CN113873532A - Intelligent park 5G network planning method - Google Patents
Intelligent park 5G network planning method Download PDFInfo
- Publication number
- CN113873532A CN113873532A CN202111026663.7A CN202111026663A CN113873532A CN 113873532 A CN113873532 A CN 113873532A CN 202111026663 A CN202111026663 A CN 202111026663A CN 113873532 A CN113873532 A CN 113873532A
- Authority
- CN
- China
- Prior art keywords
- coverage
- mdt
- image
- intelligent
- park
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000004088 simulation Methods 0.000 claims abstract description 17
- 238000009826 distribution Methods 0.000 claims abstract description 7
- 238000010295 mobile communication Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 22
- 238000004519 manufacturing process Methods 0.000 claims description 19
- 238000005259 measurement Methods 0.000 claims description 17
- 238000007726 management method Methods 0.000 claims description 14
- 238000011156 evaluation Methods 0.000 claims description 11
- 238000013527 convolutional neural network Methods 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 6
- 238000005562 fading Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 5
- 238000013135 deep learning Methods 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 239000002131 composite material Substances 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000035515 penetration Effects 0.000 claims description 3
- 230000035945 sensitivity Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims 1
- 230000007613 environmental effect Effects 0.000 claims 1
- 238000010276 construction Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 5
- 238000013507 mapping Methods 0.000 description 4
- 238000012827 research and development Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003190 augmentative effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
- H04W16/20—Network planning tools for indoor coverage or short range network deployment
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
- H04W16/225—Traffic simulation tools or models for indoor or short range network
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention provides a 5G network planning method for an intelligent park, which comprises the following steps: the method comprises the steps of obtaining mobile communication information, obtaining the planning requirement of the intelligent park, wherein the requirement comprises automatic matching and field collection, the automatic matching is realized by obtaining an environment image of the intelligent park and generating a three-dimensional map layer, identifying an environment scene in an MDT grid, and the matching is carried out to obtain the 5G service requirement of a planning area. And evaluating MDT data of the intelligent park to obtain the coverage and user distribution conditions of the intelligent park, predicting the coverage and service conditions of the intelligent park based on a link budget and a propagation model, generating a 5G coverage simulation layer, and formulating a 5G planning scheme.
Description
Technical Field
The invention belongs to the field of 5G communication network industry planning, and particularly relates to a 5G network planning method for an intelligent park.
Background
The wisdom garden is the important guarantee that promotes the digital transformation of enterprise, can be with the digitization of garden scene, intellectuality, improves garden operation, management, production efficiency and personnel work efficiency, realizes that garden overall value promotes, is just facing the rapid development opportunity period now. The biggest value of 5G is in the ToB market, the big bandwidth of going upward that the 5G network possessed, low time delay, wide connection, high reliability characteristics, the demand of all kinds of businesses in satisfying the garden that can be fine, the end-to-end ability of 5G application from terminal, network, platform to the wisdom garden can be realized in the integration of 5G and wisdom garden, 5G is towards the application scene of garden and is included 5G + high definition video monitoring, 5G + cloudization AGV, 5G + patrols and examines the robot, 5G + AR auxiliary operation, aspects such as 5G + cloudization official working.
The 5G network planning scheme facing the public field is relatively mature, but the network planning scheme of 5G in the industrial application is still in an exploration and development stage, at present, the network planning of the 5G + smart park by documents is concentrated on the concept and target level, or 5G is analyzed from the 5G network slicing and edge computing technical level and is suitable for some applications of the smart park, and the network planning scheme of 5G in the smart park is systematically explained by few documents.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the technical problems in the background technology, the invention provides a 5G network planning method for an intelligent park.
Has the advantages that: the method combines the garden space layout with the business requirements, establishes a space network requirement model, combines the characteristics of a 5G network, utilizes the existing resources for evaluation, and provides a 5G accurate plan.
Drawings
The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a diagram of the LTE parameters and MDT information of the smart campus.
Fig. 3 is a three-dimensional map layer structure diagram of the planning region.
FIG. 4 is a diagram of a 5G simulation layer and a planning scheme.
FIG. 5 is a diagram of a smart campus level block plan.
Detailed Description
The invention provides a 5G network planning method for an intelligent park, which comprises the following steps:
the method comprises the steps of obtaining mobile communication information, wherein the mobile communication information comprises current network LTE engineering parameter information (LTE, Long Term Evolution, commonly known as 4G) and MDT information (Minimization of Drive Test, MDT for short). The current network LTE working parameter information comprises the existing LTE planning station position, sector name, antenna azimuth angle, antenna height, antenna lower section angle and frequency band of the smart park.
