A method, apparatus and equipment for beam adjustment in a cell

Abstract

This invention discloses a method, apparatus, and device for beam adjustment in a cell. The method includes: reducing the order of beam weight combination schemes of at least one cell in a target area during a merging period to obtain particles corresponding to beam weight combination schemes, each particle including a set of beam weight schemes, wherein the beam weights are adjustment factors for adjusting the beam direction based on the terminal position within the cell; obtaining the cell coverage rate corresponding to each particle; determining a target particle based on the cell coverage rate corresponding to each particle; and performing joint beam direction adjustment on at least one cell in the target area based on the target particle. Through this method, this invention achieves joint optimization adjustment of the beam direction of multiple cells within a target area, improving the optimization effect.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and specifically to a method, apparatus, and device for adjusting the beam of a cell. Background Technology

[0002] Currently, Massive MIMO (Massive Multiple Input Multiple Output) weight optimization algorithms support adaptively finding user hotspots based on tidal distribution to generate corresponding beam weights. Since the movement patterns of user hotspots often exhibit regional and clustering characteristics, weight adjustments are typically performed across multiple cells within an area where multiple users exhibit similar behavioral patterns.

[0003] The current methods for adjusting multiple regional communities mainly have the following problems:

[0004] 1. The method of adjusting each cell in the area at the same time only considers the optimal coverage of the cell itself. Since there will be co-channel interference between cells with the same coverage that are close to each other, the optimal solution of the cell may cause interference to other neighboring cells.

[0005] 2. Enumerating weight combinations to find the optimal weight combination will consume a lot of computing resources when there are many weight combinations, resulting in a large amount of computation and poor timeliness.

[0006] 3. The method of optimizing the number of iterations using genetic algorithms requires encoding the constraints and defining selection and mutation operations for the weight optimization model. The quality of these definitions directly affects performance, making it quite challenging. Summary of the Invention

[0007] In view of the above problems, embodiments of the present invention are proposed to provide a cell beam adjustment method, apparatus and device to overcome or at least partially solve the above problems, thereby realizing the joint optimization adjustment of the beam direction of multiple cells in the target area and improving the optimization effect.

[0008] According to one aspect of the present invention, a beam adjustment method for a cell is provided, the method comprising:

[0009] The beam weight combination scheme of at least one cell in the target area is reduced in order during the merging period to obtain particles corresponding to the beam weight combination scheme. Each particle includes a set of beam weight schemes, and the beam weight is an adjustment factor for adjusting the direction of the beam according to the terminal position in the cell.

[0010] Obtain the cell coverage rate corresponding to each particle;

[0011] The target particle is determined based on the cell coverage rate corresponding to each particle;

[0012] Based on the target particle, perform joint beam direction adjustment on at least one cell within the target area.

[0013] Optionally, the beam weight combination scheme of at least one cell in the target area during the merging period is reduced in order, including:

[0014] During the merging period, the beam weights of at least one cell are grouped according to azimuth, downtilt angle and coverage scenario, and the beam weight combination scheme with the most numbers is selected as the initial value scheme.

[0015] During the merging period, if the azimuth difference between the cells corresponding to other schemes besides the initial value scheme and the cells corresponding to the initial value scheme is less than or equal to a first preset difference, and the downtilt angle of at least one cell is less than or equal to the first preset difference, and the coverage scenarios are the same, then the azimuth value of the beam in the other schemes is modified to the azimuth value in the initial value scheme, and the downtilt angle value of the beam in the other schemes is modified to the downtilt angle value in the initial value scheme; otherwise, the other schemes are filtered out until all schemes in the merging period have been processed.

[0016] Optionally, obtain the cell coverage corresponding to each particle, including:

[0017] Simulation results of channel data for at least one cell within the target area during the merging period are obtained;

[0018] Based on the simulation results of each cell, the reference signal received power (RSRP) of each grid in the target area for each beam weight scheme of each cell is obtained;

[0019] The cell coverage rate corresponding to each particle is obtained based on the RSRP of each grid in the target area according to the beam weight scheme of each cell.

