阅读说明：本技术 考虑可移动物体的通信环境分析和网络设计的方法和装置 (Method and apparatus for communication environment analysis and network design considering movable object ) 是由 李淳永 朴成范 蒋尚铉 赵敏成 于 2018-12-14 设计创作，主要内容包括：本公开涉及一种用于将5G通信系统与IoT技术相结合以支持比4G系统更高的数据传输速率的通信技术及其系统。本公开可以应用于基于5G通信和IoT相关技术的智能服务(例如,智能家居、智能建筑、智能城市、智能汽车或联网汽车、医疗保健、数字教育、零售业务、安保和安全性相关服务等)。根据本公开的实施例的用于识别无线通信系统中的无线信号的传输特性方法包括以下步骤：识别信号发送位置；识别信号接收位置；识别信号发送位置与所述信号接收位置之间的可移动物体所在的区域；识别所述区域中的所述可移动物体的特性；以及基于所述可移动物体的特性,识别从所述信号发送位置发送到所述信号接收位置的无线信号的传输特性。(The present disclosure relates to a communication technology for combining a 5G communication system with an IoT technology to support a higher data transmission rate than a 4G system and a system thereof. The present disclosure may be applied to intelligent services based on 5G communication and IoT related technologies (e.g., smart homes, smart buildings, smart cities, smart cars or networked cars, healthcare, digital education, retail business, security and security related services, etc.). The method for identifying transmission characteristics of a wireless signal in a wireless communication system according to an embodiment of the present disclosure includes the steps of: identifying a signal transmission position; identifying a signal receiving location; identifying an area between a signal sending location and the signal receiving location where a movable object is located; identifying a characteristic of the movable object in the region; and identifying a transmission characteristic of a wireless signal transmitted from the signal transmitting location to the signal receiving location based on the characteristic of the movable object.)

1. A method for identifying transmission characteristics of a wireless signal in a wireless communication system, the method comprising:

identifying a signal transmission position;

identifying a signal receiving location;

identifying a region in which a movable object is present between the signal transmitting location and the signal receiving location;

identifying a characteristic of the movable object in the region; and

based on the characteristics of the movable object, transmission characteristics of a wireless signal transmitted from the signal transmitting location to the signal receiving location are identified.

2. The method of claim 1, further comprising:

identifying a propagation characteristic of a wireless signal in the region determined based on a characteristic of the movable object; and

identifying a size of the movable object determined based on a characteristic of the movable object;

wherein the transmission characteristics of the wireless signal are identified by taking into account the presence of an object having the size and the propagation characteristics of the wireless signal in the area.

3. The method of claim 1, wherein the characteristic of the movable object comprises at least one of: size of the movable object, speed of the movable object, material of the movable object, number of movable objects passing over the area per unit time.

4. The method of claim 1, wherein the characteristic of the movable object is obtained based on image information associated with the region, and

wherein the characteristic of the movable object is identified based on congestion information of the movable object.

5. The method of claim 1, further comprising: identifying at least one of a dielectric constant, a reflectivity, a transmissivity, and a diffraction coefficient associated with wireless signal transmission in the region based on the characteristic of the movable object.

6. The method of claim 1, wherein the characteristic of the movable object comprises congestion information due to the movable object over a time interval.

7. The method of claim 1, further comprising:

identifying a virtual three-dimensional object having a size and material determined based on a characteristic of the movable object,

wherein the transmission characteristics of the wireless signal are identified by taking into account the presence of the virtual three-dimensional object in the region.

8. The method of claim 1, further comprising:

determining a signal to transmit to the transmit location based on a characteristic of the movable object; and

determining information to be reported by a receiver located at the receiving location based on a characteristic of the movable object.

9. A computing device for identifying transmission characteristics of wireless signals in a wireless communication system, the computing device comprising:

a transceiver configured to transmit and receive information; and

a controller configured to be connected to the transceiver, identify a signal transmission location, identify a signal reception location, identify a region having a movable object between the signal transmission location and the signal reception location, identify a characteristic of the movable object in the region, and identify a transmission characteristic of a wireless signal transmitted from the signal transmission location to the signal reception location based on the characteristic of the movable object.

10. The computing device of claim 9, wherein the controller is configured to identify a propagation characteristic of a wireless signal in the area determined based on a characteristic of the movable object; and identifying a size of the movable object determined based on the characteristics of the movable object;

wherein the transmission characteristics of the wireless signal are identified by taking into account the presence of an object having the size and the propagation characteristics of the wireless signal in the area.

11. The computing device of claim 9, wherein the characteristic of the movable object comprises at least one of: size of the movable object, speed of the movable object, material of the movable object, number of movable objects passing over the area per unit time.

12. The computing device of claim 9, wherein the characteristic of the movable object is obtained based on image information associated with the region, and

wherein the characteristic of the movable object is identified based on congestion information of the movable object.

13. The computing device of claim 9, wherein the controller is configured to identify at least one of a permittivity, a reflectivity, a transmissivity, and a diffraction coefficient associated with wireless signal transmission in the region based on a characteristic of the movable object.

14. The computing device of claim 9, wherein the characteristic of the movable object includes congestion information due to the movable object over a time interval,

wherein the controller is configured to identify a virtual three-dimensional object having a size and material determined based on a characteristic of the movable object,

wherein the transmission characteristics of the wireless signal are identified by taking into account the presence of the virtual three-dimensional object in the region.

