrectangles and obtain the hyperrectangle Q with the largest weight sum j ;

[0031] Step 403, calculate the hyperrectangle Q according to the following formula j The center of gravity coordinates of

[0032] Among them, h T is the height of the indoor base station; ω i1 is a hyperrectangle Q j The weight of the receiving point i1; x i1 is a hyperrectangle Q j The horizontal coordinate of the receiving point i1; y i1 is a hyperrectangle Q j The vertical coordinate of the receiving point i1;

[0033] Step 404: In the super matrix Q j At the center of gravity, along the supermatrix Q j The width direction of the super matrix Q j Split into two new hyperrectangles;

[0034] Step 405 , repeating steps 401 - 404 until n barycentric coordinates are obtained, and using the n barycentric coordinates as the initial positions of n base stations respectively; n is the total number of base stations in the indoor space.

[0035] Furthermore, the method of determining the optimal position of each base station by using an axial search combined with a pattern search method with the initial position of each base station as a starting point includes:

[0036] Step 501: Construct a set A of horizontal and vertical coordinates of the optimized position of the base station. λ :

[0037] Among them, λ is the number of base station location optimizations; The horizontal coordinate of the position of base station n after optimization for λ times; is the vertical coordinate of the position of base station n after optimization for λ times; n is the total number of base stations in the indoor space;

[0038] Step 502: Construct the starting position set B of the axial search base station 1 :

[0039] Step 503, to B 1 Along B 1 Move in the 2n dimensional directions with the target step size, and obtain the set B of the horizontal and vertical coordinates of the base station position corresponding to the minimum cost function value during the movement 2n+1 ;

[0040] Step 504, determine F(B 2n+1 )<F(A l ) is established; among them, F(B 2n+1 ) is the horizontal and vertical coordinates of the base station location set B 2n+1 The cost function value when F(A l ) is the horizontal and vertical coordinates of the base station location set A l The cost function value when ;

[0041] Step 505: If it is less than, make A l+1 =B 2n+1 , the descent direction vector of the cost function D = A l+1 -A l ; The next optimization of B 1 Updated to A l+1 +αD; where l+1≤λ; α is the acceleration factor used to accelerate the convergence of axial search and pattern search;

[0042] Step 506, if it is not less than; then make δ l+1 =βδ l , A l+1 =A l , the next optimization B 1 Updated to A l ; Among them, δ l is the step size of the lth base station position optimization; β is the fading factor;

[0043] Step 507: When performing l+1 base station location optimization, determine δ l+1 Is it greater than the preset allowable error ε?

[0044] Step 508: If it is greater than, repeat steps 501-507;

[0045] Step 508: If not, then A l+1 This serves as the final set of horizontal and vertical coordinates of the base station position, and ends the base station position optimization.

[0046] In a second aspect, the present invention provides a computer device comprising a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the steps of the millimeter wave frequency band indoor multi-base station position optimization method based on ray tracing described in the first aspect are implemented.

[0047] In a third aspect, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the millimeter wave frequency band indoor multi-base station position optimization method based on ray tracing described in the first aspect are implemented.

[0048] The present invention provides a method for optimizing the positions of multiple indoor base stations in the millimeter wave frequency band based on ray tracing, comprising constructing an indoor millimeter wave network model; determining optimization constraints for the indoor millimeter wave network model; constructing a cost function for the deployment of multiple base stations based on the constraints; determining the initial position of each base station in the indoor millimeter wave network model; and determining the optimal position of each base station using an axial search combined with a pattern search method, taking the initial position of each base station as a starting point.

[0049] The computational optimization initial solution method used in the present invention can accelerate the optimization algorithm and reduce the solution time of base station deployment optimization. Compared with the traditional pattern search algorithm, while retaining the advantages of lower complexity and global optimization algorithm, it can solve the indoor multi-base station optimization problem under the constraint that the base station location must be within the feasible interval, using path loss and signal-to-noise ratio as optimization parameters to achieve high quality and full coverage of the indoor millimeter wave network. The present invention can achieve a good balance between the accuracy of the optimization results of the multi-base station deployment optimization problem and the complexity of the optimization algorithm, so that the optimized base station location can provide high-quality signal coverage for the indoor millimeter wave network. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] FIG1 is a flow chart of a method for optimizing the positions of multiple indoor base stations in the millimeter wave frequency band based on ray tracing according to an embodiment of the present invention;

[0052] FIG2 is an application scenario diagram provided by an embodiment of the present invention;