At present, an LTE network is in a mature stage, the construction scale meets the requirements of daily users, the coverage rate reaches more than 95%, and the distance between LTE macro stations is 200-400 meters in an urban area. The 5G network is in the initial construction stage, the network construction scale is far lower than that of the LTE network, and the current 5G network mainly has a plurality of limited factors including frequency resources, low terminal occupancy and high construction cost, wherein in the site selection of the newly-constructed 5G station, the current construction mode is mainly to be newly constructed at the same site as the LTE network, namely the LTE and the 5G are at the same position, so that the LTE network is an important reference in the 5G planning construction process.
The operating parameters of the base station are the main influence factors of coverage, wherein the position of the base station, and the antenna heights, azimuth angles and downtilt angles of multiple sectors of the base station all affect the coverage of the sector. In addition, the largest difference between LTE and 5G also includes frequency bands, currently, the 5G mainly adopts a 2.6G frequency band, and the coverage capability is inferior to that of an LTE network compared with D frequency, F frequency and FDD frequency adopted by LTE.
In order to reduce the cost and complexity of manual network Drive tests by mobile operators using dedicated equipment, the LTE system starts from Rel-10 edition and introduces a series of Minimization of Drive Test (MDT) functions. The MDT automatically collects a measurement result related to a network by using a general LTE terminal, And then reports the measurement result to the eNB through a control plane (ControlPlane) signaling, And further reports the measurement result to a Trace Collection Entity (TCE) of an Operation And Maintenance system (Operation And Maintenance, OAM) through an upstream ground interface of the eNB, so as to adjust And optimize network deployment And Operation parameters.
According to different attributes of an MDT measurement object, the measurement content of the current LTE MDT can be classified into 3 types or 3 layers: l1: for example, for statistical measurement of the intensity RSRP and quality RSRQ of the LTE downlink pilot Signal (CRS, CSI-RS), L2: for example, for statistical measurement of packet delay/packet loss rate/packet loss amount of protocol layer data such as LTE MAC/RLC/PDCP, L3: such as statistical measurements of Data throughput/throughput and other mobility-related performance (handover, call drop, etc.) indicators for LTE-specific Data Radio bearers (DRBs for short). The accuracy of the results of the 3-layer/3-type MDT measurement may be affected by local interference from the terminal within a certain period of time.
The MDT is based on measurement information of an LTE network sub-grid, and the measurement information comprises measurement time, a main coverage cell name, average RSRP and measurement sampling point number.
Acquiring intelligent park planning requirements, wherein the requirements comprise automatic matching and field collection; the automatic matching is realized by acquiring an environment image of the intelligent park and generating a three-dimensional layer, and dividing the three-dimensional layer into more than two areas which are overlapped with the MDT grids; identifying an environment scene in an MDT grid, and matching to obtain a 5G service requirement of a planning area (the MDT is fixed and is a square with the size of 55 meters, and the area after the three-dimensional layer division is consistent with the MDT in size); the field collection comprises the collection of intelligent office, intelligent production and intelligent management.
When the intelligent park planning requirement is obtained, automatic matching and field collection are distinguished, wherein automatic matching is achieved through shooting the environment image of the intelligent park, the image is processed, and a three-dimensional map layer is generated. The three-dimensional map layer can intuitively reflect the environment of the whole park and provide convenience for subsequent path loss evaluation and service subdivision.
The method for generating the three-dimensional layer by the image comprises the following steps:
selecting a key position of a target object in a street view image; the critical locations include edges or corners of the building. It can be understood that the street view image includes objects such as buildings, parks, roads, etc.;
taking the key positions as image control points, and numbering the image control points; the rule of numbering is key position name + number + control point + number. For example, the edge of a building is selected as a key position, and the key position is numbered as follows: building 1+ control point 1, building 1+ control point 2, etc. It will be appreciated that by numbering the control points, different objects and their characteristic points can be distinguished.
Manually selecting a position corresponding to the key position as a target control point based on the three-dimensional city vector target model;
numbering the target control points by the same number as the image control points;
carrying out geometric correction on the street view image based on the control points with the same number, and then projecting and superposing the street view image on the three-dimensional city vector target model to complete multi-model composite paster; the geometric correction and the stereographic mapping of the image are carried out on the vector three-dimensional model, which is a general technology for processing the image of the image and a common function of the image and three-dimensional software. The basic principle comprises the following steps: 1. establishing a mapping relation between image point coordinates (row and column numbers) and corresponding point coordinates of the vector model, solving unknown parameters in the mapping relation, and correcting each pixel coordinate of the image according to the mapping relation; 2. and adopting image interpolation for the uncovered part outside the target control point. It can be understood that the street view picture where the image control point is located is projected and superposed on the three-dimensional city vector target model on the basis of the control points with the same number.