15. The computing device of claim 9, wherein the controller is configured to determine the signal transmitted to the transmit location based on a characteristic of the movable object; and

determining information to be reported by a receiver located at the receiving location based on a characteristic of the movable object.

Technical Field

Embodiments of the present disclosure relate to a method of modeling a frequency communication environment to operate a wireless communication system and operate a network based thereon, and an apparatus using the same. More specifically, embodiments of the present disclosure are directed to providing a method for network operation and an apparatus using the same by considering analysis of wireless signal propagation characteristics of a corresponding region using a movement path and movement characteristics of a movable object in a millimeter wave wireless communication environment and modeling the communication environment based thereon.

Background

In order to meet the increasing demand for wireless data traffic since the deployment of 4G communication systems, efforts have been made to develop an improved 5G or pre-5G communication system, and thus, the 5G or pre-5G communication system is also referred to as an "ultra-4G network" or a "post L TE system". the 5G communication system is considered to be implemented in a higher frequency (millimeter wave) band (e.g., 60GHz band) to achieve a higher data rate.in order to reduce the propagation loss of radio waves and increase the transmission distance, beamforming, massive Multiple Input Multiple Output (MIMO), full-dimensional MIMO (FD-MIMO), array antenna, analog beamforming, large-scale antenna technology are discussed in the 5G communication system.in addition, in the 5G communication system, based on advanced small cells, cloud Radio Access Network (RAN), ultra-dense network, device-to-device (D2D) communication, wireless backhaul, mobile network, cooperative communication, multipoint (CoMP), interference cancellation, etc., in the development of the system, as advanced hybrid coding and sparse coding (fbm) and multi-access coding (fbm) systems, as well as advanced coding and sparse coding (fsc-access) coding.

The internet is a person-centric interconnected network in which people can generate and consume information. Today, the internet is evolving into the internet of things (IoT) in which distributed entities, such as objects, exchange and process information without human intervention. Internet of everything (IoE) has emerged as a means of integrating internet of things technology with big data processing technology through a connection with a cloud server. IoT implementations require technical elements such as "sensing technology", "wired/wireless communication and network infrastructure", "service interface technology", and "security technology", and sensor networks, machine-to-machine (M2M) communication, Machine Type Communication (MTC), etc. have recently been studied. Such an IoT environment can provide intelligent internet technology services that create new value for human life by collecting and analyzing data generated between connected things. Through the convergence and integration between existing Information Technology (IT) and various industrial applications, IoT is applicable in various fields including smart homes, smart buildings, smart cities, smart cars or networked cars, smart grids, healthcare, smart homes, and advanced medical services.

In line with this, various attempts have been made to apply the 5G communication system to the IoT network. For example, technologies such as sensor networks, Machine Type Communication (MTC), and machine-to-machine (M2M) communication may be implemented through beamforming, MIMO, and array antennas. Cloud Radio Access Network (RAN) applications, which are the big data processing technologies described above, can also be seen as an example of the convergence between 5G technologies and IoT technologies.

In the case of the latest communication system, since a relatively high frequency communication signal is used, it is necessary to analyze a wireless communication environment in consideration of a movable object, configure a network based on this and operate the installed network.

Disclosure of Invention

Technical problem

Embodiments of the present disclosure have been made to solve the above-mentioned problems, and are intended to provide a method for operating a wireless communication system using wireless communication environment modeling for operating the wireless communication system in consideration of a movable object, and an apparatus using the same. Further, embodiments of the present disclosure are directed to providing a method of analyzing and modeling propagation characteristics of a wireless signal in consideration of a wireless signal transmitted from a transmitter and an area where a movable object is located in a communication system using the wireless signal and an apparatus based on the method.

Solution to the problem

To solve the technical problem, a method for identifying transmission characteristics of a wireless signal in a wireless communication system according to an embodiment of the present disclosure includes: identifying a signal transmission position; identifying a signal receiving location; identifying an area in which a movable object is located between the signal transmitting location and the signal receiving location; identifying a characteristic of the movable object in the region; and identifying a transmission characteristic of a wireless signal transmitted from the signal transmitting location to the signal receiving location based on the characteristic of the movable object.

A computing device for identifying transmission characteristics of wireless signals in a wireless communication system according to an embodiment of the disclosure, the computing device comprising: a transceiver configured to transmit and receive information; and a controller configured to be connected to the transceiver, identify a signal transmission position, identify a signal reception position, identify a region having a movable object between the signal transmission position and the signal reception position, identify a characteristic of the movable object in the region, and identify a transmission characteristic of a wireless signal transmitted from the signal transmission position to the signal reception position based on the characteristic of the movable object.

Advantageous effects of the invention

According to the embodiments of the present disclosure, propagation characteristics of wireless signals in a wireless communication system may be identified, and more accurate system modeling and more efficient network operation may be performed.

