CN113133022B - Download rate improving method and system based on MIMO multipath construction - Google Patents
Download rate improving method and system based on MIMO multipath construction Download PDFInfo
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Abstract
The invention discloses a download rate improving method and a download rate improving system based on MIMO multipath construction, wherein the method is characterized in that initial beam configuration information is respectively set for each cell to be planned; according to initial beam configuration information of each cell to be planned, simulation analysis is carried out on network performance of each grid through a propagation model, and coverage information and multipath information of each grid are obtained in a ray tracing mode; determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station; and carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to the iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized. According to the invention, through constructing MIMO multipath, acquiring grid coverage information and multipath information through simulation, and further realizing multi-stream transmission through iterative optimization processing, the download rate is improved, and the problems of low efficiency, long time consumption and complex process of manual analysis and optimization in the prior art are solved.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a download rate improving method and system based on MIMO multipath construction.
Background
The existing download rate improving method mainly includes that data such as drive test data, frequency sweep data or network management data are collected manually, the structure (weak coverage, overlapping coverage, interference cells, fault cells and the like) of a network is analyzed and optimized, a network gauge network optimization tool is used for optimizing the network structure in combination with user distribution and surface feature and landform, and the main lobe of an antenna is mainly considered to be directed at an area where users gather to improve the network coverage and quality, so that higher download rate is obtained.
However, although the manual optimization method can fully consider the diversification and the complication of the wireless environment, a large amount of human resource investment is required, various factors need to be considered, the time consumption is long, the process is complicated, and especially for the optimization of a large-range and whole-network antenna, the manual optimization cannot achieve a good effect.
Disclosure of Invention
In view of the above, the present invention has been made to provide a download rate boost method and system based on constructing MIMO multipaths that overcomes or at least partially solves the above-mentioned problems.
According to one aspect of the invention, a download rate improving method based on MIMO multipath construction is provided, which comprises the following steps:
setting initial beam configuration information for each cell to be planned respectively;
according to initial beam configuration information of each cell to be planned, simulation analysis is carried out on network performance of each grid through a propagation model, and coverage information and multipath information of each grid are obtained in a ray tracing mode;
determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station;
and carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to an iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized.
According to another aspect of the present invention, there is provided a download rate increasing system based on MIMO multipath construction, including:
the setting module is used for respectively setting initial beam configuration information for each cell to be planned;
the analysis module is used for carrying out simulation analysis on the network performance of each grid through a propagation model according to the initial beam configuration information of each cell to be planned and acquiring the coverage information and the multipath information of each grid in a ray tracing mode;
the cell to be optimized determining module is used for determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station;
and the processing module is used for carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to an iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized.
According to yet another aspect of the present invention, there is provided a computing device comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the download rate improving method based on the MIMO multipath construction.
According to still another aspect of the present invention, a computer storage medium is provided, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the method for increasing a download rate based on constructing MIMO multipath as described above.
According to the method and the system for improving the download rate based on the MIMO multipath construction, initial beam configuration information is respectively set for each cell to be planned; according to initial beam configuration information of each cell to be planned, simulation analysis is carried out on network performance of each grid through a propagation model, and coverage information and multipath information of each grid are obtained in a ray tracing mode; determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station; and carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to the iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized. According to the invention, through constructing MIMO multipath, grid coverage information and multipath information are obtained through simulation, and then iterative optimization processing is carried out to realize multi-stream transmission, so that the download rate is improved, and the problems of low efficiency, long time consumption and complicated process of the conventional manual analysis and optimization are solved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flowchart of a download rate increasing method based on MIMO multipath construction according to an embodiment of the present invention;
fig. 2 shows a flowchart of an iterative optimization algorithm based on a download rate boost method for constructing MIMO multipath according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a download rate boost system based on MIMO multipath construction according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computing device provided in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Multiple Input and Multiple Output (MIMO) technology can improve the capacity and spectrum utilization of a communication system by a Multiple without increasing the bandwidth. It can be defined that there are many independent channels between the sending end and the receiving end, that is, there is sufficient space between the antenna units, so that the correlation of signals between antennas is eliminated, the link performance of signals is improved, and the data throughput is increased.
Example one
Fig. 1 shows a flowchart of an embodiment of a download rate increasing method based on MIMO multipath construction, as shown in fig. 1, the method includes the following steps:
s101: and setting initial beam configuration information for each cell to be planned.