And (3) changing other angles (the image selection is limited by a wide angle, the streetscape image can be processed only from a certain angle, and a plurality of angles are required to be evaluated respectively in order to achieve 360-degree full coverage), selecting streetscape images of other same targets and different angles, and repeating the steps until the three-dimensional city vector target model is completely covered by 360 degrees. And finally obtaining a scene image.
And dividing the generated three-dimensional layer into a plurality of areas, wherein the areas are overlapped with the MDT grids. Currently MDT grids are squares 55 meters on a side. The method comprises the following steps of obtaining an environment scene to which a three-dimensional layer in an MDT grid belongs by identifying the corresponding three-dimensional layer in the MDT grid, wherein the main environment scene comprises a road, a factory building, a residential area, an office building, a green belt, a parking lot and the like, the method for identifying the environment scene in the MDT grid based on deep learning adopts a multi-scale convolution neural network, the multi-scale convolution neural network comprises a multi-scale layer for carrying out multi-scale processing on an input scene image, and the identification method comprises the following steps: setting a resolution level, wherein the resolution level associates a resolution range with image processing parameters, and the image processing parameters comprise scene image input, the number of times of the multi-scale convolutional neural network and the scale value of multi-scale processing; acquiring the image resolution of the scene image; and performing subsequent processing on the scene image according to the resolution range corresponding to the image resolution and the image processing parameters (the subsequent processing comprises the steps of inputting times, scale values of multi-scale processing, intra-scale fusion, inter-scale fusion and the like, and belongs to the prior art).
Obtaining 5G service requirements of a planning area according to environment scene matching, wherein the service requirements comprise three types: large bandwidth (eMBB), wide connection (mtc), low latency (urrllc).
The field collection comprises the collection of intelligent office, intelligent production and intelligent management,
evaluating MDT data of the intelligent park to obtain the coverage of the intelligent park and the distribution condition of users;
based on link budget and a propagation model, estimating the coverage and service conditions of the intelligent park and generating a 5G coverage simulation layer;
and formulating a 5G planning scheme, wherein the 5G planning scheme comprises a construction type and individual service configuration, the construction type comprises newly-built new 5G of new address and newly-built 5G of co-located LTE, and the individual service configuration comprises eMBB, mMTC and uRLLC.
Further, intelligent office includes show propaganda, security protection system, vehicle and personnel management, comprehensive monitoring among the on-the-spot collection, wisdom production is including official working, security protection system, production, storage commodity circulation, wisdom management includes that many platform system are integrated, many service system are integrated, many terminal system are integrated. As shown in table 1, the specific requirements for field collection are:
TABLE 1
Further, the access device for 5G service requirements includes a 5G terminal and a non-5G terminal CPE (Customer premises Equipment) data access, the 5G terminal includes a 5G mobile phone, a PC, and a PAD terminal, and the non-5G terminal CPE data access includes a network camera, a VR/AR (VR, virtual reality technology, AR, augmented reality technology), a RFID terminal (Radio Frequency Identification), a control box, an AGV (automatic Guided Vehicle), a mechanical arm, and a sensor. The sensor is mainly used for the application sensor of the internet of things on a 5G network, such as a sensor based on temperature, humidity, pH value and the like.
The 5G planning scheme comprises coverage requirement evaluation and service requirement evaluation;
the coverage requirement evaluation comprises: judging whether the LTE site in the 5G coverage simulation layer meets the 5G coverage requirement or not, and if the MDT grid coverage rate of the 5G coverage simulation layer is more than 95%, judging that the 5G coverage requirement is met;
the service requirement evaluation comprises the following steps: and judging whether the service condition in the 5G coverage simulation layer meets the 5G service condition, wherein the 5G service requirement comprises a large bandwidth eMBB, a wide connection mMTC and a low time delay uRLLC.
The following table 2 shows the 5G service conditions of different terminal types in the campus:
TABLE 2
And if the conditions in the table 2 are met, judging that the 5G service condition is met.
Further, the on-site collection also comprises surveying the spatial layout of the park, surveying the geographical position, spatial dimension, building distribution, dimension and structure of the park, and decomposing the park space from an outdoor-park road, an outdoor-matching area, an indoor-workshop/factory building and an indoor-office area by combining the difference of the human and object focusing degree and the regional function, wherein the outdoor-matching area is a living service area and a management area except production and office, and comprises a parking lot, a restaurant, a movable room and a rest area.