[0053] FIG3 is a schematic diagram of receiving point weight division according to an embodiment of the present invention;

[0054] FIG4 is a schematic diagram of a determined initial base station position according to an embodiment of the present invention;

[0055] FIG5 is a graph showing a convergence curve of a cost function according to an embodiment of the present invention;

[0056] FIG6 is a received power diagram obtained from random base station positions according to an embodiment of the present invention;

[0057] FIG7 is a received power diagram obtained by using the initial solution calculated according to an embodiment of the present invention as the initial base station position before optimization;

[0058] FIG8 is a diagram of the optimized received power provided by an embodiment of the present invention;

[0059] FIG9 is an SINR diagram obtained at random base station positions according to an embodiment of the present invention;

[0060] FIG10 is an SINR diagram obtained by using the calculated initial solution as the initial base station position before optimization according to an embodiment of the present invention;

[0061] FIG11 is a diagram of optimized SINR provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0063] In one embodiment, as shown in FIG1 , the present invention provides a method for optimizing the positions of multiple indoor base stations in the millimeter wave frequency band based on ray tracing, including:

[0064] Step 1: Build an indoor millimeter wave network model.

[0065] As shown in Figure 2, there is an indoor space with a length a of 30m, a width b of 20m, and a height of 3m. Several cubes represent tables 1, triangles represent base stations 2, and partitions 3 divide the space into several areas. There are m receiving points and n base stations in the indoor millimeter wave network model. With the lower left vertex of the optimized scene as the origin, a global coordinate system is established with the X-axis, Y-axis, and Z-axis parallel to the length, width, and height of the optimized scene. The receiving point coordinates are expressed as (x i ,y i ,z i ), i=1,2,3,...,m; the base station coordinates are expressed as (x j ,y j ,z j ), j=1,2,3,...,n.

[0066] The hyperrectangle of the optimization area is Q, Q={(x,y,h T )∈R 3 |0≤x≤a,0≤y≤b}, a is the length of the indoor space, b is the width of the indoor space, x is the length of the hyperrectangle Q, y is the width of the hyperrectangle Q, h T is the height of the indoor base station, R 3 is a three-dimensional real space. For example, two base stations are placed on the ceiling of an indoor room with a Kelvin temperature V of 290K, with a height h T is 3m, the base station's transmission power P T Set to 0dBm, carrier frequency f is 28GHz, and signal bandwidth B is 100MHz. As shown in Figure 3, 2400 receiving points are evenly set at a density of 0.5m indoors, and the height of the receiving points h is 0. R is 1.5m.

[0067] Step 2: Determine the optimization constraints of the indoor millimeter wave network model.

[0068] Exemplarily, this step includes calculating the signal power P received by the receiving point i from the base station j according to the following formula: ij : P ij =P T -P L,ij .

[0069] Among them, P L,ij is the path loss between receiving point i and base station j.

[0070] The thermal noise P in the indoor millimeter wave network model is calculated according to the following formula noise : P noise =kVB.

[0071] Where k is the Boltzmann constant, k = 1.380658 × 10 -23 J / K.

[0072] Define the receiving point to be connected to the base station with the minimum path loss to it, and the set of receiving points connected to base station j is expressed as S j , the set of receiving points connected to base station j1 ​​is represented as S j1 , the set of receiving points connected to base station j2 is represented as S j2 , and needs to meet the following requirements:

[0073] The signal-to-noise ratio γ at the receiving point i is calculated according to the following formula i :

[0074] Among them, P iis the signal power received at receiving point i; when receiving point i is connected to base station q, the signal power P received by receiving point i from base station q iq With P i Equal, the interference power is

[0075] The path loss P at the receiving point i is calculated according to the following formula L,i :

[0076] The construction constraints are:

[0077] Among them, P L,th is the preset path loss threshold; γ th is the preset signal-to-dryness ratio threshold. In this embodiment, P L,th Set to 70dB, γ th Set to 7dB. L,i Less than P L,th When , the receiving point i is covered by the signal; γ i Greater than γ th When , the signal quality at receiving point i is good.