[0020] Optionally, based on the RSRP of each grid in the target area for each cell using each beam weighting scheme, the cell coverage rate corresponding to each particle is obtained, including:

[0021] For any given cell, obtain the number of grid cells with a beam weighting scheme that exceed a preset power threshold;

[0022] The ratio of the number of grid cells to the total number of grid cells in the beam weighting scheme of the cell is determined as the coverage rate of the beam weighting scheme of the cell.

[0023] Based on the coverage of each cell's beam weight scheme, the cell coverage corresponding to each particle is obtained.

[0024] Optionally, the target particle is determined based on the cell coverage corresponding to each particle, including:

[0025] For the t-th iteration, the position vector X of particle i i (t), X i (t) represents the cell location corresponding to the beam weight scheme combination. The preset function that calculates the average coverage of each cell's beam weight scheme output is used as the objective function for this particle's current iteration.

[0026] Calculate the individual's optimal value P after the current iteration based on the objective function. i (t) and the population optimum G(t);

[0027] Based on the individual optimal value p i The target particle is determined by using the population optimum G(t) and the population optimum G(t).

[0028] Optionally, the

[0029] The

[0030] Where i represents the current number of particles, N represents the total number of particles, j represents the current iteration round, and t represents the maximum number of iteration rounds.

[0031] Optionally, the cell optimization method further includes:

[0032] Based on the results of the t-th iteration, the velocity and position for the next iteration are updated according to the following formula:

[0033] V i (t+1)=wV i (t)+c1*r1*(P i (t)-X i (t))+c2*r2*(G(t)-X i (t))X i (t+1)=X i (t)+V i (t+1)

[0034] Among them, V i (t) is the particle velocity at the start of iteration t, X i (t) is the position of the particle at the beginning of iteration t, w is the inertia weight, c1 is the first acceleration constant, r1 is the first random function, c2 is the second acceleration constant, and r2 is the second random function;

[0035] When the current iteration number exceeds the preset maximum iteration number, the iteration stops and the iteration result of the previous iteration of the current iteration is output.

[0036] According to another aspect of the present invention, a beam adjustment device for a cell is provided, the device comprising:

[0037] The acquisition module is used to perform order reduction processing on the beam weight combination scheme of at least one cell in the target area during the merging period to obtain particles corresponding to the beam weight scheme combination. Each particle includes a set of beam weight schemes, and the beam weight is an adjustment factor for adjusting the direction of the beam according to the terminal position in the cell; and to obtain the cell coverage corresponding to each particle.

[0038] The processing module is configured to determine a target particle based on the cell coverage corresponding to each particle; and to perform joint beam direction adjustment on at least one cell within the target area based on the target particle.

[0039] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0040] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the cell optimization method described above.

[0041] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the cell optimization method described above.

[0042] According to the scheme provided in the above embodiments of the present invention, by reducing the order of the beam weight combination scheme of at least one cell in the target area during the merging period, particles corresponding to the beam weight scheme combination are obtained. Each particle includes a set of beam weight schemes, and the beam weights are adjustment factors for adjusting the direction of the beam based on the terminal position in the cell. The cell coverage corresponding to each particle is obtained; a target particle is determined based on the cell coverage corresponding to each particle; and joint beam direction adjustment is performed on at least one cell in the target area based on the target particle. This method can jointly optimize and adjust the beam direction of multiple cells, with low computational cost, easily achieving a balance between computational load and performance, effectively utilizing global optimal information, being less affected by local optima, and less affected by the dimension of the problem being solved.

[0043] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more obvious and understandable, specific implementation methods of the embodiments of the present invention are described below. Attached Figure Description

[0044] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0045] Figure 1 A flowchart of a cell beam adjustment method provided in an embodiment of the present invention is shown;

[0046] Figure 2 This invention provides a specific flowchart of a cell beam adjustment method according to another embodiment of the present invention.