Further, the link budget PLmaxThe formula is as follows:
PLmax＝PTx-Lf+GTx-Mf-Ml+GRx-Lp-Lb-SRx
PTxfor base station transmit power, LfFor feeder losses, GTxFor base station antenna gain, MfFor shadow fading and fast fading margins, MlAs interference margin, GRxGain for mobile phone antenna, LpFor building penetration losses, LbIs a loss of the human body, SRxThe receiving sensitivity of the mobile phone is set;
the line-of-sight propagation model formula of the urban macro cell is as follows:
PL1＝32.4+20 lg d3D+20 lg fc
wherein PL1Represents the path loss of the line-of-sight propagation model,fcis the operating frequency of the communication network (e.g. 2515MHz-2675MHz of 5G), d3DIs the base station antenna to mobile station antenna linear distance (m).
Based on the intelligent park MDT information and the 5G path loss, the MDT data of the intelligent park 5G are estimated, a 5G coverage simulation layer is generated, and the 5G coverage simulation layer is an MDT grid coverage layer.
Example 1
A5G intelligent park macro station planning method comprises the following steps:
FIG. 1 is a flow chart of a 5G network planning method for an intelligent park, which includes the following steps:
step 102, obtaining mobile communication information, wherein the mobile communication information comprises current network LTE engineering parameter information and MDT information. Fig. 2 is a diagram of the LTE parameters and MDT information of the smart campus, where the planning area includes LTE base stations 1 and 2, and each base station has 3 sectors, as shown in table 3 below:
TABLE 3
The MDT grid comprises corresponding sampling points of occupied cells and average RSRP numbers, wherein green represents that the average RSRP in the grid is larger than-85 dBm, blue represents that the average RSRP in the grid is-85-100 dBm, yellow represents that the average RSRP in the grid is-100-110 dBm, and red represents that the average RSRP in the grid is smaller than-110 dBm. According to the LTE working parameter information and the MDT information, it can be found that a weak coverage grid exists in the south of the intelligent park, the weak coverage grid is defined as that the average RSRP in the grid is less than-110 dBm, and the weak coverage reason is mainly caused by dense factory buildings in the area and no main coverage base station around the weak coverage.
104, acquiring intelligent park planning requirements, wherein the requirements comprise automatic matching and field collection; the automatic matching is realized by acquiring an environment image of the intelligent park and generating a three-dimensional layer, and dividing the three-dimensional layer into a plurality of areas which are overlapped with the MDT grids; identifying an environment scene in the MDT grid, and matching to obtain 5G service requirements of a planning area; the field collection comprises the collection of intelligent office, intelligent production and intelligent management.
Fig. 3 is a three-dimensional map layer structure diagram of a planning area, where an unmanned aerial vehicle acquires an image of a park, and then performs image processing on the acquired image to generate a three-dimensional map image. And manually selecting a position corresponding to the key position as a target control point based on a three-dimensional city vector target model, numbering the target control point by using the same number as the image control point, geometrically correcting the street view image based on the control point with the same number, and projecting and superposing the street view image on the three-dimensional city vector target model to finish multi-model composite paster. And transforming other angles, selecting other street view images of the same target and different angles, and repeating the steps until the three-dimensional city vector target model is completely covered by 360 degrees.
And then automatically matching corresponding scenes by an identification technology, wherein the identification of the scene identification method of the environment scene in the MDT grid based on deep learning adopts a multi-scale convolutional neural network, the multi-scale convolutional neural network comprises a multi-scale layer for carrying out multi-scale processing on an input scene image, and the identification method comprises the following steps: setting a resolution level, wherein the resolution level associates a resolution range with image processing parameters, and the image processing parameters comprise scene image input, the number of times of the multi-scale convolutional neural network and the scale value of multi-scale processing; acquiring the image resolution of the scene image; and performing subsequent processing on the scene image according to the image processing parameters according to the resolution range corresponding to the image resolution.
And step 106, evaluating the MDT data of the intelligent park to obtain the coverage of the intelligent park and the distribution condition of the users. The coverage condition is based on the RSRP data of the MDT, and the user distribution is analyzed through the number of MDT sampling points in each grid.