In an optional manner, before step S101, the method further includes:
acquiring an association mapping model between scene characteristics and beam configuration information and scene related information of each cell to be planned; for each cell to be planned, extracting the characteristics of the scene related information of the cell to be planned to obtain the scene characteristics of the cell to be planned; and inputting the scene characteristics of the cell to be planned into the association mapping model, and taking the output result of the association mapping model as the initial beam configuration information of the cell to be planned.
Wherein the scene-related information includes: building distribution information under a cell coverage area, such as building height, area, number, distance, etc.; feature distribution information under the cell coverage area, for example, information such as occupation ratios of various feature types such as a green space, a road, a large building, and a village; cell parameters such as base station altitude, antenna azimuth, electrical downtilt, mechanical downtilt, transmit power, location, etc. The corresponding scene features are respectively as follows: the scene characteristics corresponding to the building distribution information under the cell coverage area are building characteristics; the scene characteristics corresponding to the ground feature distribution information under the cell coverage area are ground feature characteristics; and the scene characteristics corresponding to the cell engineering parameter information are cell characteristics.
The generation process of the association mapping model between the scene features and the beam configuration information is further realized by the following steps 1011 to 1013.
S1011: and acquiring scene related information of each sample cell and beam configuration information of each sample cell from the training sample library.
S1012: extracting the characteristics of the scene related information of each sample cell to obtain the scene characteristics of each sample cell,
s1013: and training to obtain an association mapping model between the scene characteristics and the beam configuration information according to the scene characteristics of each sample cell and the beam configuration information of each sample cell.
In an optional manner, in order to ensure the accuracy of the association mapping model, the association mapping model needs to be updated and trained periodically, and further, after the target beam configuration information of each cell to be optimized is obtained by using this embodiment, the method may further include step 1014 and step 1015.
S1014: and determining scene related information of each new sample cell and beam configuration information of each new sample cell according to the scene related information of each cell to be optimized and the target beam configuration information of each cell to be optimized, and adding the scene related information of each new sample cell and the beam configuration information of each new sample cell to a training sample library so as to update the training sample library.
S1015: and updating and training the association mapping model according to the scene related information of each sample cell in the updated training sample library and the beam configuration information of each sample cell.
S102: and according to the initial beam configuration information of each cell to be planned, performing simulation analysis on the network performance of each grid through a propagation model, and acquiring the coverage information and multipath information of each grid in a ray tracing mode.
In the step, coverage information and multi-path information of each grid contained in each cell to be planned are obtained through a propagation model simulation and ray tracing mode according to initial beam configuration information of each cell to be planned, wherein the coverage information of the grid refers to the coverage information of beams under each grid, an interaction diagram of the beams in each grid is generated through a propagation model, and the multi-path propagation of the beams comprises; reflection, diffraction, transmission, and scattering, etc.; reflection refers to reflection of a beam by the ground, roof or vertical facade of a building; diffraction refers to the diffraction of a beam over an obstacle (ground, building, vegetation) or the diffraction of a beam over a vertical edge of a building; transmission refers to transmission of beams through vegetation or building exterior walls; scattering refers to the diffuse scattering of a beam through the exterior walls of a building.
Ray tracing refers to generating a large number of rays and ray pipelines at the position of an emission source, wherein each ray needs to be traced and calculated, and meanwhile, each ray needs to be subjected to intersection testing with a plane in a building so as to judge the propagation mechanism of the ray.
By dividing the space into a number of cube elements (voxels) which are evenly distributed throughout the space, each voxel having a unique coordinate number, the location of the voxel can be quickly determined by the coordinate number. Then, voxel preprocessing of scene polyhedrons is carried out, and the voxel and each polyhedron are stored in a data list of the voxel. The initial set of tubes is constructed from the transmit antenna information and the initial tube construction parameters such as antenna position, antenna power, antenna pattern and information of the number of equal parts per face of the regular icosahedron. And traversing the initial ray tube set and tracking each ray. And carrying out intersection test on each ray with directivity in the ray tube, a polyhedral surface and a receiving point plane in a scene, finding an intersection surface and then carrying out next judgment. In the process of intersection testing of a polyhedron, buildings in a scene have different heights and surfaces, and planes which cannot be intersected can be removed by adopting a back collection algorithm in order to reduce the calculated amount.