[0047] Figure 3 A schematic diagram of the beam adjustment device for a cell provided in an embodiment of the present invention is shown;

[0048] Figure 4 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention is shown. Detailed Implementation

[0049] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0050] Figure 1 This invention illustrates a cell beam adjustment method according to an embodiment of the present invention, the method comprising:

[0051] Step 11: Reduce the order of the beam weight combination scheme of at least one cell in the target area during the merging period to obtain particles corresponding to the beam weight scheme combination. Each particle includes a set of beam weight schemes. The beam weight is an adjustment factor for adjusting the direction of the beam according to the terminal position in the cell.

[0052] Step 12: Obtain the cell coverage rate corresponding to each particle;

[0053] Step 13: Determine the target particle based on the cell coverage rate corresponding to each particle;

[0054] Step 14: Based on the target particle, perform joint beam direction adjustment on at least one cell within the target area.

[0055] It should be noted that the beam weight combination scheme is a combination of beam weight schemes of at least two cells in the target area. For example, cell 1 has weight schemes A and B, cell 2 has weight schemes C, D and E, and cell 3 has weight schemes E and F. Here, the weight combination can be weight schemes A, C and E, or weight schemes A, D and F or any other combination.

[0056] The beam weighting scheme combination is the result obtained after the beam weighting scheme has been reduced in order.

[0057] This embodiment of the invention reduces the order of beam weight combination schemes for at least one cell within a target area during a merging period to obtain particles corresponding to the beam weight combination schemes. Based on the cell coverage rate corresponding to each particle, target particles are determined. Then, based on the target particles, joint beam direction adjustment is performed on at least one cell within the target area. This method enables joint optimization adjustment of beam directions for multiple cells, with low computational cost, easily achieving a balance between computational complexity and performance. It effectively utilizes global optimal information, is less affected by local optima, and is less influenced by the dimension of the problem being solved.

[0058] In an optional embodiment of the present invention, step 11, which involves reducing the order of the beam weight combination scheme of at least one cell within the target area during the merging period, may include:

[0059] Step 111: During the merging period, the beam weights of at least one cell are grouped according to azimuth, downtilt angle and coverage scenario, and the beam weight combination scheme with the most numbers is selected as the initial value scheme.

[0060] Step 112: During the merging period, if the azimuth difference between the cells corresponding to other schemes besides the initial value scheme and the cells corresponding to the initial value scheme is less than or equal to a first preset difference, and the downtilt angle of at least one cell is less than or equal to the first preset difference, and the coverage scenarios are the same, then the azimuth value of the beams in the other schemes is modified to the azimuth value in the initial value scheme, and the downtilt angle value of the beams in the other schemes is modified to the downtilt angle value in the initial value scheme; otherwise, the other schemes are not merged with the initial value scheme until all schemes in the merging period have been processed.

[0061] In this embodiment, the beam weight combination scheme of at least one cell in the target area is downgraded during the merging period. The azimuth and downtilt values ​​of some beam weight combinations that meet the above-mentioned preset requirements can be merged and adjusted, which greatly reduces the processing workload. Some beam weight combination schemes that do not meet the above-mentioned preset requirements can be filtered out and not merged with the initial value scheme. This can reduce the difficulty of the subsequent calculation process, effectively avoid unnecessary calculation processes, simplify the calculation process, and improve the calculation efficiency.

[0062] In an optional specific embodiment of the present invention, the process of reducing the order of beam weight combination schemes for at least one cell within the target area during the merging period may include:

[0063] Step 1: During the merging period, the beam weights of at least one cell are grouped according to azimuth, downtilt angle and coverage scenario, and the beam weight combination scheme with the most numbers is selected as the initial value scheme, denoted as Scheme A;

[0064] Step 2: If the azimuth difference between the cell corresponding to Scheme B and the cell corresponding to Scheme A is less than or equal to 3, and the downtilt angle is less than or equal to 3, and the coverage scenarios are the same, then modify the azimuth and downtilt values ​​in Scheme B to the azimuth and downtilt values ​​in Scheme A; otherwise, filter out Scheme B.