And step 108, pre-estimating the coverage and service conditions of the intelligent park based on the link budget and the propagation model, and generating a 5G coverage simulation layer. Link budget PLmaxThe formula is as follows:
PLmax＝PTx-Lf+GTx-Mf-Ml+GRx-Lp-Lb-SRx
PTxfor base station transmit power, LfFor feeder losses, GTxFor base station antenna gain, MfFor shadow fading and fast fading margins, MlAs interference margin, GRxGain for mobile phone antenna, LpFor building penetration losses, LbIs a loss of the human body, SRxThe receiving sensitivity of the mobile phone is set;
the line-of-sight propagation model of urban macro cells is based on the 3GPP protocol 38.901, and its formula is:
PL1＝32.4+20 lg d3D+20 lg fc
PL2＝32.4+40 lg d3D+20 lg fc-10lg((d′BP)2+(hBS-hUT)2)
the above scenario needs to satisfy the following conditions: sigmaSF＝4，1.5m≤hUT≤22.5m，hBS＝25m。
fcIs the operating frequency (GHz), hBSIs the base station antenna effective height (m), which is specified in the Uma model herein as 25m, hUTIs the effective height (m), d of the mobile station antenna2DIs the horizontal distance (m), d) between the base station and the mobile station3DIs the linear distance (m), σ, between the base station antenna and the mobile station antennaSFStandard value of path loss, d'BPIs the apparent distance. Through comparing different losses of LTE and 5G, the coverage condition of 5G in a planning area is estimated, wherein the main evaluation object is fcIs the working frequency (GHz), LTE mainly adopts frequency bands such as D frequency, F frequency, FDD900, FDD1800 and the like at present, 5G mainly samples a 2.6G frequency band, FDD1800 frequency band is sampled according to LTE, 2.6G frequency band is sampled according to 5G, and under the condition that other conditions are the same, the frequency bands are mutually matchedThe same distance coverage 5G consumes about 6.34dB more compared to LTE. In the actual process, the actual loss is more obvious compared with the LTE network due to various environments.
Fig. 4 is a 5G simulation layer and a planning scheme diagram, and a 5G simulation layer is generated on the basis of the LTE grid through the loss estimation, where it can be seen that, at the position of the original LTE weak coverage grid, coverage corresponding to 5G is worse, and especially in a south office building area, weak coverage is obvious.
Step 110, a 5G planning scheme is formulated, wherein the 5G planning scheme includes a construction type and an individual service configuration, the construction type includes new-address 5G and new-address 5G of co-located LTE, and the individual service configuration includes eMBB, mtc, and urrllc. Through the above analysis, the 5G macro station planning scheme of the intelligent park is as follows: A5G base station is newly built on the roof of a southern office building, the total number of sectors is 3, the height of an antenna is 20 m, the azimuth angles are respectively 70 degrees, 250 degrees and 310 degrees, and the downward inclination angles are respectively 8 degrees, 8 degrees and 10 degrees. In the aspect of service configuration, the corresponding region of the eMBB is as follows: dormitory district, office building district, mMTC corresponds the region and is: research and development building, wisdom factory building, the corresponding region of uRLLC is: research and development building, wisdom factory, factory building, dormitory district.
Example 2
A5G room division planning method for an intelligent park comprises the following steps:
fig. 5 is a diagram of a layout of intelligent campus divisions, the industrial campus occupying an area of 120 square meters or more, including production plants, scientific research and test office buildings and supporting buildings. In the 5G + intelligent park project, 5G is combined with an existing research and development design system, a production control system, a service management system and the like in park production, so that the deep change of the production processes of research and development design, production manufacturing, management service and the like in the 5G vertical industry can be comprehensively promoted, and the conversion of the manufacturing industry to intellectualization, servitization and high-end transformation is realized.
The specific use steps of the 5G intelligent park network planning scheme in the industrial park are as follows:
s1: and collecting the park business requirements. The data are collected from three aspects of intelligent office, intelligent production and intelligent management, as shown in table 4.
TABLE 4
S2: and converting the service requirements of the park. And classifying the terminals of the terminal access equipment which realizes the park service requirement in the step S1, counting the number of each type of terminals, and giving the communication network requirement for each type of terminal to refer to.
S3: spatial layout of the exploration park. The total area of the garden is more than 120 ten thousand square meters, the south and north are 1.65 kilometers long, and the east and west are 0.74 kilometers wide. According to the different functions of buildings in the garden, the garden is divided into a production-factory building, a production-office area, a matching area and a garden main road.
S4: and establishing a garden space network demand model. The communication network requirements of each type of terminal in the step of S2 are combined with the four areas of the campus in the step of S3, and the spatially distributed locations of each type of terminal and the corresponding communication network requirements are determined, as shown in table 5.
TABLE 5
S5: a planning scheme of a 5G wireless network in a park is used for planning indoor subsystems of factory buildings, production-scientific research buildings, production-office buildings and matched buildings, the scene service application capacity requirement is low, the indoor environment is spacious, the partition is less, indoor 5G low-frequency coverage can be adopted, and AAU/RRU indoor installation is selected to meet the coverage requirement. The scientific research building recommends adopting RHUB + PRRU mode because of involving multiple service types.
The invention provides a method for planning a 5G network of an intelligent park, which has many specific methods and ways for implementing the technical solution, and the above description is only a preferred embodiment of the invention, and it should be noted that, for those skilled in the art, a plurality of improvements and modifications can be made without departing from the principle of the invention, and these improvements and modifications should also be regarded as the protection scope of the invention. All the components not specified in the present embodiment can be realized by the prior art.