The back-side acquisition algorithm refers to that when a ray intersects an object with a closed side, the ray passes through the surface of the object at least twice, and the ray has at least two intersection points with the surface of the object. When the construction function with the external normal vector of the object surface being n generates the polyhedron, the external surface of the object is assumed to be over against the user, and the points forming the plane are input according to the anticlockwise direction, so that the generated vector direction meets the right-hand rule and is over against the user, and the intersection point of the near intersection points always meets the formula: (r2-r1) n < 0; where r2 is the vector of the starting point of the ray, r1 is the vector of the end point of the ray, and n is the outer normal vector of the surface at the intersection.
Judging the intersection test result:
if the intersecting plane is the plane where the intersecting point is located, the corresponding receiving point needs to store the path information of the ray, including field intensity, time delay, receiving angle and the like, and the result is stored in a data list for continuing the ray tube tracking;
if the intersecting plane is a polyhedral surface, whether the ray tube is reflected, transmitted, refracted or diffracted is judged according to the intersecting condition of the ray tube and the plane, a next generation of ray tube is correspondingly constructed and added into the ray tube set;
if the scene is a boundary plane, the tracking calculation of the ray tube is considered to be up to now, the tracking is not continued, and a next generation ray tube is not constructed.
When the tracking of the ray tube is finished, the diffracted next-generation ray tube needs to be subjected to iterative calculation continuously until the diffraction iteration times of the ray tube reach the parameter setting requirement; and completing the tracking calculation of other tubes in the tube set in turn until all the tubes in the tube set are completely tracked.
In the process of ray tracing, when a ray intersects with a receiving plane, signal information carried by the ray is stored in a data list, and the signal receiving condition of each receiving point needs to be output, including path information such as the position of the receiving point, each path ID, signal strength, time delay, arrival angle and the like. In the statistical channel model, statistical distributions such as field strength, time delay, phase, arrival angle, etc. are counted, and a corresponding channel model is established accordingly. And dividing the paths with certain similarity into a cluster by utilizing information such as time delay, power angle and the like in the ray tracing simulation result. Thereby obtaining statistical information of corresponding multipath, i.e. multipath information.
S103: and determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station.
In an optional manner, step S103 further includes: screening grids, of which the distances between the grids and the base station accord with a preset distance threshold value and the coverage information accords with a weak coverage threshold value, from the grids; clustering the grids obtained by screening to obtain a plurality of weak coverage areas; and determining the areas to be optimized according to the plurality of weak coverage areas, and determining the cells in the areas to be optimized as the cells to be optimized.
Specifically, coverage information of each grid is obtained through propagation model simulation according to the step S102, the grids which are less than a preset distance threshold from a base station and have the coverage levels of the grids less than the preset threshold are selected as weak coverage grids, the weak coverage grids which are closer to each other are clustered into one class through a clustering algorithm, and a rectangular envelope is generated according to the clustered weak coverage areas to obtain an area to be optimized; and determining the cells contained in the area to be optimized as the cells to be optimized. It should be noted that, a protection area needs to be generated outside the area to be optimized, and the width of the protection area is 500 meters (adjustable) outside the area to be optimized. After the beam configuration is adjusted, it needs to be ensured that the indexes of the two areas, namely the area to be optimized and the protection area, are not deteriorated.
S104: and carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to the iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized.
In an alternative, fig. 2 is a flowchart of the iterative optimization algorithm, and as shown in fig. 2, step S104 further includes steps 1-6:
Generating a region to be optimized and a cell to be optimized according to the step S103; the objective function in the iterative optimization algorithm refers to the index to be optimized in the area to be optimized.
And 2, setting a plurality of beam configuration combination schemes for each cell to be optimized, and taking each beam configuration combination scheme as particles in the particle swarm.
Specifically, the particle group is composed of N particles, each of which is an m-dimensional vector, and the ith particle can be expressed as: xi＝Xi1，Xi2，......XimWherein i ═ 1, 2, 3imNamely the beam configuration information of the mth cell to be optimized.
And 3, calculating the fitness of each particle by using the objective function.