[0065] Step 3: Repeat steps 1 and 2 until all schemes in the merging period have been merged.

[0066] In another optional embodiment of the present invention, step 12 may include:

[0067] Step 121: Obtain simulation results of channel data for at least one cell within the target area during the merging period;

[0068] Step 122: Based on the simulation results of each cell, obtain the reference signal received power (RSRP) of each grid in the target area for each beam weight scheme of each cell;

[0069] Step 123: Based on the RSRP of each grid in the target area for each cell according to the beam weighting scheme of each cell, obtain the cell coverage rate corresponding to each particle.

[0070] The simulation results can be obtained by simulating the data using a ray simulation engine, but are not limited to this method.

[0071] In this embodiment, the cell coverage rate corresponding to each particle is calculated. The corresponding coverage rate can be determined based on the density of terminals in each cell of the target area, which facilitates subsequent calculations and improves the accuracy of the calculations.

[0072] In another optional embodiment of the present invention, step 123 may include:

[0073] Step 1231: For any cell, obtain the number of grid cells with a beam weighting scheme that are higher than the preset power threshold.

[0074] Step 1232: The ratio of the number of grid cells to the total number of grid cells in the beam weighting scheme of the cell is determined as the coverage rate of the beam weighting scheme of the cell.

[0075] Step 1233: Based on the coverage of each cell's beam weight scheme, obtain the cell coverage corresponding to each particle.

[0076] In this embodiment, the coverage rate of a cell's beam weighting scheme is obtained based on the number of grids exceeding a preset power threshold under the beam weighting scheme. Then, based on the coverage rate of each cell's beam weighting scheme, the cell coverage rate corresponding to each particle is obtained. This further improves the calculation accuracy and facilitates subsequent calculations.

[0077] In another optional embodiment of the present invention, step 13 may include:

[0078] Step 131, for the t-th iteration, the position vector X of particle i... i (t), X i (t) represents the location of the cell corresponding to the beam weight scheme combination. The preset function of the average coverage output of the beam weight scheme of each cell is used as the objective function of this particle in this round of iteration.

[0079] Step 132: Calculate the individual optimal value P after the current iteration based on the objective function. i (t) and the population optimum G(t);

[0080] Step 133, based on the individual optimal value P i The target particle is determined by using the population optimum G(t) and the population optimum G(t).

[0081] Wherein, the individual optimal value is:

[0082] The optimal value for the group:

[0083] Where i represents the current number of particles, N represents the total number of particles, j represents the current iteration round, and t represents the maximum number of iteration rounds.

[0084] In this embodiment, the beam weight combination scheme is mapped to a vector. By utilizing the directional characteristics of the vector, other vectors are guided to move based on the globally optimal vector and the optimal vector in all its iterations. This makes it easy to achieve a balance between computational load and performance.

[0085] In another optional embodiment of the present invention, the cell optimization method may further include:

[0086] Step 15: Based on the results of the t-th iteration, update the velocity and position for the next iteration using the following formula:

[0087] V i (t+1)=wV i (t)+c1*r1*(P i (t)-X i (t))+c2*r2*(G(t)-X i (t))X i (t+1)=X i (t)+V i (t+1)

[0088] Among them, V i (t) is the particle velocity at the start of iteration t, X i (t) is the position of the particle at the beginning of iteration t, w is the inertia weight, c1 is the first acceleration constant, r1 is the first random function, c2 is the second acceleration constant, and r2 is the second random function;

[0089] When the current iteration number exceeds the preset maximum iteration number, the iteration stops and the iteration result of the previous iteration of the current iteration is output.

[0090] In this embodiment, the above calculation process requires fewer parameters, consumes fewer computing resources, has lower computational complexity, faster convergence speed, and higher timeliness.

