CN115623491A - Coverage prediction method, device and equipment for wireless network - Google Patents
Coverage prediction method, device and equipment for wireless network Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for predicting the coverage of a wireless network, wherein the method comprises the following steps: acquiring a human body posture model library in a simulation area; obtaining a three-dimensional antenna directional diagram of the terminal according to the human body posture model library; and according to the three-dimensional antenna directional diagram of the terminal, performing simulation prediction on wireless network signals to obtain a coverage prediction result of the wireless network. Through the mode, the method and the device provided by the invention realize that the influence of the human body on the antenna and multipath propagation is considered in the simulation process, the coverage influence caused by the human body in millimeter wave high-frequency bands can be considered, and the network planning and optimization are better served.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a coverage prediction method, a coverage prediction device and coverage prediction equipment for a wireless network.
Background
The coverage evaluation of the wireless network through a planning tool is one of important means for planning and optimizing the high-quality wireless network. The simulation accuracy of each basic index (e.g., level value, interference, rate) of the communication technology is a key to affecting the accuracy of coverage assessment.
With the wide application of mobile internet services, the mobile data traffic is explosively increased, according to survey, 80% of mobile data services occur indoors, the establishment of indoor wireless networks has become a key for mobile operators to meet the requirements of mobile data services and improve the satisfaction degree of users, a large number of indoor coverage networks in various forms such as indoor distributed systems, repeaters, base stations and small base stations are generated, and the indoor coverage networks and outdoor macro base stations jointly form a seamless mobile communication network. The now commonly used frequency bands below 6GHz are already very crowded and in order to find new spectral resources, large manufacturers aim at higher frequencies and larger bandwidths, such as millimeter waves.
5G, so that the future times face the requirements of mass connection and ultrahigh speed, and the millimeter wave technology is mainly applied to capacity expansion of hot spot high-traffic areas. Outdoor, business extremely hot spot areas such as commercial pedestrian blocks, stations, streets, etc. have become millimeter wave representative scenes. Indoor, high-density venues and indoor private enterprise scenes such as concerts, stadiums will also be millimeter wave suitable places. However, due to the complex indoor scene, the electric wave is very easy to be blocked, thereby causing inaccurate coverage prediction of the wireless network.
Disclosure of Invention
In view of the above problems, embodiments of the present invention are proposed to provide a coverage prediction method, apparatus and device for a wireless network that overcome the above problems or at least partially solve the above problems.
According to an aspect of an embodiment of the present invention, there is provided a coverage prediction method for a wireless network, the method including:
acquiring a human body posture model library in a simulation area;
obtaining a three-dimensional antenna directional diagram of the terminal according to the human body posture model library;
and according to the three-dimensional antenna directional diagram of the terminal, performing simulation prediction on wireless network signals to obtain a coverage prediction result of the wireless network.
According to another aspect of the embodiments of the present invention, there is provided a coverage prediction apparatus for a wireless network, including:
the first acquisition module is used for acquiring a human body posture model associated with the terminal in the simulation area;
the second acquisition module is used for acquiring a three-dimensional antenna directional diagram according to the human body posture model;
and the processing module is used for carrying out simulation prediction on wireless network signals according to the three-dimensional antenna directional diagram to obtain a coverage prediction result of the wireless network.
According to still another aspect of an embodiment of the present invention, there is provided a computing device including: 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 coverage prediction method of the wireless network.
According to another aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, where the executable instruction causes a processor to perform operations corresponding to the coverage prediction method of the wireless network.
According to the scheme provided by the embodiment of the invention, the coverage prediction method of the wireless network can be realized by acquiring a human body posture model library in a simulation area; obtaining a three-dimensional antenna directional diagram of the terminal according to the human body posture model library; according to the three-dimensional antenna directional diagram of the terminal, simulation prediction of wireless network signals is carried out to obtain a coverage prediction result of the wireless network, so that the problem that the wireless network coverage prediction result cannot adapt to a 5G indoor scene mainly based on mobile data services is solved, the influence of a human body on an antenna and multipath propagation is considered in the simulation process, the coverage influence caused by the human body in high frequency bands such as millimeter waves can be considered, and the beneficial effects of better serving for network planning and optimization are achieved.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the embodiments of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments 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 embodiments of the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a coverage prediction method for a wireless network according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a standing pose of a mannequin in a mannequin library provided by an embodiment of the invention;
FIG. 3 is a diagram illustrating sitting postures of mannequins in a mannequin library provided by an embodiment of the invention;
FIG. 4 is a diagram illustrating a one-handed Internet surfing gesture of a hand model in a human gesture model library according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a specific workflow of a ray tracing simulation model provided by an embodiment of the present invention;
FIG. 6 is a flow chart illustrating a specific embodiment provided by an embodiment of the present invention;
fig. 7 is a schematic structural diagram illustrating a coverage prediction apparatus of a wireless network according to an embodiment of the present invention;
fig. 8 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 may 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.