Specifically, the objective function for performing beam optimization is a fitness function, and the general fitness optimization objectives include the following:
1. reducing the fraction of weak coverage grids in the area to be optimized, wherein MweakRefers to the number of weak coverage grids (the coverage level of the grid is less than the threshold), M is the total coverage grid number of the area, f1(x) Refers to the proportion of the weak coverage grid, as shown in formula (1):
2. the average grid overlap coverage in the area to be optimized is reduced,
the overlapping coverage of the grid refers to the number of adjacent cells of which the level difference between the grid and the main cell is less than a threshold f2(x) For grid overlap coverage, as in equation (2):
3. the richness of the multi-path is increased,
number of grids with transmission paths greater than or equal to 3, f3(x) Multipath richness, as in equation (3):
meanwhile, in order to ensure that the statistical calculation result of the protection region does not deteriorate, the constraint condition needs to be considered while establishing the objective function, and the constraint function is as in formula (4):
f′k(x)≤f′k(x0)，k＝1，2，....，K； (4)
the multi-objective problem translates into a single objective problem: weighted summation is performed on all targets, as in equation (5):
the belt constraint problem translates into an unconstrained problem: for the constraint condition that the protection area is not deteriorated, the constraint condition can be put into an objective function by a penalty function method, and a final objective function formula is obtained as follows:
and 4, updating the individual extreme value, the global extreme value, the speed and the position of the plurality of particles according to the fitness of the plurality of particles.
Calculating the fitness of the plurality of particles according to the objective function in the step 3, and in each iteration, updating an individual extreme value, a global extreme value, a speed and a position of the plurality of particles, wherein the individual extreme value pBest refers to an optimal solution found by the particles; the global extremum gBest refers to the best solution currently found for the entire population.
In the whole searching process, the particle updates the speed of the particle according to a formula (7) and updates the position of the particle according to a formula (8):
Vi＝w*Vi+c1*r1*(pi-Xi)+c2*r2*(pg-Xi)； (7)
Xi＝Xi+Vi； (8)
where Vi is the velocity of the particle; c1 and c2 refer to learning factors, typically c1 ═ c2 ═ 2; xi is the current position of the particle (current beam configuration information of the cell to be optimized); r1 and r2 are random numbers between (0, 1).
And 6, outputting the target beam configuration information of each cell to be optimized.
And finishing the iterative optimization after the iteration reaches a certain number of times or meets a certain condition, and outputting the target beam configuration information of each cell to be optimized.
By adopting the method provided by the embodiment, the area with poor network performance, namely the weak coverage area, is quickly found based on simulation, the cell to be optimized is determined according to a plurality of weak coverage areas, the iterative optimization is carried out by using an algorithm for constructing MIMO multipath by simulation optimization, multipath information is obtained in a ray tracing mode, and the influence of the wireless environment and the MIMO multipath on the download rate is comprehensively considered through a plurality of optimization targets (such as weak coverage grid occupation ratio, grid overlapping degree, multipath abundance and the like), a reasonable antenna weight configuration proposal is given out, on the premise that the overall coverage is not reduced, the multipath abundance is improved, the weak coverage grid occupation ratio is reduced, the overlapping coverage degree is reduced, so that more different wireless transmission paths are obtained, the signal-to-noise ratio of a wireless link is improved, so that higher download rate and better network performance are obtained, and the low efficiency of the existing manual analysis and optimization is overcome, long time consumption, complex process and high cost for acquiring the multipath information. Meanwhile, the target beam configuration information of each cell to be optimized is determined in a mode of combining supervised learning and iterative optimization, so that resources and time consumed by determining the target beam configuration information in a single mode are reduced, and the target beam configuration information can be accurately determined.
Example two
Fig. 3 shows a schematic structural diagram of an embodiment of a download rate enhancement system based on MIMO multipath construction according to the present invention. As shown in fig. 3, the system includes: a setting module 301, an analysis module 302, a cell to be optimized determination module 303 and a processing module 304.
A setting module 301, configured to set initial beam configuration information for each cell to be planned respectively.
An analysis module 302, configured to perform simulation analysis on the network performance of each grid through a propagation model according to the initial beam configuration information of each cell to be planned, and acquire coverage information and multipath information of each grid in a ray tracing manner.
A cell to be optimized determining module 303, configured to determine each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station.
And the processing module 304 is configured to perform iterative optimization processing on the initial beam configuration information of each cell to be optimized according to an iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized.
In an optional manner, the system further comprises:
the acquisition module is used for acquiring an association mapping model between the scene characteristics and the beam configuration information and scene related information of each cell to be planned; wherein the scene-related information includes: building distribution information under a cell coverage area, ground feature distribution information under the cell coverage area and cell engineering parameter information;
the scene feature extraction module is used for extracting the features of the scene related information of each cell to be planned to obtain the scene features of the cell to be planned;
the initial beam configuration information generation module is used for inputting the scene characteristics of the cell to be planned into the association mapping model and taking the output result of the association mapping model as the initial beam configuration information of the cell to be planned;
the system comprises a sample cell beam configuration information acquisition module, a training sample library and a beam configuration information acquisition module, wherein the sample cell beam configuration information acquisition module is used for acquiring scene related information of each sample cell and beam configuration information of each sample cell from the training sample library;
the sample cell scene feature extraction module is used for extracting features of scene related information of each sample cell to obtain scene features of each sample cell;
and the model training module is used for training to obtain an association mapping model between the scene characteristics and the beam configuration information according to the scene characteristics of each sample cell and the beam configuration information of each sample cell.