[0078] Step 3: Construct a cost function for multi-base station location deployment based on the constraints. Exemplarily, this step includes:

[0079] Construct the cost function F expression:

[0080] Among them, f1 is the first objective function; f2 is the second objective function; f3 is the third objective function; In this embodiment, is the optimization priority of the first objective function; is the optimization priority of the second objective function; The optimization priority of the third objective function is:

[0081] Among them, ω i is the weight of receiving point i; ω i The size of represents the level of network signal quality requirement of receiving point i; as shown in Figure 3, the larger circle represents a high-weight receiving point with a weight of 1; the smaller circle represents a low-weight receiving point with a weight of 0.2. i=1,2,3,...,m; m is the total number of receiving points in the indoor space; P L,i is the path loss at receiving point i; P L,th is the preset path loss threshold; γ th is the preset signal-to-dryness ratio threshold; μ i is the penalty factor at receiving point i, μ i The size of PL,i and / or γ i The severity of the result of not meeting the threshold; the penalty factor of the receiving point is set to be consistent with its weight.

[0082] The cost function value represents the network coverage and quality level. The smaller the value, the better the network coverage and quality level. Therefore, the base station location optimization process is transformed into a process of finding the minimum value of the cost function under the constraints.

[0083] Step 4: Determine the initial position of each base station in the indoor millimeter wave network model. Exemplarily, this step includes:

[0084] Step 401 : Determine the weight sum of each hyper-rectangle in the current indoor space; the weight sum of each hyper-rectangle is the sum of the weights of all receiving points in the corresponding hyper-rectangle.

[0085] Step 402: traverse the weight sums of all hyperrectangles and obtain the hyperrectangle Q with the largest weight sum j .

[0086] Step 403, calculate the hyperrectangle Q according to the following formula j The center of gravity coordinates of

[0087] Among them, h T is the height of the indoor base station; ω i1 is a hyperrectangle Q j The weight of the receiving point i1; x i1 is a hyperrectangle Q j The horizontal coordinate of the receiving point i1; y i1 is a hyperrectangle Q j The vertical coordinate of the receiving point i1.

[0088] Step 404: In the super matrix Q j At the center of gravity, along the supermatrix Q j The width direction of the super matrix Q j It is divided into two new super-rectangles, as shown in Figure 4, where the triangle represents the initial position of the base station and the vertical line divides the optimization area into two super-rectangles.

[0089] Step 405 , repeating steps 401 - 404 until n barycentric coordinates are obtained, and using the n barycentric coordinates as the initial positions of n base stations respectively; n is the total number of base stations in the indoor space.

[0090] Step 5: Starting from the initial position of each base station, an axial search combined with a pattern search method is used to determine the optimal position of each base station. Exemplarily, this step includes:

[0091] Step 501: Construct a set A of horizontal and vertical coordinates of the optimized position of the base station. λ :

[0092] Among them, λ is the number of base station location optimizations; The horizontal coordinate of the position of base station n after optimization for λ times; is the vertical coordinate of the position of base station n after optimization for λ times; n is the total number of base stations in the indoor space.

[0093] Because h T is a constant. During the optimization process, the base station position must satisfy 0<x j <a, 0 <y j <b. Construct the constraint matrix H based on this constraint condition:

[0094] Step 502: Construct the starting position set B of the axial search base station 1 :

[0095] Step 503, before the axial search combined with the pattern search method starts to execute, set B 1 =A 1 , first search axially. 1 Along B 1 Move in the 2n dimensional directions with the target step size, and obtain the set B of the horizontal and vertical coordinates of the base station position corresponding to the minimum cost function value during the movement 2n+1 .

[0096] Step 504: When searching for a pattern, determine whether F(B 2n+1 )<F(A l ) is established; among them, F(B 2n+1 ) is the horizontal and vertical coordinates of the base station location set B 2n+1 The cost function value when F(A l ) is the horizontal and vertical coordinates of the base station location set A l The cost function value at .

[0097] Step 505: If it is less than, make A l+1 =B 2n+1 , the descent direction vector of the cost function D = A l+1 -A l ; The next optimization of B 1 Updated to A l+1 +αD; where l+1≤λ; α is the acceleration factor used to accelerate the convergence of axial search and pattern search;

[0098] Step 506, if it is not less than; then make δ l+1 =βδl , A l+1 =A l , the next optimization B 1 Updated to A l ; Among them, δ l is the step size of the first base station position optimization; β is the fading factor, which is 0.5 in this embodiment.

[0099] Step 507: When performing l+1 base station location optimization, determine δ l+1 Is it greater than the preset allowable error ε? In this embodiment, ε is 0.5.

[0100] Step 508: If it is greater than, repeat steps 501-507.

[0101] Step 508: If not, then A l+1 This serves as the final set of horizontal and vertical coordinates of the base station position, and ends the base station position optimization.