Fig. 1 shows a flowchart of a coverage prediction method for a wireless network according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
and step 13, performing simulation prediction on wireless network signals according to the three-dimensional antenna directional diagram of the terminal to obtain a coverage prediction result of the wireless network.
In the embodiment, a human body posture model library in a simulation area is obtained, a three-dimensional antenna directional diagram of a terminal is obtained through the human body posture model library, simulation prediction of wireless network signals is carried out on the three-dimensional antenna directional diagram of the terminal, and finally a coverage prediction result of a wireless network is obtained; in the simulation process, the influence of a human body on an antenna and multipath propagation is considered, and the coverage influence caused by the human body in high frequency bands such as millimeter waves can be considered, so that the network planning and optimization are better served.
In this embodiment, step 11 may include:
and step 111, acquiring a human body posture model library according to the density of the terminal and the human body pose probability information in the simulation area. Wherein the human body posture model library comprises: a body posture model associated with the terminal and a body posture model not associated with the terminal.
In the embodiment, according to the density of the terminal in the simulation area and the probability information of the Human body pose, a corresponding Human body pose model library can be preferably constructed and generated in the simulation area through computer graphics software such as CAD, make Human, poser and the like, the simulation area is divided into at least one grid according to a certain resolution, the central point of each grid respectively corresponds to the position of the terminal, and the resolution is preferably 1m × 1m;
as shown in fig. 2 to 4, the body posture model associated with the terminal may affect a three-dimensional antenna pattern of the terminal and multipath propagation of wireless network signals, and specifically includes: at least one of a standing posture of the human body model, a sitting posture of the human body model, a walking posture of the human body model, a one-hand internet surfing posture of the hand model, a two-hand internet surfing posture of the hand model and a communication posture of the hand model;
the human body posture model which is not associated with the terminal can affect the multipath propagation of wireless network signals, and the method specifically comprises the following steps: at least one of a standing posture of the human body model, a sitting posture of the human body model, a walking posture of the human body model, a one-hand internet surfing posture of the hand model, a two-hand internet surfing posture of the hand model, a conversation posture of the hand model, a natural drooping posture of two hands when the hand model stands in a normal state and a posture of placing the two hands on two legs when the hand model sits in a normal state;
in an optional embodiment of the present invention, the step 12 includes:
and step 121, inputting the human body posture model base and the antenna directional diagram of the terminal into a time domain finite difference simulation model for processing to obtain the three-dimensional antenna directional diagram.
In the embodiment, a human body posture model library and an antenna directional pattern of a terminal are used as input and input into a finite difference time domain simulation (FDTD) model to obtain a three-dimensional antenna directional pattern;
in addition, a three-dimensional antenna directional diagram can be measured in a darkroom measuring mode, specifically, a tester holds a terminal to measure in a darkroom, and the measured three-dimensional antenna directional diagram can be obtained by using an antenna directional diagram measuring method.
In an optional embodiment of the present invention, the step 13 includes:
step 131, performing simulation prediction on a wireless network signal according to configuration information of a human body posture model associated with the terminal, the three-dimensional antenna directional diagram, scene information within a preset geographic range and parameter information of a service base station of the terminal to obtain a coverage prediction result of the wireless network.
Wherein the scene information in the preset geographic range comprises: and the three-dimensional electronic map and/or the preset scene model of the preset geographic range.
Fig. 5 is a schematic diagram illustrating a specific work flow of a ray tracing simulation model provided by an embodiment of the present invention. As shown in fig. 5, when step 131 is implemented, it may include:
step 1311, inputting configuration information of a human body posture model associated with the terminal, the three-dimensional antenna directional diagram, scene information within a preset geographic range, and parameter information of a service base station of the terminal into a ray tracing simulation model for calculation processing to obtain path loss data;
and 1313, obtaining a coverage prediction result of the wireless network according to the received power.
In the embodiment, configuration information of a human body posture model associated with a terminal, the three-dimensional antenna directional diagram, scene information in a preset geographic range and parameter information of a service base station of the terminal are used as input information, a ray tracing simulation model is input, and road loss data are obtained through calculation; the scene information in the preset geographic range comprises a three-dimensional electronic map and/or a preset scene model of the preset geographic range, and the range size of the preset geographic range is preferably larger than the range size of the simulation area; for example, the preset geographic range may include an indoor area including the simulation area or an outdoor area including the simulation area, and may also include indoor and outdoor areas including the simulation area;
and calculating the received power of all associated terminals under each service base station by taking the obtained path loss data, line loss data and the generated power of the terminal as input through a link budget model, thereby obtaining a coverage prediction result of the wireless network.