Drawings

FIG. 1 is a diagram for explaining a network design using a mathematical modeling technique;

fig. 2 is a view for explaining a ray tracing simulation method according to an embodiment of the present disclosure;

fig. 3a and 3b are views for explaining a method of obtaining 3D map information according to an embodiment of the present disclosure;

fig. 4a and 4b are views for explaining a method of obtaining material information of an object in an image through image information according to an embodiment of the present disclosure;

fig. 5 is a view for explaining a method of analyzing a communication channel environment through ray tracing according to an embodiment of the present disclosure;

FIG. 6 is a diagram illustrating the effect of a movable object on wireless signal transmission and the resulting signal loss according to an embodiment of the present disclosure;

fig. 7 is a view for explaining a method of performing wireless signal transmission simulation in consideration of a movable object according to an embodiment of the present disclosure;

fig. 8 is a view for explaining a method of performing modeling and simulation in consideration of congestion information according to an embodiment of the present disclosure;

fig. 9 is a view for explaining a method of obtaining movable object information in consideration of traffic information according to an embodiment of the present disclosure;

fig. 10 is a view for explaining a method of determining movable areas on roads and sidewalks and modeling physical coefficients based on congestion information in the corresponding areas according to an embodiment of the present disclosure;

fig. 11 is a view for explaining a method of performing mapping on map information in consideration of congestion of movable areas and objects and analyzing a signal transmission/reception environment based on the mapping according to an embodiment of the present disclosure;

fig. 12 is a view for explaining a method of applying a congestion degree of a movable object over time according to an embodiment of the present disclosure;

fig. 13 is a view for explaining a method of determining the propagation characteristics of a signal based on the congestion degree of each movable object according to an embodiment of the present disclosure;

FIG. 14 is a diagram illustrating a computing device according to an embodiment of the present disclosure; and

fig. 15 is a view for explaining a base station according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

In describing the embodiments of the present disclosure, descriptions related to technical contents well known in the art and not directly related to the present disclosure will be omitted. Such omission of unnecessary description is intended to prevent the main concepts of the present disclosure from being obscured, and to more clearly convey the main concepts.

For the same reason, in the drawings, some elements may be enlarged, omitted, or schematically shown. Further, the size of each element does not completely reflect the actual size. In the drawings, the same or corresponding elements have the same reference numerals.

Advantages and features of the present disclosure and the manner of attaining them will become apparent by reference to the following detailed description of embodiments when taken in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments set forth below, but may be implemented in various different forms. The following examples are provided only for complete disclosure and to inform those skilled in the art of the scope of the disclosure, and the disclosure is limited only by the scope of the appended claims. Throughout the specification, the same or similar reference numerals denote the same or similar elements.

In this context, it will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions may also be stored in a computer usable or computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer usable or computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

Furthermore, each block of the flowchart illustrations may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

As used herein, a "unit" refers to a software element or a hardware element, such as a Field Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC), which performs a predetermined function. However, the "unit" does not always have a meaning limited to software or hardware. The "unit" may be configured to be stored in an addressable storage medium or to execute one or more processors. Thus, a "unit" includes, for example, software elements, object-oriented software elements, class elements or task elements, procedures, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and parameters. The elements and functions provided by a "unit" may be combined into a smaller number of elements or "units" or divided into a larger number of elements or "units". Further, these elements and "units" may be implemented as one or more CPUs within a rendering device or secure multimedia card.

In the drawings describing the methods according to the embodiments, the order described does not always correspond to the order in which the steps of each method are performed, and the order relationship between the steps may be changed or the steps may be performed in parallel. Further, it is apparent that steps mentioned as unnecessary in the embodiments may be selectively performed.

Fig. 1 is a view for explaining a network design using a mathematical modeling technique.

Referring to fig. 1, a transmitter 110 and a transmitter 120 may form a transmission beam 112 and a beam 122, respectively, to transmit signals.

In this way, the mathematical modeling technique can predict the RF information by inputting the frequency and distance of the transmission signal through a function explicitly expressed through a specific signal transmission/reception modeling technique. As shown in fig. 1, the transmitter 110 and the transmitter 120 may form the beam 112 and the beam 122 in three directions, respectively, and thus, the RF characteristics of the transmission signal may be applied through a modeling technique. Thus, with mathematical modeling techniques, RF information can be predicted with less computation, but methods for making accurate measurements at higher frequencies are needed.

Fig. 2 is a view for explaining a ray tracing simulation method according to an embodiment of the present disclosure.

Referring to fig. 2, it is assumed that one or more transmitters 212, 214, and 216 transmit signals, and thus, the strength at which the signals transmitted by each transmitter 212, 214, and 216 are received is indicated by gray shading on the map. Darker colors indicate regions with stronger received strength and lighter colors indicate regions with weaker signal strength.

More specifically, assuming the location of the receiver 220, the reception strength of the signal in the corresponding area can be determined. In addition, the transmission channel for each possible path from one transmitter 212 to the receiver 220 may be determined. There may be a signal 242 transmitted directly from the transmitter 212 to the receiver 220 and there may also be a signal 232 received by reflection from another object 230. When the simulation according to the ray tracing is performed as described above, information on the strength of the signals received from the transmitters 212, 214, and 216 in a specific area and the transmission path of the respective signals can be obtained. When the signal reception strength is determined according to the transmission path of the signal, the receiver 220 can obtain more accurate signal reception information in consideration of at least one of the external shapes of the surface material and the reflective object. Although referred to as surface material in the embodiment, this does not mean only the outer surface of the object, and conceptually includes an inner material that can affect the reflection of radio waves, and the characteristics of the radio wave reflection can be estimated more accurately by this information.