Claims (10)
1. A5G network planning method for an intelligent park is characterized by comprising the following steps:
step 1, acquiring mobile communication information;
step 2, obtaining the planning requirement of the intelligent park through automatic matching and field collection,
step 3, evaluating MDT data of the intelligent park to obtain the coverage of the intelligent park and the distribution condition of users;
step 4, estimating the coverage and service conditions of the smart park based on the link budget and a line-of-sight propagation model of the urban macro-cell, and generating a 5G coverage simulation layer;
and 5, making a 5G planning scheme.
2. The method according to claim 1, wherein in step 1, the mobile communication information includes current network LTE parameters and MDT information;
the current network LTE working parameter information comprises the existing LTE planning site position, sector name, antenna azimuth angle, antenna height, antenna lower angle and frequency band of the smart park;
the MDT information is based on measurement information performed on an LTE network sub-grid, and the measurement information comprises measurement time, a main coverage cell name, average RSRP and measurement sampling point number.
3. The method of claim 2, wherein in step 2, the automatically matching comprises: the method comprises the steps that an environment image of an intelligent park is obtained and a three-dimensional layer is generated, the three-dimensional layer is divided into more than two areas, the areas are overlapped with an MDT grid, an environment scene in the MDT grid is identified, and the 5G service requirements of a planning area are obtained through matching;
the field collection comprises the collection of intelligent office, intelligent production and intelligent management.
4. The method according to claim 3, wherein in step 2, the method for generating the three-dimensional layer is:
step a1, selecting key positions of target objects in street view images;
step a2, using the key position as an image control point, and numbering the image control point;
step a3, selecting a position corresponding to the key position as a target control point based on a three-dimensional city vector target model;
a step a4 of numbering the target control points based on the same number as the image control points;
step a5, carrying out geometric correction on the street view image based on the control points with the same number, and then projecting and superposing the street view image on the three-dimensional city vector target model to complete multi-model composite paster;
step a6, changing other angles, selecting other street view images of the same target and different angles, repeating the steps a1 and a5 until the three-dimensional city vector target model is completely covered by 360 degrees, and finally obtaining a scene image.
5. The method of claim 4, wherein in step 2, the identifying the environmental scene in the MDT grid comprises: identifying an environment scene in the MDT grid by adopting a multi-scale convolutional neural network and a scene identification method based on deep learning;
the multi-scale convolutional neural network comprises a multi-scale layer for carrying out multi-scale processing on an input scene image;
the scene recognition method based on deep learning comprises the following steps: setting a resolution level, wherein the resolution level associates a resolution range with image processing parameters, and the image processing parameters comprise scene image input, times of a multi-scale convolutional neural network and scale values of multi-scale processing; and acquiring the image resolution of the scene image, and performing subsequent processing on the scene image according to the resolution range corresponding to the image resolution and the image processing parameters.
6. The method according to claim 5, wherein in step 2, the access equipment required by the 5G service comprises a 5G terminal and a non-5G terminal CPE data access, the 5G terminal comprises a 5G mobile phone, a PC and a PAD terminal, and the non-5G terminal CPE data access comprises a network camera, a VR/AR, an RFID terminal, a control box, an AGV, a mechanical arm and a sensor.
7. The method of claim 6, wherein in step 2, the intelligent office includes displaying publicity, security systems, vehicle and personnel management and comprehensive monitoring;
the intelligent production comprises office, security system, production and warehouse logistics;
the intelligent management comprises multi-platform system integration, multi-service system integration and multi-terminal system integration.
8. The method of claim 7, wherein in step 2, the on-site collection further comprises surveying the spatial layout of the campus, surveying the geographical location, spatial dimensions, building distribution, dimensions and structure of the campus, and decomposing the campus space from outdoor to campus road, outdoor to matching area, indoor to workshop or factory building, indoor to office area, according to the difference of people and object focusing degree and regional function, wherein the outdoor to matching area is a life service area and a management area except production and office, including parking lot, restaurant, activity room and rest area.
9. The method according to claim 8, wherein in step 4, the link budget PL is calculated using the following formulamax：
PLmax＝PTx-Lf+GTx-Mf-Ml+GRx-Lp-Lb-SRx
Wherein, PTxFor base station transmit power, LfFor feeder losses, GTxFor base station antenna gain, MfFor shadow fading and fast fading margins, Ml for interference margins, GRxGain for mobile phone antenna, LpFor building penetration losses, LbIs a loss of the human body, SRxThe receiving sensitivity of the mobile phone is set;
the line-of-sight propagation model formula of the urban macro cell is as follows:
PL1＝32.4+20lgd3D+20lgfc
wherein PL1Representing the path loss of the line-of-sight propagation model, fcIs the operating frequency of the communication network, d3DIs the linear distance between the base station antenna and the mobile station antenna;
based on the intelligent park MDT information and the 5G path loss, the MDT data of the intelligent park 5G are estimated, a 5G coverage simulation layer is generated, and the 5G coverage simulation layer is an MDT grid coverage layer.