Ray tracking of the ray tracking simulation model can track multipath propagation such as direct projection, reflection, scattering, transmission and diffraction based on the object geometric relation in the scene model of the real preset geographic range, and can perform electromagnetic calculation according to different propagation models based on material information of objects in the scene model of the preset geographic range, so as to finally realize propagation prediction of the scene model of the real preset geographic range;
in a specific embodiment, the configuration information of the human body posture model associated with the terminal, the three-dimensional antenna directional diagram and scene information in a preset geographic range can be subdivided by a triangulation grid method, and the configuration information, the three-dimensional antenna directional diagram and the scene information in a preset geographic range are respectively dispersed into triangular basic surface elements in a scene representation module of the ray tracking simulation model, so that the triangular basic surface elements are geometrically fused into a whole; the electromagnetic parameters of the material can be obtained by measurement or actual measurement correction and the like, and are substituted into the corresponding propagation model to finally realize the electromagnetic calculation of various multipaths.
Fig. 6 is a flow chart of a specific embodiment provided by the embodiment of the present invention. As shown in fig. 6, in a specific embodiment, configuration information of a human body posture model associated with a terminal and a three-dimensional antenna directional pattern of the terminal are obtained through density and human body pose probability of the terminal in a simulation area, a three-dimensional electronic map and/or a preset scene model of a preset geographic range and parameter information of a service base station of the terminal are used as input, the input is input into a ray tracing simulation model to calculate to obtain path loss data, and the parameter information, the line loss data, the path loss data and other numerical values of the service base station of the terminal are substituted into a link budget model to calculate to obtain received power of all associated terminals under each service base station, so as to obtain a coverage prediction result.
According to the scheme of the embodiment of the invention, a human body posture model library in a simulation area is obtained; obtaining a three-dimensional antenna directional diagram of the terminal according to the human body posture model library; according to the three-dimensional antenna directional diagram of the terminal, performing simulation prediction on wireless network signals to obtain a coverage prediction result of a wireless network; the problem that the method cannot adapt to a 5G indoor scene mainly based on mobile data services is solved, the influence of a human body on an antenna and multipath propagation is considered in the simulation process, the coverage influence caused by the human body in millimeter wave high-frequency bands can be considered, and the method can be better used for network planning and optimization.
Fig. 7 is a schematic structural diagram illustrating a coverage prediction apparatus of a wireless network according to an embodiment of the present invention. As shown in fig. 7, the apparatus 70 includes:
a first obtaining module 71, configured to obtain a human body posture model associated with a terminal in the simulation area;
a second obtaining module 72, configured to obtain a three-dimensional antenna directional pattern according to the human body posture model;
and the processing module 73 is configured to perform simulation prediction on a wireless network signal according to the three-dimensional antenna directional diagram to obtain a coverage prediction result of the wireless network.
Optionally, obtaining a human body posture model library in the simulation region includes:
and acquiring a human body posture model library according to the density of the terminal and the human body pose probability information in the simulation area.
Optionally, the human body posture model library includes: a body posture model associated with the terminal and a body posture model not associated with the terminal.
Optionally, obtaining a three-dimensional antenna directional pattern according to the human body posture model library includes:
and inputting the human body attitude model library and the antenna directional diagram of the terminal into a finite difference time domain simulation model for processing to obtain the three-dimensional antenna directional diagram.
Optionally, performing simulation prediction on a wireless network signal according to the three-dimensional antenna directional diagram to obtain a coverage prediction result of the wireless network, including:
and performing simulation prediction on wireless network signals according to configuration information of a human body posture model associated with the terminal, the three-dimensional antenna directional diagram, scene information in a preset geographic range and parameter information of a service base station of the terminal to obtain a coverage prediction result of the wireless network.
Optionally, the scene information in the preset geographic range includes: and the three-dimensional electronic map and/or the preset scene model of the preset geographic range.