In an optional manner, the system further comprises:
the training sample base updating module is used for determining scene related information of each new sample cell and beam configuration information of each new sample cell according to the scene related information of each cell to be optimized and target beam configuration information of each cell to be optimized, and adding the scene related information of each new sample cell and the beam configuration information of each new sample cell to the training sample base so as to update the training sample base;
and the association mapping model updating training module is used for updating and training the association mapping model according to the scene related information of each sample cell in the updated training sample library and the beam configuration information of each sample cell.
In an optional manner, the to-be-optimized cell determining module 303 is further configured to: screening grids, of which the distances between the grids and the base station accord with a preset distance threshold value and the coverage information accords with a weak coverage threshold value, from the grids; clustering the grids obtained by screening to obtain a plurality of weak coverage areas; and determining the areas to be optimized according to the plurality of weak coverage areas, and determining the cells in the areas to be optimized as the cells to be optimized.
In an optional manner, the processing module 304 is further configured to: according to the beam optimization target, constructing a target function in an iterative optimization algorithm, and determining the number of transmission paths in the iterative optimization algorithm according to multi-path information; setting a plurality of beam configuration combination schemes for each cell to be optimized, and taking each beam configuration combination scheme as particles in the particle swarm; calculating the fitness of each particle by using an objective function; updating individual extreme values, global extreme values, speeds and positions of the particles according to the fitness of the particles; judging whether an iteration end condition is met; if yes, outputting target beam configuration information of each cell to be optimized; if not, the fitness of each particle is calculated by continuously utilizing the objective function.
By adopting the system provided by the embodiment, an area with poor network performance, namely a weak coverage area, is quickly found based on simulation, a cell to be optimized is determined according to a plurality of weak coverage areas, iterative optimization is carried out by using an algorithm for constructing MIMO multipath by simulation optimization, multipath information is obtained in a ray tracing mode, and the influence of a wireless environment and the MIMO multipath on a download rate is comprehensively considered through a plurality of optimization targets (such as weak coverage grid occupation ratio, grid overlapping degree, multipath abundance and the like), a reasonable antenna weight configuration proposal is given out, on the premise that the overall coverage is not reduced, the multipath abundance is improved, the weak coverage grid occupation ratio is reduced, the overlapping coverage degree is reduced, so that more different wireless transmission paths are obtained, the signal-to-noise ratio of a wireless link is improved, so that higher download rate and better network performance are obtained, and the low efficiency of the existing manual analysis and optimization is overcome, long time consumption, complex process and high cost for acquiring the multipath information. Meanwhile, the target beam configuration information of each cell to be optimized is determined in a mode of combining supervised learning and iterative optimization, so that resources and time consumed by determining the target beam configuration information in a single mode are reduced, and the target beam configuration information can be accurately determined.
EXAMPLE III
The embodiment of the invention provides a nonvolatile computer storage medium, wherein at least one executable instruction is stored in the computer storage medium, and the computer executable instruction can execute the download rate improving method based on the constructed MIMO multipath in any method embodiment.
The executable instructions may be specifically configured to cause the processor to:
setting initial beam configuration information for each cell to be planned;
according to initial beam configuration information of each cell to be planned, simulation analysis is carried out on network performance of each grid through a propagation model, and coverage information and multipath information of each grid are obtained in a ray tracing mode;
determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station;
and carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to the iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized.
Example four
Fig. 4 is a schematic structural diagram of an embodiment of a computing device according to the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
As shown in fig. 4, the computing device may include: a processor (processor), a Communications Interface (Communications Interface), a memory (memory), and a Communications bus.
Wherein: the processor, the communication interface, and the memory communicate with each other via a communication bus. A communication interface for communicating with network elements of other devices, such as clients or other servers. The processor is configured to execute a program, and may specifically execute the relevant steps in the embodiment of the download rate increase method based on MIMO multipath construction.
In particular, the program may include program code comprising computer operating instructions.