[0102] As shown in Figure 5, the present invention can significantly shorten the convergence time of the optimization algorithm. By using the calculated optimized initial solution as the initial base station position, the present invention effectively reduces the number of iterations compared to traditional optimization methods that use random base stations as the initial base station position.

[0103] As shown in Figures 6, 7 and 8, the receiving power diagrams for three cases, random sites, before optimization and after optimization, are shown. As shown in Figures 9, 10 and 11, the SINR (signal-to-noise ratio) diagrams for three cases, random sites, before optimization and after optimization, are shown. It should be noted that before optimization, the calculated optimized initial solution is used as the initial position of the base station. The darkest block in the figure represents the partition, the white area represents that there is no signal here, and the high-weight receiving point is surrounded by a hollow circle. Analysis shows that the axial search based on ray tracing combined with the pattern search method in the present invention can effectively optimize the multi-base station deployment problem, and the obtained optimal base station position can effectively improve the path loss and SINR in the millimeter wave network to achieve high-quality signal coverage in the millimeter wave network.

[0104] In another embodiment, the present invention provides a computer device comprising a processor and a memory; wherein, when the processor executes a computer program stored in the memory, the steps of the above-mentioned millimeter wave frequency band indoor multi-base station position optimization method based on ray tracing are implemented.

[0105] For more specific details about the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be described again here.

[0106] In another embodiment, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the above-mentioned millimeter wave frequency band indoor multi-base station position optimization method based on ray tracing are implemented.

[0107] For more specific details about the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be described again here.

[0108] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar portions of the various embodiments will be sufficient. The devices and storage media disclosed in the embodiments are described briefly because they correspond to the methods disclosed in the embodiments. For relevant details, refer to the method description.

[0109] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention or certain portions of the embodiments.

[0110] The present invention has been described in detail above with reference to specific embodiments and exemplary examples. However, these descriptions should not be construed as limiting the present invention. Those skilled in the art will appreciate that various equivalent substitutions, modifications, or improvements may be made to the technical solutions and implementations of the present invention without departing from the spirit and scope of the present invention, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for optimizing the positions of multiple indoor base stations in the millimeter wave frequency band based on ray tracing, characterized in that: include: Build an indoor mmWave network model; Determine optimization constraints for indoor mmWave network models; Construct a cost function for multiple base station location deployment based on constraints; Determine the initial position of each base station in the indoor millimeter wave network model; Taking the initial position of each base station as the starting point, the optimal position of each base station is determined by using the axial search combined with the pattern search method.

2. The method for optimizing indoor multi-base station locations in the millimeter wave frequency band according to claim 1, characterized in that: The building of the indoor millimeter wave network model includes: The intersection of the length and width of the indoor space is taken as the origin, the length direction is the X-axis direction, the width direction is the Y-axis direction, and the vertical direction is the Z-axis direction to construct a global coordinate system, and the indoor hyperrectangle Q is obtained, Q = {(x, y, h T )∈R 3 |0≤x≤a,0≤y≤b}, a is the length of the indoor space, b is the width of the indoor space, x is the length of the hyperrectangle Q, y is the width of the hyperrectangle Q, h T is the height of the indoor base station, R 3 is a three-dimensional real number space.

3. The method for optimizing indoor multi-base station locations in the millimeter wave frequency band according to claim 1, characterized in that: The determining of optimization constraints of the indoor millimeter wave network model includes: The signal power P received by the receiving point i from the base station j is calculated according to the following formula ij : P ij =P T -P L,ij ; Where i = 1, 2, 3, ..., m; m is the total number of receiving points in the indoor space; j = 1, 2, 3, ..., n; n is the total number of base stations in the indoor space; P T is the transmission power of the base station; P L,ij is the path loss between receiving point i and base station j; The thermal noise P in the indoor millimeter wave network model is calculated according to the following formula noise : P noise =kVB; Where k is the Boltzmann constant; V is the room Kelvin temperature; B is the signal bandwidth; The signal-to-noise ratio γ at the receiving point i is calculated according to the following formula i : Among them, P i is the signal power received at receiving point i; when receiving point i is connected to base station q, the signal power P received by receiving point i from base station q iq With P i equal, the interference power is The path loss P at the receiving point i is calculated according to the following formula L,i : The construction constraints are: Among them, P L,th is the preset path loss threshold; γ th It is the preset signal-to-dryness ratio threshold.

4. The method for optimizing indoor multi-base station locations in the millimeter wave frequency band according to claim 1, characterized in that: The cost function of multi-base station location deployment is constructed according to the constraint conditions, including: Construct the cost function F expression: Among them, f1 is the first objective function; f2 is the second objective function; f3 is the third objective function; is the optimization priority of the first objective function; is the optimization priority of the second objective function; The optimization priority of the third objective function is: Among them, ω i is the weight of receiving point i; ω i The size of represents the quality requirement of the network signal at the receiving point i; i = 1, 2, 3, ..., m; m is the total number of receiving points in the indoor space; P L,i is the path loss at receiving point i; P L,th is the preset path loss threshold; γ th is the preset signal-to-dryness ratio threshold; μ i is the penalty factor at receiving point i, μ i The size of P L,i and / or γ i The severity of the result caused by not meeting the threshold; n is the total number of base stations in the indoor space; γ i is the signal-to-noise ratio at receiving point i.

5. The method for optimizing indoor multi-base station locations in the millimeter wave frequency band according to claim 1, characterized in that: The determining of the initial position of each base station in the indoor millimeter wave network model includes: Step 401, determining the weight sum of each super rectangle in the current indoor space; the weight sum of each super rectangle is the sum of the weights of all receiving points in the corresponding super rectangle; Step 402, traverse the weight sums of all hyper-rectangles and obtain the hyper-rectangle Q with the largest weight sum j ; Step 403, calculate the hyperrectangle Q according to the following formula j The centroid coordinates of Among them, h T is the height of the indoor base station; ω i1 is a hyperrectangular Q j The weight of receiving point i1 in x i1 is a hyperrectangular Q j The horizontal coordinate of the receiving point i1; y i1 is a hyperrectangular Q j The ordinate of the receiving point i1; Step 404, in the super matrix Q j At the center of gravity, along the supermatrix Q j The width direction of the super matrix Q j Split into two new hyperrectangles; Step 405, repeating steps 401-404 until n barycentric coordinates are obtained, and the n barycentric coordinates are respectively used as the initial positions of n base stations; n is the total number of base stations in the indoor space.

6. The method for optimizing indoor multi-base station locations in the millimeter wave frequency band according to claim 1, characterized in that: The method of taking the initial position of each base station as the starting point and using the axial search combined with the pattern search method to determine the optimal position of each base station includes: Step 501: construct a set A of horizontal and vertical coordinates of the optimized position of the base station λ : Among them, λ is the number of base station location optimizations; is the horizontal coordinate of the position of base station n after optimization for λ times; is the vertical coordinate of the position of the base station after optimization for λ times; n is the total number of base stations in the indoor space; Step 502: Construct a base station starting position set B for axial search 1 : Step 503, to B 1 Along B 1 Move in the 2n dimensional directions with the target step size, and obtain the set B of the horizontal and vertical coordinates of the base station position corresponding to the minimum cost function value during the movement 2n+1 ; Step 504, determine F(B 2n+1 )<F(A l ) is true; among them, F(B 2n+1 ) is the set of base station location horizontal and vertical coordinates B 2n+1 The cost function value when F(A l ) is the set of base station location horizontal and vertical coordinates A l The cost function value when ; Step 505, if it is less than, make A l+1 =B 2n+1 , the descending direction vector of the cost function D = A l+1 -A l ; The next optimization of B 1 Update to A l+1 +αD; where l+1≤λ; α is the acceleration factor, which is used to accelerate the convergence of axial search and pattern search; Step 506, if not less than; then make δ l+1 =βδ l , A l+1 =A l , the next optimization B 1 Update to A l ; Among them, δ l is the step size of the lth base station location optimization; β is the fading factor; Step 507: When performing l+1 base station location optimization, determine δ l+1 Is it greater than the preset allowable error ε? Step 508: If it is greater than, repeat steps 501-507; Step 508: If not, then A l+1 This serves as the final set of horizontal and vertical coordinates of the base station location, and ends the base station location optimization.

7. A computer device, characterized in that: It includes a processor and a memory; wherein, when the processor executes the computer program stored in the memory, it implements the steps of the millimeter wave frequency band indoor multi-base station location optimization method based on ray tracing as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: Used to store computer programs; when the computer programs are executed by the processor, the steps of the method for optimizing the positions of multiple indoor base stations in the millimeter wave frequency band based on ray tracing are implemented as described in any one of claims 1 to 6.

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