Further, there may be an obstacle capable of transmitting a radio wave on a path directly transmitting a signal. As an example of such an obstacle, a tree may be given, and an obstacle other than the tree, in which signal attenuation may occur while transmitting radio waves, may be considered in the ray tracing simulation. In this way, by considering information about obstacles capable of transmitting radio waves, more accurate simulation results can be obtained. As an example of the obstacle, the tree may be another plant or object that is installed on the communication path and causes signal attenuation when transmitting radio waves, and may include other objects that may cause signal attenuation.

In this way, it is possible to determine at least one of the optimal transmitter position and receiver position on the map by performing ray tracing as described above. Also, according to the embodiment, ray tracing simulation may be performed in consideration of a plurality of transmitter position candidates and receiver position candidates, and at least one of the transmitter position and the receiver position may be determined according to a result of ray tracing.

As described above, ray tracing analog techniques may determine a transmission channel for each path through which an RF signal passes, and may predict RF signal information based on the location of the transmission channel at receiver 220. In an embodiment, the ray tracing simulation technique calculates at least one of an environment (e.g., a type of a medium), 3D terrain, reflection and diffraction of a building, and a distance over which a signal is transmitted in determining a channel environment from a signal path, thereby more accurately predicting RF signal information. Further, the channel estimation method using the above-described technique according to the frequency of the RF signal is not limited, the actual environment can be accurately reflected, and at least one of the optimal transmission position and reception position can be determined based on the simulation result.

In addition, the 5G network uses ultra high frequency signals of 28GHz to 60 GHz. Therefore, in order to find information about wireless signals in a 5G network design tool, ray tracing simulation techniques may be used to improve accuracy, rather than mathematical modeling techniques. In the example of ray tracing simulation, when predicting the path of a radio wave striking a building and reflecting, the reflection can be calculated by assuming that the surfaces of all buildings have the same RF characteristics. However, since the reflectivity of the RF signal varies according to the surface material, the external shape, and the pattern of the reflection surface, this assumption cannot guarantee an accurate simulation result.

Fig. 3a and 3b are views for explaining a method for obtaining 3D map information according to an embodiment of the present disclosure.

Referring to fig. 3a and 3b, three-dimensional map information may be obtained based on the actual image information of fig. 3a and the position information corresponding to the image information. More specifically, the 3D map information of fig. 3b may be obtained based on the image information to perform ray tracing simulation.

The map information obtained in fig. 3b may include a building 310, a receiver candidate area 330, and a tree 320. As described above, by obtaining map information, transmitter position candidates and receiver position candidates may be determined based on the map information, and accordingly, ray tracing simulations may be performed to determine at least one of an optimal transmitter position and receiver position.

Further, in this embodiment, an element such as the building 310 may have a characteristic of reflecting or scattering radio waves, and in the case of such an element, more accurate simulation results can be obtained by considering the surface material and the external shape.

Further, in this embodiment, the tree 320 may transmit radio waves, but the transmitted radio waves may experience greater signal attenuation than in the air. In this way, more accurate simulation results may be obtained by considering propagation characteristics through objects such as tree 320.

Further, in this embodiment, the receiver candidate region 330 may be selectively performed according to ray tracing, and the receiver candidate region 330 may include a region in which a fixed or movable receiver may be installed. More specifically, the receiver may be installed in a window area of the building 310, and thereby the receiver installed in the window area may play a role of a relay in communication between another receiver inside the building and a transmitter outside the building. As described above, ray tracing simulation may be performed in consideration of the receiver candidate region 330 in order to obtain a result value in consideration of a better signal reception environment.

Fig. 4a and 4b are views for explaining a method of obtaining material information of an object in an image through image information according to an embodiment of the present disclosure.

Referring to fig. 4a and 4b, the material of the object displayed inside the image may be determined from the image information. More specifically, from the image information, the material of objects inside the image may be determined based on computer vision techniques based on depth learning. The functionality associated with more specific deep learning based computer vision techniques will be described later.

In an embodiment, the image information of fig. 4a may be analyzed to obtain the results of fig. 4 b. At this time, each element may be determined based on at least one of color, contrast, reflectivity, mutual positional relationship between each element, and arrangement of all components of the image. In an embodiment, the materials of asphalt 410, concrete 420, vegetation 430, steel structure 440, vehicle 450, sky 460, glass 470, etc. may be determined by image analysis. As described above, by determining the material of the elements displayed in the image through the image information and considering the characteristics of the material in the ray tracing simulation, more accurate results can be obtained.

Fig. 5 is a view for explaining a method of analyzing a communication channel environment through ray tracing according to an embodiment of the present disclosure.

Referring to fig. 5, a method for performing ray tracing simulations is disclosed. In an embodiment, the ray tracing simulation may be performed in a computing device including a controller. The computing device may be a computing device that includes a central control processor and may include a personal computer, workstation, or the like. In the following embodiments, it may be assumed that the simulation is performed by a computing device.

In operation 510, the computing device may obtain 2D image information and 3D map information. In an embodiment, the 2D image information may include additional information corresponding to the image, and the additional information may include information on a position where the image is photographed, direction information, and photographing information including an angle of view. The 3D map information corresponding to the 2D image information may be determined based on the additional information. Further, the 3D map information may include position information and 3D map information corresponding thereto. According to an embodiment, such information may comprise information about the shape of buildings, structures and plants on the surface or water surface, and may comprise information about at least one of the transmitter candidate location and the receiver candidate location.