10. The method according to claim 9, wherein in step 5, the 5G planning scheme comprises coverage requirement evaluation and business requirement evaluation;
the coverage requirement evaluation comprises: judging whether the LTE site in the 5G coverage simulation layer meets the 5G coverage requirement or not, and if the MDT grid coverage rate of the 5G coverage simulation layer is more than 95%, judging that the 5G coverage requirement is met;
the service requirement evaluation comprises the following steps: and judging whether the service condition in the 5G coverage simulation layer meets the 5G service condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111026663.7A CN113873532B (en) | 2021-09-02 | 2021-09-02 | Intelligent park 5G network planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111026663.7A CN113873532B (en) | 2021-09-02 | 2021-09-02 | Intelligent park 5G network planning method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113873532A true CN113873532A (en) | 2021-12-31 |
| CN113873532B CN113873532B (en) | 2024-04-19 |
Family
ID=78989287
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111026663.7A Active CN113873532B (en) | 2021-09-02 | 2021-09-02 | Intelligent park 5G network planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113873532B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115829294A (en) * | 2023-01-05 | 2023-03-21 | 阿里巴巴（中国）有限公司 | Network planning method, storage medium and electronic device |
| CN116390106A (en) * | 2023-04-18 | 2023-07-04 | 国脉通信规划设计有限公司 | Intelligent community 5G network planning method |
| CN116806026A (en) * | 2023-08-25 | 2023-09-26 | 山东高速信息集团有限公司 | 5G network synchronous laying method and equipment based on expressway construction |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102523590A (en) * | 2012-01-05 | 2012-06-27 | 北京邮电大学 | Planning method of multi-system intelligent configurable wireless network |
| CN103052081A (en) * | 2012-12-20 | 2013-04-17 | 大唐移动通信设备有限公司 | Network coverage planning method and device of evolution communication system |
| CN103544734A (en) * | 2013-10-11 | 2014-01-29 | 深圳先进技术研究院 | Street vie based three-dimensional map modeling method |
| CN109379746A (en) * | 2018-11-19 | 2019-02-22 | 张晓波 | A kind of emulation mode and system of the covering of smart city signal |
| CN109831793A (en) * | 2019-03-12 | 2019-05-31 | 中国电力科学研究院有限公司 | A kind of method and system of the network planning suitable for 230M electric power wireless communication |
| WO2020098575A1 (en) * | 2018-11-16 | 2020-05-22 | 华为技术有限公司 | Capacity planning method and device |
| US20200178085A1 (en) * | 2018-11-29 | 2020-06-04 | Nokia Solutions And Networks Oy | Measurement-based wireless communications network design |
| CN111314940A (en) * | 2020-03-06 | 2020-06-19 | 重庆邮电大学 | Wireless network deployment method for 5G NSA networking mode |
| CN111382685A (en) * | 2020-03-04 | 2020-07-07 | 电子科技大学 | Scene recognition method and system based on deep learning |
| CN112084916A (en) * | 2020-08-31 | 2020-12-15 | 东南大学 | Automatic generation and diagnosis method for urban three-dimensional skyline contour line based on shielding rate |
| CN112469066A (en) * | 2019-09-09 | 2021-03-09 | 中国移动通信集团河北有限公司 | 5G network coverage evaluation method and device |
| CN113038485A (en) * | 2019-12-24 | 2021-06-25 | 中国移动通信集团浙江有限公司 | MDT data-based base station cell power parameter calculation method and device |
| CN113283824A (en) * | 2021-07-26 | 2021-08-20 | 浙江九州云信息科技有限公司 | Comprehensive management method and system for intelligent park data |
-
2021
- 2021-09-02 CN CN202111026663.7A patent/CN113873532B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102523590A (en) * | 2012-01-05 | 2012-06-27 | 北京邮电大学 | Planning method of multi-system intelligent configurable wireless network |
| CN103052081A (en) * | 2012-12-20 | 2013-04-17 | 大唐移动通信设备有限公司 | Network coverage planning method and device of evolution communication system |
| CN103544734A (en) * | 2013-10-11 | 2014-01-29 | 深圳先进技术研究院 | Street vie based three-dimensional map modeling method |
| WO2020098575A1 (en) * | 2018-11-16 | 2020-05-22 | 华为技术有限公司 | Capacity planning method and device |
| CN109379746A (en) * | 2018-11-19 | 2019-02-22 | 张晓波 | A kind of emulation mode and system of the covering of smart city signal |
| US20200178085A1 (en) * | 2018-11-29 | 2020-06-04 | Nokia Solutions And Networks Oy | Measurement-based wireless communications network design |