Optionally, performing simulation prediction on a wireless network signal according to configuration information of a human body posture model associated with the terminal, the three-dimensional antenna directional diagram, scene information within a preset geographic range, and parameter information of a service base station of the terminal, to obtain a coverage prediction result of the wireless network, including:
inputting configuration information of a human body posture model related to a terminal, the three-dimensional antenna directional diagram, scene information in a preset geographic range and parameter information of a service base station of the terminal into a ray tracing simulation model for calculation processing to obtain road loss data;
inputting the path loss data, the transmitting power of the terminal and the line loss data into a link budget model for calculation processing to obtain the receiving power of all associated terminals under the service base station;
and obtaining a coverage prediction result of the wireless network according to the received power.
It should be noted that the apparatus is an apparatus corresponding to the coverage prediction method for the wireless network, and all implementation manners in the method embodiment are applicable to the embodiment of the apparatus, and the same technical effect can be achieved.
An embodiment of the present invention provides a non-volatile computer storage medium, where the computer storage medium stores at least one executable instruction, and the computer executable instruction may execute the coverage prediction method of the wireless network in any method embodiment described above.
Fig. 8 is a schematic structural diagram of a computing device according to an embodiment of the present invention, where the specific embodiment of the present invention does not limit a specific implementation of the computing device.
As shown in fig. 8, 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 the program, and may specifically execute the relevant steps in the coverage prediction method embodiment for the wireless network of the computing device.
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 computing device includes one or more processors, which may 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, such as at least one disk memory.
The program may specifically be configured to cause the processor to perform the coverage prediction method for a wireless network in any of the above-described method embodiments. For specific implementation of each step in the procedure, reference may be made to corresponding steps and corresponding descriptions in units in the embodiment of the coverage prediction method for a wireless network, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
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 is 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 embodiments of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best modes of embodiments of the invention.
In the description provided herein, numerous specific details are set forth. However, it is 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 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 disclosure 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 is, the claimed embodiments of the invention require 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 devices in an embodiment may be adaptively changed and arranged 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. Embodiments of the invention may also be implemented 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 embodiments of the present invention may be stored on computer-readable media 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. Embodiments of the invention may 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 may 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 method for coverage prediction for a wireless network, the method comprising:
acquiring a human body posture model library in a simulation area;
obtaining a three-dimensional antenna directional diagram of the terminal according to the human body posture model library;
and according to the three-dimensional antenna directional diagram of the terminal, performing simulation prediction on wireless network signals to obtain a coverage prediction result of the wireless network.
2. The method of claim 1, wherein obtaining a human body posture model library in a simulation region comprises:
and acquiring a human body posture model library according to the density of the terminal and the human body pose probability information in the simulation area.
3. The method of claim 2, wherein the human pose model library comprises: a body posture model associated with the terminal and a body posture model not associated with the terminal.
4. The method of claim 1, wherein obtaining a three-dimensional antenna pattern from the library of human pose models comprises:
and inputting the human body attitude model library and the antenna directional diagram of the terminal into a finite difference time domain simulation model for processing to obtain the three-dimensional antenna directional diagram.
5. The method of claim 1, wherein the performing simulation prediction of wireless network signals according to the three-dimensional antenna pattern to obtain a coverage prediction result of a wireless network comprises:
and performing simulation prediction on wireless network signals according to configuration information of a human body posture model associated with the terminal, the three-dimensional antenna directional diagram, scene information in a preset geographic range and parameter information of a service base station of the terminal to obtain a coverage prediction result of the wireless network.
6. The method of claim 5, wherein the scene information within the preset geographic range comprises: and the three-dimensional electronic map and/or the preset scene model of the preset geographic range.
7. The method according to claim 5 or 6, wherein the step of performing simulation prediction on wireless network signals according to configuration information of a human body posture model associated with a terminal, the three-dimensional antenna directional diagram, scene information within a preset geographic range, and parameter information of a serving base station of the terminal to obtain a coverage prediction result of the wireless network comprises:
inputting configuration information of a human body posture model related to a terminal, the three-dimensional antenna directional diagram, scene information in a preset geographic range and parameter information of a service base station of the terminal into a ray tracing simulation model for calculation processing to obtain road loss data;
inputting the path loss data, the transmitting power of the terminal and the line loss data into a link budget model for calculation processing to obtain the receiving power of all associated terminals under the service base station;
and obtaining a coverage prediction result of the wireless network according to the received power.
8. An apparatus for coverage prediction for a wireless network, comprising:
the first acquisition module is used for acquiring a human body posture model associated with the terminal in the simulation area;
the second acquisition module is used for acquiring a three-dimensional antenna directional diagram according to the human body posture model;
and the processing module is used for carrying out simulation prediction on wireless network signals according to the three-dimensional antenna directional diagram to obtain a coverage prediction result of the wireless network.
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 coverage prediction method of the wireless network according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the coverage prediction method of a wireless network of any one of claims 1-7.
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