[0091] Figure 2 This invention illustrates a specific flow of a cell optimization method provided by an embodiment of the present invention. It involves merging beam weight schemes for at least one cell within a target area that is output hourly via Massive MIMO (Massive Multiple Input Multiple Output). For each time period, data from cells whose scheme category is regional joint commissioning is simulated using a ray tracing simulation engine. Based on the simulation results, the optimal beam weight scheme for the cells in the regional joint commissioning category is determined using a convergence function, and the optimal solution of the optimal beam weight scheme is output. Specifically, this may include:

[0092] Step 21: Arrange and combine the beam weight schemes of each cell in the target area during the merging period to obtain the beam weight combination scheme;

[0093] For example, during the merging period, cells A, B, and C each have two corresponding beam weighting schemes, for a total of eight beam weighting schemes. These eight beam weighting schemes are combined, resulting in 2 to the power of 3 combinations. Each combination can be constructed as a particle (the particle has a dimension of d, where the size of d is the number of cells in the joint debugging area).

[0094] Step 22: For each cell with a different beam weighting scheme, map the beam weighting scheme to different integer values ​​(initialization settings);

[0095] Here, since the different beam weighting schemes for each cell are mapped to discrete integer values, the initial position is not completely random and should be selected from discrete values. Both the initial position and direction vectors are initialized to zero. Furthermore, because each cell has a different beam weighting scheme, a threshold must be set for each vector dimension to prevent out-of-bounds errors. For example, if the first cell has three weighting schemes, they are mapped to [0, 1, 2]. The first dimension of the position vector must move within the range [0, 2], and the result of each movement needs to be rounded to the nearest integer.

[0096] Step 23: Reduce the order of the beam weight combination scheme for at least one cell in the target area during the merging period;

[0097] Step 24: Combine the reduced-order beam weight schemes and simulate them using a ray tracing engine. Based on the simulation results for each cell, obtain the reference signal received power (RSRP) of each beam weight scheme for each cell in each grid of the target area. For example, denote the RSRP of weight scheme h for cell e in grid f as RSRPehf.

[0098] Step 25: Record the number of grids in cell e with a weight scheme h that is higher than -110dB as RSRP_tasteeh, where RSRP_tasteeh = count(RSRPehf>-110dBm), and the total number of grids in cell e with a beam weight scheme h as tasteeh. Finally, the coverage rate of cell e with a beam weight scheme h is covereh = RSRP_tasteeh / tasteeh.

[0099] Step 26: For the t-th iteration, we know the position vector X of particle i. i The vector (t) represents the weights of all cells. The average of the coverage (covereh) output by the beam weight scheme for each cell is taken to obtain the objective function for the current iteration of that particle. Where E is the number of cells, and P is the optimal value of each individual after the current iteration, calculated according to the objective function. i(t) and the group optimal value G(t), where G(t) is the optimal beam weight combination scheme determined from multiple beam weight combination schemes. The beams in each cell are adjusted according to this scheme, so that the priority effect of the cell is better.

[0100]

[0101] Where i represents the current number of particles, N represents the total number of particles, j represents the current iteration round, and t represents the maximum number of iteration rounds.

[0102] Step 27: Update the velocity and position for the next iteration, where Vi(t) is the particle's velocity at the start of iteration t, and X... i (t) is the position of the particle at the beginning of iteration t:

[0103] V i (t+1)=wV i (t)+c1*r1*(P i (t)-X i (t))+c2*r2*(G(t)-X i (t))

[0104] X i (t+1)=X i (t)+V i (t+1)

[0105] Among them, the acceleration constants c1 and c2: c1 represents learning from one's own experience, and c2 represents learning from the experience of the group. Generally, c1 = c2 = 2.

[0106] Random functions r1 and r2 are generally set to random(0, 1);

[0107] The inertia weight w can be set to decrease linearly from 0.9 to 0.1 as the number of iterations increases;

[0108] The maximum number of iterations T and the number of particles N are set to 50. If the number of permutations and combinations is less than 1000, the number of particles is the number of solutions. If the number of permutations and combinations is greater than 1000, the number of permutations and combinations is first divided by the number of iterations, and then the number of particles is checked to see if it is less than 500. If it is less than 500, then it is equal to 500. For example, if the number of iterations is 50 and the number of solutions is 3000, then the number of particles = 3000 / 50 < 500, so the final number of particles is 500. Due to limited computing resources, the upper limit of iterations needs to be limited according to actual needs. For example, if the number of solutions is greater than 100,000, then the number of particles is set to 2000.