In operation 520, the computing device may obtain information about the actual environment based on at least one of the map information and the image information. The information about the actual environment may include information about an object located on the communication path and characteristics of the object. More specifically, by analyzing the 2D image information, characteristics of objects that may be located on the communication path may be determined based on the analysis. The characteristic of the object may include at least one of a material of a surface of the object and an external shape of the object, and in the case where the object is capable of transmitting radio waves, information about a shape of the object and a degree of signal attenuation during transmission may be included.

In operation 530, the computing device may map the actual environment information on the communication path to 3D map information based on the information obtained in steps 510 and 520. As described above, when mapped to 3D map information, additional information obtained through the 2D image information may be mapped to an object corresponding to the 3D map information based on the additional information included in the 2D image information.

At operation 540, the computing device may perform ray tracing simulations based on the information generated by operation 530. In an embodiment, the ray tracing simulation may sequentially perform ray tracing simulations corresponding to beam information while changing the beam information in consideration of beams in a specific direction, or may assume that beams of all directions, which may be transmitted from the transmitter, are transmitted within the same period of time and perform ray tracing simulations corresponding to the assumption. As a result of performing ray tracing simulations, the signal quality that may be received at the receiver may be predicted and analyzed by considering the path through which the signal transmitted from the transmitter was received by the receiver, as well as information about the actual environment along the path. Further, in an embodiment, when performing the ray tracing simulation, at least one of the transmission position and the reception position may be determined based on the 3D map information, and the signal transmission environment may be determined based on the information mapped in step 530.

At operation 550, a result value may be obtained based on the ray tracing simulation, and additional ray tracing simulations may be performed based on the obtained result value and a value measured in an actual environment. More specifically, when the simulation result value is compared with the real environment measurement value and the compared value is different, the simulation result value may be regenerated by changing the information obtained in operation 520 based on the actual environment measurement value. In this way, ray tracing simulation is performed by reflecting information on an actual environment on a 3D map, thereby realizing more reliable communication channel analysis. More specifically, the transmitter and the receiver may be directly installed in the target area for the ray tracing simulation, and the basic information for performing the ray tracing simulation may be added or updated based on the result value of the signal transmitted from the transmitter.

As described above, based on the ray tracing simulation results, at least one of an optimal transmitter position and receiver position for providing wireless service to a specific area in the map may be determined. In this way, efficient network design may be performed by determining at least one of an optimal transmitter position and receiver position. More specifically, an optimal base station position at which wireless signals can be efficiently provided to wireless terminals in a particular area can be determined. By determining the best base station location in this manner, efficient service can be provided even if fewer base stations are provided.

Furthermore, by reflecting signal reception information measured in an actual environment, adaptive network management is possible. More specifically, after the transmitter is installed, if the surrounding environment changes, additional ray tracing simulation may be performed in consideration of the changed environment, and network management such as adjusting the transmitter position by additionally reflecting the result value may be performed. Further, such network management may include changing beam-related information transmitted from the transmitter, in addition to adjusting the transmitter position. More specifically, the transmitter may determine the transmit beam and the receive beam based on the ray tracing simulation result value. To determine the transmission beam and the reception beam, beam alignment may be performed based on the result value of the ray tracing simulation. Such adaptive network management may be performed periodically.

Fig. 6 is a view for explaining an influence of a movable object on wireless signal transmission and a resulting signal loss according to an embodiment of the present disclosure.

Referring to fig. 6, a road 630 and a sidewalk 640 may be located on a path along which a wireless signal transmitted from a transmitter 610 is transmitted to a receiving location 640. In this case, a movable object such as the vehicle 635 or the pedestrian 645 may be located in a transmission path of the wireless signal, and thus signal loss may occur. At this time, as the frequency of the wireless signal transmitted from the transmitter 610 increases, the signal loss may increase depending on the kind of the object, and thus it may be necessary to determine the signal transmission environment in consideration thereof.

In the embodiment, such moving objects may frequently occur in the case of the road 630 and the sidewalk 640, and thus, in practice, in order to determine the signal transmission environment between the transmitter 610 and the receiving location 640, it is necessary to consider the objects moving on the areas such as the road 630 and the sidewalk 640.

In the case of the lower graph 650, it shows the loss of signal transmitted from the transmitter 610 to the receiving location 640 as the object passes over time. In an embodiment, where an object passes through interval 655, signal loss may increase, and when a large object passes, a greater signal loss may occur.

At this time, it is necessary to identify the characteristics of the movable object that may be located in a specific area on the wireless signal transmission path. More specifically, an area where an object may be located on a transmission path may be determined, and a wireless signal transmission environment may be modeled in consideration of the size, frequency, speed, movement pattern, and material of the object passing through the area.

Fig. 7 is a view for explaining a method of performing wireless signal transmission simulation in consideration of a movable object according to an embodiment of the present disclosure.

Referring to fig. 7, a computing device may acquire information related to signal transmission and perform simulation based on the information, and when a transmission device is to be installed, a communication system may be controlled based on the simulation result.

At operation 705, the computing device may determine a location of the transmitter and a location of the receiver, and may obtain information about an area in which a movable object located on a path along which the signal may be transmitted may be present. Such information may be obtained through an external database or user input, may also be obtained based on map information, and may identify areas where movable objects may be present based on images such as satellite photographs and street views. As described above, in the case where the region of the movable object is determined based on the image information, the region where the movable object exists can be identified by using the image analysis method of machine learning.

At operation 710, the computing device may identify features of objects that are movable in the corresponding region. More specifically, at least one of size information of the moving object, frequency information of the moving object, speed information of the moving object, motion pattern information at the time of periodic motion, and material information of the moving object may be recognized. Further, information regarding characteristics of the movable object that may affect wireless signal transmission may be identified. This information may also be obtained through user input, external databases, and image or video analysis. More specifically, a characteristic of the movable object in the corresponding region may be identified over time, and the characteristic may be obtained based thereon.

At operation 715, the computing device may model an environment in which signals are transmitted from the transmitter to the receiver based on the obtained information. At this time, in the case of a region where the movable object can be located, modeling can be performed assuming that an object having a physical quantity determined based on information obtained in the corresponding region is located in the region. Such a physical quantity may be determined based on the characteristics of the movable object that may be located in three dimensions, and it may be determined that there are a plurality of objects having different physical quantities according to the characteristics of the movable object in one area. For example, in the case of a road on which a vehicle travels, when an object having a specific physical value is located in an area occupied by the traveling vehicle, modeling may be performed based on the average size of passing vehicles. The larger the average size of the vehicle, the larger the size of the object, and if the vehicle moves more frequently, the modeled signal loss may have a larger physical value.

Based on the information modeled in operation 720, simulations related to wireless signal transmission may be performed. Based on the simulation results, the computing device may more accurately analyze the wireless signal transmission pattern in view of the movable object. Further, based on the simulation result, the transmitter may transmit a signal based on the simulation result when the communication system is operated in the future.

Fig. 8 is a view for explaining a method of performing modeling and simulation in consideration of congestion information according to an embodiment of the present disclosure.

Referring to fig. 8, a computing device may perform modeling of an area in which a movable object is located based on congestion information and may perform ray tracing based thereon to analyze wireless signal propagation patterns or network operations by adjusting beam strength and direction. In embodiments, the computing device performing the simulation and the computing device performing the network operations may be the same computing device or different computing devices.

At operation 805, the computing device may identify an area in which the movable object may be located. More specifically, information about an area in which the movable object may be located, such as a road or a sidewalk, may be identified, and such information may be identified by analyzing user input or image information. More specifically, the position, size, and the like of an area in which a movable object such as a road or a sidewalk may be located may be identified based on image information corresponding to map information.

At operation 810, the computing device may identify available congestion information. More specifically, congestion information that may be used for the identified area may be identified. Such congestion information may be identified by separate data or may be connected to another system or server to receive data related thereto.

If the available congestion information is a congestion map, the computing device may model physical coefficients related to the wireless signal propagation based on congestion related information obtained from the congestion map at operation 815. In an embodiment, such a congestion map may be a map indicating traffic changes and may comprise a map indicating changes in the number of users of the cellular network in the access area. At this time, information on the size of the moving object may be analyzed by the above image information, and when it is detected that the moving speed is faster than the normal walking speed, it may be assumed that it is a vehicle that is traveling, and physical coefficient modeling may be performed based thereon.

If the available information is real-time traffic/pedestrian information, the computing device may estimate a degree of congestion based on the information at operation 820. More specifically, such information may include at least one of image information and network access information obtained through CCTV. When image information is obtained by CCTV, the computing device may estimate a degree of congestion and a temporal variation based on the image information. Further, in the case of the network access information, the congestion degree may be estimated based on information on the number of network accesses and speed information of the user accessing the network.

At operation 825, the computing device may perform physical coefficient modeling based on the predicted congestion.

If no congestion information is available, physical coefficient modeling may be performed based on user input, as in operation 830. Performing physical coefficient modeling based on the user input may include modeling an entire region in which the movable object is located with the same physical coefficients. At this time, the same physical coefficient may include determining the physical coefficient as an average value based on the measurement information of the plurality of areas.

At operation 835, the computing device may model the road/sidewalk as a 3D object based on the information. In modeling, the size of the area and the physical coefficient relating to radio wave transmission can be determined from the information obtained above.

At operation 840, the computing device may perform ray tracing based on the modeled information to analyze wireless signal propagation patterns.

At operation 845, the computing device may change network operations according to the modeled roads/sidewalks. More specifically, in embodiments it may be performed by a computing device included in the base station, which is the network operation. At this time, if there are many movable objects in the area, based on the real-time traffic/pedestrian information, the state of the wireless channel in the area can be expected to be not good, and the beam intensity, direction, and the like can be adjusted based on this. Further, when reporting the channel of the terminal, it may be configured to report at least one of precoding matrix information and channel quality information of a wider area. Such a configuration may be configured as a higher layer signal such as RRC.

In an embodiment, the congestion degree may comprise information about the number of movable objects passing on the road/sidewalk per unit time.

Fig. 9 is a view for explaining a method of obtaining movable object information in consideration of traffic information according to an embodiment of the present disclosure.

Referring to fig. 9, an example of a congestion map according to traffic information is shown. More specifically, road information may be identified on map 920. Such road information may be identified based on information additionally included in the map information, or may be identified based on separate image information. In this road information, the current average speed, the corresponding speed, and the required time distance may be displayed for each section, and thus, the density of the movable object in the corresponding region may be identified. As described above, the 3D object in the corresponding region for analyzing the wireless signal propagation mode may be modeled in consideration of the density and velocity of the movable object.

In an embodiment, the wireless signal propagation mode may be analyzed based on the modeling result, or a network operation may be performed. In performing the modeling, physical coefficients associated with the propagation of the wireless signal in the region may be modeled. When operating the network at the base station, the beam information may be adjusted in real time using the congestion information, or the channel-related information reported to the terminal may be set.

Fig. 10 is a view for explaining a method of determining movable areas on roads and sidewalks and modeling physical coefficients based on congestion information in the respective areas according to an embodiment of the present disclosure.

Referring to fig. 10, roads and sidewalks may be located in corresponding map areas. In an embodiment, a method of performing modeling based on roads 1015 and sidewalks 1020 to analyze wireless signal propagation patterns is shown.

The wireless signal propagation pattern may be modeled based on information of movable objects on road 1015 and pavement 1020 in the roads and pavements of an embodiment. Further, information of the movable object may be obtained based on the information described in the previous embodiments.

In the case of the road 1015, the characteristics of the vehicle traffic may vary according to the traffic direction. More specifically, characteristics of vehicular traffic may be determined based on the direction of movement. Further, the size of the 3D object may be determined based primarily on the average size of the moving vehicle, but the disclosure is not limited thereto. In an embodiment, the physical quantity may be determined based on the moving characteristics of the vehicle in the upward direction on the road 1015. More specifically, at least one of the dielectric constant, the reflectivity, and the diffraction coefficient in the upward direction may be determined as a1, b1, and c1, respectively, and based on the above information, it may be modeled as a 3D object made of a material determined based on the dielectric constant, the reflectivity, and the diffraction coefficient, the size of which is determined based on the size of a vehicle passing on a road in the downward direction of the road 1015. Similarly, at least one of the dielectric constant, the reflectivity, and the diffraction coefficient in the upward direction may be determined as a2, b2, and c2, respectively, and based on the above information, it may be modeled as a 3D object made of a material determined based on the dielectric constant, the reflectivity, and the diffraction coefficient, the size of which is determined based on the size of a vehicle passing on the road in the downward direction of the road 1015. In an embodiment, transmittance may be further considered for modeling.

Further, in an embodiment, based on characteristics of pedestrians where pedestrians may be located, it may be determined that a corresponding object is located on the sidewalk 1020. More specifically, the corresponding physical quantity may be determined on the sidewalk 1020 based on the moving characteristics of the pedestrian. More specifically, on the sidewalk 1020, at least one of a dielectric constant, a reflectivity, and a diffraction coefficient may be determined as a3, b3, and c3, respectively, based on the motion characteristics of the passing object, and based on the above information, it may be modeled as a 3D object made of a material determined based on the dielectric constant, the reflectivity, and the diffraction coefficient, the size of which is determined based on the average size of pedestrians passing through the sidewalk 1020.

This result can be explained in more detail in conjunction with reference numerals 1050 and 1060. More specifically, based on the motion characteristics of the vehicle and the pedestrian on reference numeral 1050, in the case of a road, an object such as reference numeral 1065 may be modeled as an object being located there, and in the case of a sidewalk, an object such as reference numeral 1070 may be modeled as an object being located there. In the case of reference numeral 1065, it can be modeled as an object having different materials and sizes according to each passing direction.

As described above, it is possible to more accurately analyze the propagation characteristics of the wireless signal by assuming that a 3D object having a specific physical quantity is located in a corresponding region and model the propagation characteristics of the wireless signal based on the characteristics, instead of considering the movable object one by one.

Fig. 11 is a view for explaining a method of performing mapping on map information in consideration of congestion of movable areas and objects and analyzing a signal transmission/reception environment based on the mapping according to an embodiment of the present disclosure.

In an embodiment, at least one road or sidewalk may be identified on a map. In an embodiment, a first road 1112 and a second road 1114 may be identified. Further, a congestion degree of the corresponding road may be identified based on at least one of the embodiments of the present disclosure.

In an embodiment, as shown by reference numeral 1120, at least one change pattern of dielectric constant, diffraction coefficient, and transmittance according to the degree of congestion may be identified. Such a pattern of variation may be identified based on a plurality of measurements. In an embodiment, the dielectric constant and the diffraction coefficient increase with an increase in the degree of congestion, and the transmittance decreases with an increase in the degree of congestion.

In an embodiment, by mapping to reference numeral 1120 (or more precisely reference numerals 1122 and 1124) based on the congestion value of each road 1112 and 1114, at least one of the dielectric constant, the diffraction coefficient, and the transmittance may be mapped based on the congestion of the corresponding road. In an embodiment, the relationship between the dielectric constant, the diffraction coefficient and the transmittance may vary according to the characteristics of the object moving in the respective regions. For example, a road on which a vehicle travels and a sidewalk through which a pedestrian passes may have a relationship between a dielectric constant, a diffraction coefficient, and a transmittance according to different congestion degrees.

As indicated by reference numeral 1130, the dielectric constant, diffraction coefficient, and transmittance of the corresponding area may be identified based on the degree of congestion of the road and the sidewalk, and the radio wave propagation mode may be determined based thereon.

Fig. 12 is a view for explaining a method of applying a congestion degree of a movable object over time according to an embodiment of the present disclosure.

Referring to fig. 12, taking a vehicle as an example, a temporal change in average speed, traffic volume, and size of passing vehicles may be identified, and based on this, an environment according to the temporal change may be identified, and a network may be operated according to the changed environment. Although beam forming is exemplified in the present embodiment, it is considered that objects of different materials and sizes are located in corresponding areas at different times, and therefore, network operation can also be performed based on this.

At reference numeral 1205, time-varying traffic information can be identified from a device (such as a CCTV) or database that can obtain time-varying image information on a particular road.

Based on this, as shown at reference numeral 1210, changes in average speed, traffic volume, and size of passing vehicles may be identified. The degree of congestion may be identified based on at least one of an average speed, a volume of traffic, and a temporal change in size of passing vehicles. The lower the average speed, the greater the degree of congestion, the greater the amount of traffic, the greater the degree of congestion, and the greater the average vehicle size, the greater the degree of congestion. Further, the average vehicle size may be used as information for determining the size of the object assumed to be located in the corresponding area.

According to an embodiment, as shown by reference numeral 1215, a degree of congestion may be determined based on the identified information according to time, and the degree of congestion may be quantified. According to the embodiment, different congestion value may be applied at a time based on the temporal change of the congestion degree that is not quantized, but the amount of calculation may also be reduced by quantizing the congestion degree.

As indicated by reference numeral 1220, at least one of a dielectric constant, a transmittance, and a diffraction coefficient may be determined based on the quantified degree of congestion. This may determine a physical coefficient corresponding to each degree of congestion based on the plurality of measurements, and may determine a physical coefficient corresponding to the material of the object assumed to be located in the respective area based on the determined degree of congestion.

As shown by reference numeral 1225, even if the corresponding physical coefficient changes with time, the physical coefficient can be mapped.

As indicated by reference numeral 1230, network operations may be performed based on the variably mapped physical coefficients. More specifically, the network operation may be performed based on the variation by referring to the physical coefficient value according to the degree of congestion of the corresponding area, which may include different beamforming methods.

Fig. 13 is a view for explaining a method of determining a propagation characteristic of a signal based on a congestion degree of each movable object according to an embodiment of the present disclosure.

Referring to fig. 13, a method of differently using a signal transmission method of a base station based on congestion degrees of roads is shown.

Transmitter 1305 and transmitter 1355 may transmit signals to receive location 1315 and receive location 1365. At this time, the signal may be transmitted in consideration of the transmission path based on the congestion degrees of the links 1320 and 1370. More specifically, if a road is congested, a signal may be transmitted in consideration of a path closest to a straight-line distance. As an example, a beamforming factor may be determined to facilitate signal transmission in a respective direction. When the road is not congested, signal transmission may be performed in consideration of a direct path and a path due to reflection. As described above, spatial multiplexing can be more efficiently performed by changing the network operation method based on the congestion degree of a road.

Fig. 14 is a view for explaining a computing device according to an embodiment of the present disclosure.

Fig. 14 is a diagram illustrating a computing device, according to an embodiment of the present disclosure.

Referring to fig. 14, the computing device 1400 of this embodiment includes an input unit 1410, a memory 1415, and a controller 1420.

The transceiver 1410 may transmit/receive signals with devices external to the computing device 1400. More specifically, data can be transmitted/received with an external device, and an interface unit for this purpose can be included.

The memory 1415 may store at least one of information related to the computing device 1400 and information transmitted/received through the transceiver 1410. In addition, the memory 1415 may store general information necessary for simulation in the embodiment of the present disclosure, for example, information on the object surface material and the external shape according to image analysis, 3D map information, and information on the object surface material and the external shape mapped thereto. Further, according to an embodiment, the memory 1415 may store information on the moving object and the movement characteristics of the object in an area where the movable object may be located. Further, the information stored in the memory 1415 may be added, deleted, and updated based on at least one of the simulation result and the comparison result.

The controller 1420 may control the operation of the computing device 1400 and may perform overall control of the computing device to perform operations related to the operating device described in the above embodiments. The controller 1420 may include at least one processor. Also, the processor may be controlled by a program including instructions for performing the method described in the embodiments of the present disclosure. Further, the program may be stored in a storage medium, and the storage medium may include a volatile memory or a nonvolatile memory. The memory may be a medium capable of storing data, and is not limited in form as long as it can store instructions.

Fig. 15 is a view for explaining a base station according to an embodiment of the present disclosure.

Referring to fig. 15, a base station 1500 of this embodiment includes a transceiver 1510, a memory 1515, and a controller 1520.

The transceiver 1510 may transmit/receive signals with a terminal and other network entities.

Memory 1515 can store at least one of information related to base station 1500 and information transmitted and received via transceiver 1510. Further, as a result of the simulation according to the present embodiment, information relating to the degree of congestion, which varies with time in the area where the movable object can be located and the corresponding area, may be stored. In embodiments, the configuration of memory 1515 may not be necessary.

The controller 1520 may control the operation of the base station 1500 and may control the entire base station to perform the operations related to the base station described in the above embodiments. The controller 1520 may include at least one processor.

Although exemplary embodiments of the present disclosure have been described and illustrated in the specification and drawings by using specific terms, they are used in a general sense only to easily explain technical contents of the present disclosure and to assist understanding of the present disclosure, and are not intended to limit the scope of the present disclosure. It will be apparent to those skilled in the art that other modifications besides the embodiments disclosed herein can be implemented based on the technical ideas of the present disclosure.