The processor may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The server comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And the memory is used for storing programs. The memory may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program may specifically be adapted to cause a processor to perform the following operations:
setting initial beam configuration information for each cell to be planned;
according to initial beam configuration information of each cell to be planned, simulation analysis is carried out on network performance of each grid through a propagation model, and coverage information and multipath information of each grid are obtained in a ray tracing mode;
determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station;
and carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to an iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, 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 in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means can be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
Claims (10)
1. A download rate improving method based on MIMO multipath construction is characterized by comprising the following steps:
setting initial beam configuration information for each cell to be planned;
according to initial beam configuration information of each cell to be planned, simulation analysis is carried out on network performance of each grid through a propagation model, and coverage information and multipath information of each grid are obtained in a ray tracing mode;
determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station;
and carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to an iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized.
2. The method according to claim 1, wherein before the setting of the initial beam configuration information for each cell to be planned, the method further comprises:
acquiring an association mapping model between scene characteristics and beam configuration information and scene related information of each cell to be planned;
for each cell to be planned, extracting the characteristics of the scene related information of the cell to be planned to obtain the scene characteristics of the cell to be planned;
and inputting the scene characteristics of the cell to be planned into the association mapping model, and taking the output result of the association mapping model as the initial beam configuration information of the cell to be planned.
3. The method of claim 2, further comprising:
acquiring scene related information of each sample cell and beam configuration information of each sample cell from a training sample library;
extracting the characteristics of the scene related information of each sample cell to obtain the scene characteristics of each sample cell;
and training to obtain an association mapping model between the scene characteristics and the beam configuration information according to the scene characteristics of each sample cell and the beam configuration information of each sample cell.
4. The method of claim 3, further comprising:
determining scene related information of each new sample cell and beam configuration information of each new sample cell according to the scene related information of each cell to be optimized and target beam configuration information of each cell to be optimized, and adding the scene related information of each new sample cell and the beam configuration information of each new sample cell to a training sample library to update the training sample library;
and updating and training the association mapping model according to the scene related information of each sample cell in the updated training sample library and the beam configuration information of each sample cell.
5. The method of claim 2, wherein the scene-related information comprises: building distribution information under a cell coverage area, ground feature distribution information under the cell coverage area, and cell engineering parameter information.
6. The method according to any of claims 1-5, wherein the determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station further comprises:
screening grids, of which the distances between the grids and the base station accord with a preset distance threshold value and the coverage information accords with a weak coverage threshold value, from the grids;
clustering the grids obtained by screening to obtain a plurality of weak coverage areas;
and determining areas to be optimized according to the plurality of weak coverage areas, and determining the cells in the areas to be optimized as the cells to be optimized.
7. The method according to any one of claims 1 to 5, wherein the performing iterative optimization processing on the initial beam configuration information of each cell to be optimized according to the iterative optimization algorithm and the multipath information to obtain the target beam configuration information of each cell to be optimized further comprises:
step 1, constructing an objective function in the iterative optimization algorithm according to a beam optimization target, and determining the number of transmission paths in the iterative optimization algorithm according to the multipath information;
step 2, setting a plurality of beam configuration combination schemes for each cell to be optimized, and taking each beam configuration combination scheme as particles in the particle swarm;
step 3, calculating the fitness of each particle by using the objective function;
step 4, updating the individual extreme value, the global extreme value, the speed and the position of the plurality of particles according to the fitness of the plurality of particles;
step 5, judging whether an iteration end condition is met; if yes, executing step 6; if not, skipping to execute the step 3;
and 6, outputting the target beam configuration information of each cell to be optimized.
8. A download rate boost system based on building MIMO multipath, comprising:
the setting module is used for respectively setting initial beam configuration information for each cell to be planned;
the analysis module is used for carrying out simulation analysis on the network performance of each grid through a propagation model according to the initial beam configuration information of each cell to be planned and acquiring the coverage information and the multipath information of each grid in a ray tracing mode;
the cell to be optimized determining module is used for determining each cell to be optimized according to the coverage information of each grid and the distance between each grid and the base station;
and the processing module is used for carrying out iterative optimization processing on the initial beam configuration information of each cell to be optimized according to an iterative optimization algorithm and the multipath information to obtain target beam configuration information of each cell to be optimized.
9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the download rate increase method based on the constructed MIMO multipath in any one of claims 1-7.
10. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the download rate increase method based on constructing MIMO multipath according to any one of claims 1-7.
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| CN115278711A (en) * | 2022-07-04 | 2022-11-01 | 中国电信股份有限公司 | Beam weight determination method and device, electronic equipment and readable storage medium |
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