[0109] Step 28: When the current iteration number is greater than the set maximum iteration number, stop the iteration and output the result G(t) of the previous iteration.

[0110] In the above embodiments of the present invention, optimization can be performed on multiple cells simultaneously with low computational cost, easily achieving a balance between computational load and performance, effectively utilizing global optimal information, being less affected by local optima, and having little impact from the dimension of the problem being solved.

[0111] The above-described scheme of this invention uses the types of merging time schemes for each cell within a given time period as the selection space, and permutes and combines the beam weight schemes of all cells. By balancing the number of iterations and particles based on the number of permutations and combinations, it is more conducive to finding the global optimum, with lower space complexity and less computational resource consumption. This algorithm has a fast convergence speed and high timeliness. It has a large optimization space, making it suitable for large-dimensional computations, meaning it can optimize many cells simultaneously. Particles explore the solution space in a "leap" manner, resulting in low computational cost. The particle size and number of iterations can be set, easily achieving a balance between computational load and performance. Compared to other biomimetic algorithms, this algorithm requires less code and parameters. The particle swarm optimization algorithm shares information by finding the current optimum, effectively utilizing global optimum information, is less affected by local optima, and is less affected by the dimensionality of the problem being solved, allowing for simultaneous optimization of multiple cells.

[0112] Figure 3 An embodiment of the present invention illustrates a cell optimization device 30, the device 30 comprising:

[0113] The acquisition module 31 is used to perform order reduction processing on the beam weight combination scheme of at least one cell in the target area during the merging period to obtain particles corresponding to the beam weight scheme combination. Each particle includes a set of beam weight schemes, and the beam weight is an adjustment factor for adjusting the direction of the beam according to the terminal position in the cell. The module 31 is also used to obtain the cell coverage corresponding to each particle.

[0114] The processing module 32 is used to determine the target particle based on the cell coverage corresponding to each particle; and to perform joint beam direction adjustment on at least one cell in the target area based on the target particle.

[0115] Optionally, the beam weight combination scheme of at least one cell in the target area during the merging period is reduced in order, including:

[0116] During the merging period, the beam weights of at least one cell are grouped according to azimuth, downtilt angle and coverage scenario, and the beam weight combination scheme with the most numbers is selected as the initial value scheme.

[0117] During the merging period, if the azimuth difference of the beam velocity between the cells corresponding to other schemes besides the initial value scheme and the cells corresponding to the initial value scheme is less than or equal to a first preset difference, and the downtilt angle of at least one cell is less than or equal to the first preset difference, and the coverage scenarios are the same, then the azimuth value of the beam in the other schemes is modified to the azimuth value in the initial value scheme, and the downtilt angle value of the beam in the other schemes is modified to the downtilt angle value in the initial value scheme; otherwise, the other schemes are not merged with the initial value scheme until all schemes in the merging period have been processed.

[0118] Optionally, obtain the cell coverage corresponding to each particle, including:

[0119] Simulation results of channel data for at least one cell within the target area during the merging period are obtained;

[0120] Based on the simulation results of each cell, the reference signal received power (RSRP) of each grid in the target area for each beam weight scheme of each cell is obtained;

[0121] The cell coverage rate corresponding to each particle is obtained based on the RSRP of each grid in the target area according to the beam weight scheme of each cell.

[0122] Optionally, based on the RSRP of each grid in the target area for each cell using each beam weighting scheme, the cell coverage rate corresponding to each particle is obtained, including:

[0123] For any given cell, obtain the number of grid cells with a beam weighting scheme that exceed a preset power threshold;

[0124] The ratio of the number of grid cells to the total number of grid cells in the beam weighting scheme of the cell is determined as the coverage rate of the beam weighting scheme h of the cell.

[0125] Based on the coverage of each cell's beam weight scheme, the cell coverage corresponding to each particle is obtained.

[0126] Optionally, the target particle is determined based on the cell coverage corresponding to each particle, including:

[0127] For the t-th iteration, the position vector X of particle i i (t), X i (t) represents the location of the cell corresponding to the beam weight scheme combination. The preset function of the average coverage output of the beam weight scheme of each cell is used as the objective function of this particle in this round of iteration.

[0128] Calculate the individual's optimal value P after the current iteration based on the objective function. i(t) and the population optimum G(t);

[0129] Based on the individual optimal value P i The target particle is determined by using the population optimum G(t) and the population optimum G(t).

[0130] Optionally, the individual optimal value:

[0131] The optimal value for the group:

[0132] Where i represents the current number of particles, N represents the total number of particles, j represents the current iteration round, and t represents the maximum number of iteration rounds.

[0133] Optionally, the processing module 32 can also be used to: update the speed and position of the next iteration according to the following formula based on the result of the t-th iteration:

[0134] V i (t+1)=wV i (t)+c1*r1*(P i (t)-X i (t))+c2*r2*(G(t)-X i (t))

[0135] X i (t+1)=X i (t)+V i (t+1)

[0136] Among them, V i (t) is the particle velocity at the start of iteration t, X i (t) is the position of the particle at the beginning of iteration t, w is the inertia weight, c1 is the first acceleration constant, r1 is the first random function, c2 is the second acceleration constant, and r2 is the second random function;

[0137] When the current iteration number exceeds the preset maximum iteration number, the iteration stops and the iteration result of the previous iteration of the current iteration is output.

[0138] It should be noted that in this embodiment, the device is the same as the method described above. All implementations in the method embodiments described above are applicable to the device embodiments and can achieve the same technical effect.

[0139] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the cell optimization method in any of the above method embodiments.

[0140] Figure 4The diagram shows a schematic of the structure of a computing device provided in an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0141] like Figure 4 As shown, the computing device may include a processor, a communications interface, memory, and a communications bus.

[0142] The processor, communication interface, and memory communicate with each other via a communication bus. The communication interface is used to communicate with other network elements, such as clients or other servers. The processor executes programs, specifically the relevant steps described in the cell optimization method embodiment for computing devices.

[0143] Specifically, the program may include program code, which includes computer operation instructions.

[0144] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0145] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0146] Specifically, the program can be used to cause the processor to execute the cell optimization method in any of the above method embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units in the above cell optimization method embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0147] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the embodiments of the present invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the embodiments of the present invention.

[0148] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0149] Similarly, it should be understood that, in order to streamline the embodiments of the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0150] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0151] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0152] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0153] It should be noted that the above embodiments are illustrative of the present invention and not restrictive of the invention, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the present invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A beam adjustment method for a cell, characterized in that, The method includes: The beam weight combination schemes of at least one cell within the target area are reduced in order during the merging period to obtain particles corresponding to the beam weight combination schemes. Each particle includes a set of beam weight schemes, where the beam weights are adjustment factors used to adjust the beam direction based on the terminal position within the cell. During the merging period, the beam weights of at least one cell are grouped according to azimuth, downtilt angle, and coverage scenario, and the beam weight combination scheme with the most occurrences is selected as the initial value scheme. During the merging period, if the beam weights of other schemes besides the initial value scheme are... If the azimuth difference between the beams of a cell and the cell corresponding to the initial value scheme is less than or equal to a first preset difference, and the downtilt angle of at least one cell is less than or equal to the first preset difference, and the coverage scenarios are the same, then the azimuth values ​​of the beams in other schemes are modified to the azimuth values ​​in the initial value scheme, and the downtilt values ​​of the beams in other schemes are modified to the downtilt values ​​in the initial value scheme; otherwise, the other schemes are not merged with the initial value scheme until all schemes in the merging period have been processed; the particles are obtained according to the particle swarm optimization algorithm. The cell coverage rate corresponding to each particle is obtained; wherein, the simulation results of channel data of at least one cell in the target area during the merging period are obtained; based on the simulation results of each cell, the reference signal received power (RSRP) of each beam weight scheme of each cell in each grid of the target area is obtained; for any cell, the number of grids with a power higher than a preset power threshold under the beam weight scheme of the cell is obtained; the ratio of the number of grids to the total number of grids in the beam weight scheme of the cell is determined as the coverage rate of the beam weight scheme of the cell; based on the coverage rate of each beam weight scheme of each cell, the cell coverage rate corresponding to each particle is obtained; The target particle is determined based on the cell coverage rate corresponding to each particle; Based on the target particle, perform joint beam direction adjustment on at least one cell within the target area.

2. The beam adjustment method for a cell according to claim 1, characterized in that, Based on the cell coverage rate corresponding to each particle, the target particle is determined, including: For the t-th iteration, the position vector X of particle i i (t), X i (t) represents the cell location corresponding to the beam weight scheme combination. The preset function that calculates the average coverage of each cell's beam weight scheme output is used as the objective function for this particle's current iteration. Calculate the individual's optimal value after the current iteration based on the objective function. and group optimal value ; Based on the individual optimal value and group optimal value Identify the target particle.

3. The beam adjustment method for a cell according to claim 2, characterized in that, The ; The ; Where i represents the current number of particles, N represents the total number of particles, j represents the current iteration round, and t represents the maximum number of iteration rounds.

4. The beam adjustment method for a cell according to claim 2, characterized in that, Also includes: Based on the results of the t-th iteration, the velocity and position for the next iteration are updated according to the following formula: in, It is the particle's velocity at the start of iteration t. It is the position of the particle at the start of iteration t. For inertial weights, The first acceleration constant, For the first random function, The second acceleration constant, It is the second random function; When the current iteration number exceeds the preset maximum iteration number, the iteration stops and the iteration result of the previous iteration of the current iteration is output.

5. A beam adjustment device for a cell, characterized in that, The device includes: The acquisition module is used to reduce the order of beam weight combination schemes of at least one cell in the target area during the merging period, to obtain particles corresponding to the beam weight combination schemes. Each particle includes a set of beam weight schemes, where the beam weights are adjustment factors for adjusting the direction of the beam based on the terminal position within the cell. It also obtains the cell coverage rate corresponding to each particle. During the merging period, the beam weights of at least one cell are grouped according to azimuth, downtilt angle, and coverage scenario, and the beam weight combination scheme with the most occurrences is selected as the initial value scheme. During the merging period, if the azimuth difference between the cells corresponding to other schemes (excluding the initial value scheme) and the cells corresponding to the initial value scheme is less than or equal to a first preset difference, and the downtilt angle of the at least one cell is less than or equal to the first preset difference, and the coverage scenarios are the same, then the azimuth difference of the beams in the other schemes is adjusted. The azimuth angle value is modified to the azimuth angle value in the initial value scheme, and the downtilt angle value of the beam in other schemes is modified to the downtilt angle value in the initial value scheme; otherwise, the other schemes are not merged with the initial value scheme until all schemes in the merging period are processed; the particles are obtained according to the particle swarm algorithm; the simulation results of the channel data of at least one cell in the target area during the merging period are obtained; based on the simulation results of each cell, the reference signal received power (RSRP) of each grid in the target area for each beam weight scheme of each cell is obtained; for any cell, the number of grids with a power higher than a preset power threshold under the beam weight scheme of the cell is obtained; the ratio of the number of grids to the total number of grids in the beam weight scheme of the cell is determined as the coverage of the beam weight scheme of the cell; based on the coverage of each beam weight scheme of each cell, the cell coverage corresponding to each particle is obtained; The processing module is configured to determine a target particle based on the cell coverage corresponding to each particle; and to perform joint beam direction adjustment on at least one cell within the target area based on the target particle.

6. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the method as described in any one of claims 1-4.

7. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the method as described in any one of claims 1-4.

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