| CN109831793A (en) * | 2019-03-12 | 2019-05-31 | 中国电力科学研究院有限公司 | A kind of method and system of the network planning suitable for 230M electric power wireless communication |
| CN112469066A (en) * | 2019-09-09 | 2021-03-09 | 中国移动通信集团河北有限公司 | 5G network coverage evaluation method and device |
| CN113038485A (en) * | 2019-12-24 | 2021-06-25 | 中国移动通信集团浙江有限公司 | MDT data-based base station cell power parameter calculation method and device |
| CN111382685A (en) * | 2020-03-04 | 2020-07-07 | 电子科技大学 | Scene recognition method and system based on deep learning |
| CN111314940A (en) * | 2020-03-06 | 2020-06-19 | 重庆邮电大学 | Wireless network deployment method for 5G NSA networking mode |
| CN112084916A (en) * | 2020-08-31 | 2020-12-15 | 东南大学 | Automatic generation and diagnosis method for urban three-dimensional skyline contour line based on shielding rate |
| CN113283824A (en) * | 2021-07-26 | 2021-08-20 | 浙江九州云信息科技有限公司 | Comprehensive management method and system for intelligent park data |
Non-Patent Citations (2)
| Title |
|---|
| 钱权智: "面向5G与LTE混合组网的无线网络规划研究", 《中国优秀硕士学位论文全文数据库(电子期刊)》, 15 February 2021 (2021-02-15), pages 3 * |
| 陆南昌: "5G无线网络智能规划技术的探索与实践", 《移动通信》, 15 May 2020 (2020-05-15), pages 2 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115829294A (en) * | 2023-01-05 | 2023-03-21 | 阿里巴巴（中国）有限公司 | Network planning method, storage medium and electronic device |
| CN116390106A (en) * | 2023-04-18 | 2023-07-04 | 国脉通信规划设计有限公司 | Intelligent community 5G network planning method |
| CN116806026A (en) * | 2023-08-25 | 2023-09-26 | 山东高速信息集团有限公司 | 5G network synchronous laying method and equipment based on expressway construction |
| CN116806026B (en) * | 2023-08-25 | 2023-11-03 | 山东高速信息集团有限公司 | 5G network synchronous laying method and equipment based on expressway construction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113873532B (en) | 2024-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113873532B (en) | Intelligent park 5G network planning method | |
| CN110831019B (en) | Base station planning method, base station planning device, computer equipment and storage medium | |
| Kurner et al. | Prediction of outdoor and outdoor-to-indoor coverage in urban areas at 1.8 GHz | |
| CN111062466B (en) | Method for predicting field intensity distribution of cell after antenna adjustment based on parameters and neural network | |
| CN106886552A (en) | Location fingerprint database update method and system | |
| CN109286946B (en) | Mobile communication indoor wireless network optimization method and system based on unsupported positioning | |
| CN110798804B (en) | Indoor positioning method and device | |
| CN112203293A (en) | Cell over-coverage identification method, device, equipment and computer storage medium | |
| CN108281023B (en) | Method and system for displaying real-time road conditions through mobile terminal | |
| CN114466452A (en) | Indoor high-precision positioning system and method based on 5G | |
| CN114025372B (en) | MR data-based 5G RSRP calculation method and system | |
| CN109474887B (en) | High-low floor distinguishing method based on LTE signals | |
| CN114828026A (en) | Base station planning method, device, equipment, storage medium and program product | |
| JP5407040B2 (en) | Search area reduction to estimate the location of wireless terminals based on indoor detection | |
| Ramamurthy et al. | Ml-based classification of device environment using wi-fi and cellular signal measurements | |
| CN116133037A (en) | Wireless network evaluation method and device | |
| CN115360633A (en) | Power network operation and maintenance system and method based on data perception fusion | |
| Wang et al. | Improving the localization accuracy for Sigfox low-power wide area networks | |
| CN116324803A (en) | Data processing method and related equipment | |
| CN107454565B (en) | A kind of localization method and device | |
| Li et al. | Geo2SigMap: High-Fidelity RF Signal Mapping Using Geographic Databases | |
| CN111432429B (en) | Wireless channel model matching correction method based on map information | |
| CN113806643B (en) | Method and device for determining cell to be adjusted, electronic equipment and readable storage medium | |
| CN114501618B (en) | Positioning model training method, positioning method, and computer-readable storage medium | |
| EP3015878B1 (en) | Method, recording medium and electronic device of 3d geolocation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |