CN110636516B - Method and device for determining signal propagation model - Google Patents

Method and device for determining signal propagation model Download PDF

Info

Publication number
CN110636516B
CN110636516B CN201910828985.XA CN201910828985A CN110636516B CN 110636516 B CN110636516 B CN 110636516B CN 201910828985 A CN201910828985 A CN 201910828985A CN 110636516 B CN110636516 B CN 110636516B
Authority
CN
China
Prior art keywords
value
fitting
path loss
signal propagation
parameter sets
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.)
Active
Application number
CN201910828985.XA
Other languages
Chinese (zh)
Other versions
CN110636516A (en
Inventor
杨艳
冯毅
张涛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China United Network Communications Group Co Ltd
Original Assignee
China United Network Communications Group Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by China United Network Communications Group Co Ltd filed Critical China United Network Communications Group Co Ltd
Priority to CN201910828985.XA priority Critical patent/CN110636516B/en
Publication of CN110636516A publication Critical patent/CN110636516A/en
Application granted granted Critical
Publication of CN110636516B publication Critical patent/CN110636516B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Quality & Reliability (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The application provides a method and a device for determining a signal propagation model, relates to the field of communication, and is used for improving the accuracy of the signal propagation model. The method comprises the following steps: the server determines RSRPs of P places according to the mapping relation between the SINR and the RSRP in the coverage area of the base station and SINR values of P places in the coverage area of the base station; the server determines P measurement parameter sets according to the RSRP of the P places; the set of measurement parameters includes: a value of signal propagation distance and a value of measurement path loss; the server determines N fitting parameter sets according to the P measurement parameter sets, and determines a first fitting function and a second fitting function according to the N fitting parameter sets; the server determines a target value of a path loss constant and a target value of an attenuation coefficient according to the first fitting function and the second fitting function; and the server determines a signal propagation model according to the target value of the path loss constant and the target value of the attenuation coefficient.

Description

Method and device for determining signal propagation model
Technical Field
The present application relates to the field of communications, and in particular, to a method and an apparatus for determining a signal propagation model.
Background
When planning a network, the inter-station distance of base stations in a target area needs to be determined by means of signal propagation model prediction, and network planning and station setting are carried out on the target area according to the inter-station distance of the base stations.
The current signal propagation model determination method comprises the following steps: and a simple Continuous Wave (CW) base station is built, the CW base station transmits a single-beam signal, and a signal propagation model is determined according to the signal intensity of the single-beam signal transmitted by the CW base station and the signal intensity of the single-beam signal received by the terminal to evaluate the relationship between the path loss and the signal propagation distance.
However, the beams transmitted by the CW base station are relatively simple, and cannot reflect the relationship between the path loss and the signal propagation distance in an actual scene, and the current estimation method for the path loss and the signal propagation distance cannot reflect the actual propagation relationship between the path loss and the signal propagation distance. Therefore, the accuracy of the signal propagation model determined by the method for constructing the CW base station to transmit the single-beam signal in the prior art is low.
Disclosure of Invention
The embodiment of the application provides a method, a device and a system for determining a signal propagation model. For improving the accuracy of the signal propagation model.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides a method for determining a signal propagation model, the method comprising: the server determines RSRPs of P places according to a mapping relation between signal to interference plus noise ratio (SINR) and received reference signal strength (RSRP) in a coverage area of the base station and SINR values of the P places in the coverage area of the base station;
the server determines P measurement parameter sets according to the RSRP of the P places; wherein, P measurement parameter sets correspond to P places in the coverage area of the base station one by one, and the measurement parameter sets comprise: a value of signal propagation distance and a value of measurement path loss, P being a positive integer; the server determines N fitting parameter sets according to the P measurement parameter sets, wherein the fitting parameter sets comprise: the value of the path loss constant, the value of the attenuation coefficient, the value of the sample solution coefficient, and the value of the root mean square error, where N is a positive integer; the server determines a first fitting function and a second fitting function according to the N fitting parameter sets; the dependent variable of the first fitting function is a sample coefficient, and the independent variable of the first fitting function is a path loss constant and an attenuation coefficient; the dependent variable of the second fitting function is root mean square error, and the independent variable of the second fitting function is a path loss constant and an attenuation coefficient; the server determines a target value of a path loss constant and a target value of an attenuation coefficient according to the first fitting function and the second fitting function; the server determines a signal propagation model based on the target value of the path loss constant and the target value of the attenuation coefficient.
Based on the technical scheme, the influence of the terminal on other base stations and devices when measuring the RSRP is large, and the influence of the terminal on other base stations and devices when measuring the SINR is small, so that the SINR of P places is measured, the RSRP of the P places is determined according to the mapping relation between the SINR and the RSRP, and the influence of other base stations and devices on the collected RSRP is reduced. The server determines P measurement parameter sets according to the RSRP of the P places, and the P measurement parameter sets comprise signal propagation distance values and measurement path loss values and correspond to the P places of the coverage area of the base station one by one, so that the P measurement parameter sets can comprehensively reflect the signal propagation condition of the base station in the coverage area of the base station. Further, the P measurement parameter sets are subjected to mathematical analysis, and a target value of a path loss constant and a target value of an attenuation coefficient in the signal propagation model are determined. Thus, the target values of the path loss constant and the attenuation coefficient determined based on the P measurement parameter sets are accurate. Furthermore, the signal propagation model determined by the embodiment of the application has higher accuracy.
In a second aspect, the present application provides an apparatus for determining a signal propagation model, the apparatus comprising: the processing unit is used for determining the RSRPs of P places according to the mapping relation between the signal to interference plus noise ratio (SINR) in the coverage area of the base station and the received reference signal strength (RSRP) and SINR values of the P places in the coverage area of the base station; the processing unit is further configured to determine P measurement parameter sets according to the RSRPs of the P sites; wherein, P measurement parameter sets correspond to P places in the coverage area of the base station one by one, and the measurement parameter sets comprise: a value of signal propagation distance and a value of measurement path loss, P being a positive integer; the processing unit is further configured to determine N fitting parameter sets according to the P measurement parameter sets, where the fitting parameter sets include: a value of a path loss constant, a value of an attenuation coefficient, a value of a sample solution coefficient, and a value of a root mean square error, N being a positive integer; the processing unit is further used for determining a first fitting function and a second fitting function according to the N fitting parameter sets; the dependent variable of the first fitting function is a sample coefficient, and the independent variable of the first fitting function is a path loss constant and an attenuation coefficient; the dependent variable of the second fitting function is root mean square error, and the independent variable of the second fitting function is a path loss constant and an attenuation coefficient; the processing unit is further used for determining a target value of the path loss constant and a target value of the attenuation coefficient according to the first fitting function and the second fitting function; and the processing unit is also used for determining a signal propagation model according to the target value of the path loss constant and the target value of the attenuation coefficient.
In a third aspect, the present application provides an apparatus for determining a signal propagation model, the apparatus comprising: a processor and a communication interface; the communication interface is coupled to a processor for executing a computer program or instructions to implement the method for determining a signal propagation model as described in the first aspect and any one of its implementations.
In a fourth aspect, the present application provides a readable storage medium, in which instructions are stored, and when the instructions are executed by a computer, the computer executes the method for determining a signal propagation model described in the first aspect and any one of the implementation manners thereof.
In a fifth aspect, the present application provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the method for determining a signal propagation model as described in the first aspect and any one of its implementations.
In a sixth aspect, an embodiment of the present application provides a chip, where the chip includes a processor and a communication interface, and the communication interface is coupled to the processor, and the processor is configured to execute a computer program or instructions to implement the method for determining a signal propagation model as described in the first aspect and any implementation manner thereof.
In particular, the chip provided in the embodiments of the present application further includes a memory for storing a computer program or instructions.
Drawings
Fig. 1 is a schematic structural diagram of a server according to an embodiment of the present application;
fig. 2 is a system architecture diagram of a communication system according to an embodiment of the present application;
fig. 3 is a first flowchart of a method for determining a signal propagation model according to an embodiment of the present disclosure;
fig. 4 is a distribution diagram of a coordinate point composed of SINR and RSRP in a coordinate system according to an embodiment of the present application;
fig. 5 is a second flowchart of a method for determining a signal propagation model according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram illustrating a distance between a terminal and a target base station according to an embodiment of the present disclosure;
fig. 7 is a flowchart three of a method for determining a signal propagation model according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a first fitted curve provided by an embodiment of the present application;
FIG. 9 is a schematic diagram of a second fitted curve provided by an embodiment of the present application;
fig. 10 is a schematic structural diagram of an apparatus for determining a signal propagation model according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of another apparatus for determining a signal propagation model according to an embodiment of the present application;
fig. 12 is a schematic structural diagram of another apparatus for determining a signal propagation model according to an embodiment of the present application.
Detailed Description
The information reading method and apparatus provided in the present application will be described in detail below with reference to the accompanying drawings.
The terms "first" and "second", etc. in the description and drawings of the present application are used for distinguishing between different objects and not for describing a particular order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
The terms referred to in this application are explained below to facilitate the understanding of the reader:
(1) signal propagation model
The signal propagation model is used for representing the relationship between the path loss generated when a wireless signal transmitted by a target base station propagates in the coverage area of the base station and the propagation distance of the wireless signal in the coverage area of the base station.
The path loss generated during the propagation of the wireless signal in the coverage area of the base station mainly includes: propagation loss d-nShadow fading
Figure GDA0003590087490000041
And multipath fading
Figure GDA0003590087490000042
The received signal power and the path loss of the terminal generally satisfy the following relationship:
Figure GDA0003590087490000051
where d is the distance traveled by the wireless signal transmitted by the target base station (i.e., the distance from the target base station to the terminal).
Is the received signal power of the terminal, which is a function of the distance between the target base station and the terminal.
d-nThe value of n is generally 3-4 for space propagation loss.
Figure GDA0003590087490000052
The shadow fading is fading caused by the topography in the propagation environment, and the shielding of the building and other obstacles from the radio wave, and is also called slow fading, and the fading characteristic thereof conforms to the log-normal distribution.
Figure GDA0003590087490000053
The fading is a fading caused by multipath propagation in a mobile propagation environment, and is also called as fast fading, and the fading characteristic of the fading conforms to rayleigh distribution.
The formula:
Figure GDA0003590087490000054
can also be expressed as: r (x) m (x) ro(x)。
Wherein:
Figure GDA0003590087490000055
r (x) is the received signal power, ro(x) For rayleigh fading, m (x) is the average of the received signals for the base station coverage area. 2l is the average sampling interval length, also called the intrinsic length.
Through derivation and simplification, the standard propagation model often used in existing network planning software is:
L50＝K1+K2log10d+K3log10hte+K4log10LDiffraction
+K5log10dlog10hte+K6log10hre+K7fClutter
wherein L is50Represents the path loss; d represents a signal propagation distance (i.e., the distance between the target base station and the terminal); t is theRepresenting the base station antenna height; h is a total ofreRepresents the mobile station height; l isDiffractionRepresents the diffraction loss; f. ofClutterRepresenting feature loss; k1、K2、K3、K4、K5、K6、K7The adjustment parameters of the standard propagation model can be determined according to scenes, propagation environments, terrains and the like of different areas.
After the standard propagation model is subjected to parameter combination, a simple path loss formula (the path loss formula is the following first signal propagation model) can be obtained:
L＝A+10×m×log10(d)
wherein L represents a path loss; d represents a signal propagation distance; m represents an attenuation coefficient.
Path loss: the loss generated when a signal transmitted by a base station propagates between the base station and a terminal.
(2) Fitting (fitting)
Fitting is a method of expressing a functional relationship between a plurality of known data using a functional relationship. When a plurality of known data exist, a function curve is fitted according to the plurality of known data, and the function curve is close to each known data as a whole. Figuratively, fitting is to connect a series of points on a plane with a smooth curve. Because of the myriad possibilities for this curve, there are various methods of fitting. The fitted curve can be generally represented by a function, and different fitting names (for example, least square curve fitting method) are provided according to the function. Polyfit can also be used to fit polynomials in MATLAB.
The implicit mathematical relationship between the data can be characterized by a functional relationship obtained by fitting to known data.
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
The embodiment of the present application provides a server 30, configured to execute the method for determining a signal propagation model described in the embodiment of the present application.
As shown in fig. 1, the server 30 includes a communication module 301, a calculation module 302, and a determination module 303.
The communication module 301 is configured to communicate with a base station, and acquire a base station coverage capability parameter of the base station; the communication module 301 is further configured to communicate with the terminal, and acquire the location information of the terminal and the SINR and/or RSRP measured by the terminal.
Or, the communication module 301 is configured to communicate with the terminal, and acquire a base station coverage capability parameter acquired by the terminal from the base station, location information of the terminal, and SINR and/or RSRP measured by the terminal.
A calculating module 302, configured to obtain the location information of the terminal and SINR and/or RSRP measured by the terminal according to the base station coverage capability parameter obtained by the communication module 301. A set of measurement parameters is determined.
And a determining module 303, configured to perform mathematical model analysis on the measurement parameter set determined by the calculating module 302, and determine a signal propagation model.
The server 30 provided in the embodiment of the present application can be specifically applied to the communication system 100 shown in fig. 2, so as to execute the method for determining a signal propagation model described in the embodiment of the present application.
As shown in fig. 2, the communication system 100 includes: a base station 10, a terminal 20, and a server 30. The base station 10, the terminal 20 and the server 30 are communicatively connected to each other.
The Base Station 10 may be a Base Transceiver Station (BTS) in a Global System for Mobile Communication (GSM) or Code Division Multiple Access (CDMA), a Base Station (NodeB) in a Wideband Code Division Multiple Access (WCDMA), an evolved Node b (eNB) or e-NodeB (evolved Node b) in LTE, or the like. Or may be an eNB in the Internet of Things (Internet of Things, IoT) or a narrowband Internet of Things (Narrow Band-Internet of Things, NB-IoT), which is not specifically limited in this embodiment.
The base station 10 is configured to provide network services for terminals in a coverage area of the base station, and the base station establishes a communication connection with the terminals by means of transmitting wireless signals and performs transmission of service data.
The terminal 20 may be: user Equipment (UE), access terminal, terminal unit, terminal station, mobile station, remote terminal, mobile device, wireless communication device, vehicular user equipment, terminal agent, or terminal device, etc. Optionally, the terminal may be various handheld devices, vehicle-mounted devices, wearable devices, and computers with communication functions, which is not limited in this embodiment of the present application. For example, the handheld device may be a smartphone. The in-vehicle device may be an in-vehicle navigation system. The wearable device may be a smart bracelet. The computer may be a Personal Digital Assistant (PDA) computer, a tablet computer, and a laptop computer.
The terminal 20 has a positioning function and a function of measuring a Signal to Interference plus Noise Ratio (SINR) and a Reference Signal Receiving Power (RSRP) of a radio Signal.
Illustratively, the terminal is a mobile phone or an RSRP testing device and the like.
The terminal 20 is configured to perform and maintain service transmission with the base station 10, and periodically record to obtain location information of the terminal and SINR and/or RSRP measured by the terminal.
In one possible implementation, the terminal 20 is further configured to communicate with the base station 10 to obtain a base station coverage capability parameter of the base station.
As shown in fig. 3, a method for determining a signal propagation model provided in an embodiment of the present application includes the following steps:
s301, the server determines the RSRPs of the P places according to the mapping relation between the SINR and the RSRP in the coverage area of the base station and the SINR values of the P places in the coverage area of the base station.
Specifically, the server obtains SINR and RSRP of Q sites within the coverage area of the base station.
And the server performs fitting and normalized analysis on the SINR and the RSRP of the Q places to determine the mapping relation between the SINR and the RSRP in the coverage area of the base station.
And the server acquires SINRs of P places in the target area and determines the RSRP of the P places according to the mapping relation.
As one implementation manner, the server determines, according to the SINR and RSRP of Q locations, the distribution of Q points in the coordinate system, which are composed of the SINR and RSRP of the Q locations, in the coverage area of the base station, as shown in fig. 4.
As can be seen from fig. 4, the linear relationship between SINR and RSRP is close to a straight line. Therefore, in conjunction with fig. 4, the server determines the mapping relationship between SINR and RSRP according to the following formula (1).
Figure GDA0003590087490000081
Wherein x and y are parameter values to be determined.
The server respectively brings SINR and RSRP of Q places into formula (1), and the value of parameter x is determined to be x through fittingrThe value of y is yr。
The server sends xrAnd yrSubstituting into equation (1), determining the mapping relationship between SINR and RSRP may be performed by a first mapping function: RSRP ═ xr×SINR+yrAnd (4) showing.
And the server acquires SINR values of P places, and brings the SINR values into the first mapping function respectively to determine the RSRP of the P places.
S302, the server determines P measurement parameter sets according to the RSRP of the P places and the position information of the P places.
Wherein, the P measurement parameter sets are in one-to-one correspondence with P places of the coverage area of the base station. Each of the P sets of measurement parameters includes: a value of signal propagation distance and a value of measurement path loss; p is a positive integer.
S303, the server determines N fitting parameter sets according to the P measurement parameter sets.
Wherein the fitting parameter set comprises: the value of the path loss constant, the value of the attenuation coefficient, the value of the sample solution coefficient, and the value of the root mean square error, N being a positive integer.
The ith fitting parameter set of the N fitting parameter sets is described below. Wherein i is an integer of 0 or more and N or less.
The value A of the path loss constant in the ith set of fitting parametersiThe ith value in the value set of the path loss constant is the value set of the path loss constant, and the value set of the path loss constant comprises N different values.
The value m of the attenuation coefficient in the ith set of fitting parametersiAccording to AiAnd P sets of measurement parameters are determined in a fitting manner.
The values R of the sample coefficients in the ith set of fitting parametersi 2According to AiAnd miAnd (4) determining.
Numerical value RMSE of root mean square error in ith fitting parameter setiAccording to AiAnd miAnd (4) determining.
For the determination of Ri 2And RMSEiThe description will be specifically made.
(1) R in the ith fitting parameter seti 2According to the formula:
Figure GDA0003590087490000091
and (4) determining.
Wherein,
Figure GDA0003590087490000092
the average value of the values of the measured path loss in the P measurement parameter sets is taken; l isjThe value of the measured path loss in the jth measurement parameter set in the P measurement parameter sets is obtained;
Figure GDA0003590087490000093
according to the formula L ═ Ai+10×mi×log10d, and djA determined value of fitted path loss; djJ is more than or equal to 0 and less than or equal to N, and is the value of the signal propagation distance in the jth measurement parameter set.
(2) RMSE in the ith set of fitting parametersiAccording to the formula:
Figure GDA0003590087490000094
and (4) determining.
S304, the server determines a first fitting function and a second fitting function according to the N fitting parameter sets.
The dependent variable of the first fitting function is a sample coefficient, and the independent variable of the first fitting function is a path loss constant and an attenuation coefficient. The dependent variable of the second fitting function is the root mean square error, and the independent variables of the second fitting function are the path loss constant and the attenuation coefficient.
S305, the server determines a target value of the path loss constant and a target value of the attenuation coefficient according to the first fitting function and the second fitting function.
Wherein the target value of the path loss constant and the target value of the attenuation coefficient are used to maximize the dependent variable of the first fitting function and minimize the dependent variable of the second fitting function.
It should be appreciated that in fitting attenuation coefficients according to a path loss constant, the sample block coefficient is used to represent the goodness of fit of the fitted attenuation coefficient, and the root mean square error is used to represent the degree of dispersion of the fitted attenuation coefficient.
The larger the value of the sample coefficient, the smaller the root mean square error, and the better the fitting effect of the fitted attenuation coefficient. Therefore, when the dependent variable of the first fitting function takes the maximum value and the dependent variable of the second fitting function takes the minimum value, the values of the corresponding path loss constant and the attenuation coefficient are closest to the real values of the path loss constant and the attenuation coefficient of the signal propagation model in the coverage area of the base station.
S306, the server determines a signal propagation model according to the target value of the path loss constant and the target value of the attenuation coefficient.
Wherein, the signal propagation model is: l ═ ATarget+10×mTarget×log10d。
Wherein L is the path loss, ATargetIs the target value of the path loss constant, mTargetD is the signal propagation distance, which is the target value of the attenuation coefficient.
Based on the technical scheme, the influence of the terminal on other base stations and devices when measuring the RSRP is large, and the influence of the terminal on other base stations and devices when measuring the SINR is small, so that the SINR of P places is measured, the RSRP of the P places is determined according to the mapping relation between the SINR and the RSRP, and the influence of other base stations and devices on the collected RSRP is reduced. The server determines P measurement parameter sets according to the RSRP of the P places, and the P measurement parameter sets comprise signal propagation distance values and measurement path loss values and correspond to the P places of the coverage area of the base station one by one, so that the P measurement parameter sets can comprehensively reflect the signal propagation condition of the base station in the coverage area of the base station. Further, the P measurement parameter sets are subjected to mathematical analysis, and a target value of a path loss constant and a target value of an attenuation coefficient in the signal propagation model are determined. Thus, the target values of the path loss constants and the target values of the attenuation coefficients determined based on the P measurement parameter sets are accurate. Furthermore, the signal propagation model determined by the embodiment of the application has higher accuracy.
As a possible embodiment of the present application, based on the technical solution shown in fig. 3, as shown in fig. 5, S302 may be specifically implemented as:
s3021, the server obtains the base station coverage capability parameter of the base station coverage area and the position information of the P places.
Wherein, the base station coverage capability parameters include: base station location information, base station antenna gain, and base station antenna power.
The base station coverage capability parameter may be sent by the base station directly to the server, or may be sent by the base station to the server through a terminal or other device communicatively connected to the server.
As one implementation manner, the base station coverage capability parameter further includes: base station number and station height. The position information of the P places is measured by the terminal, and the position information of the P places also comprises the terminal height of the terminal.
Optionally, the server stores tables with preset formats, which are respectively shown in the following table 1 and table 2.
The server records the base station coverage capability parameters in table 1 as shown below.
TABLE 1
Figure GDA0003590087490000111
The server records the position information of the P points and the terminal height in table 2 shown below.
TABLE 2
Figure GDA0003590087490000112
In one implementation, the P pieces of location information and the P pieces of SINR values in step 301 may be obtained simultaneously, so as to greatly reduce the workload of terminal measurement.
Specifically, the terminal establishes and maintains UDP service connection with the base station.
The terminal moves on the main road in the coverage area of the base station and periodically (for example, one minute is a period) records the position information of the terminal and the detected SINR of the terminal.
And the terminal determines the P places and the SINR corresponding to each of the P places according to the position information and the SINR which are recorded periodically.
A table for recording SINR values is added to table 2 accordingly. The position information and the SINR value are simultaneously stored in said table 2.
TABLE 2
Figure GDA0003590087490000113
The server obtains SINR values corresponding to P locations, and then determines RSRPs corresponding to P locations according to the method described in step 301.
In another implementation manner, when the terminal disconnects the service connection with the target base station during the test, the terminal initiates the service connection to the target base station again. And when the change of the service connection rate of the terminal and the target base station in a preset time period is less than a preset threshold value, the terminal continues to carry out drive test.
S3022, the server determines P measurement parameter sets according to the base station coverage capability parameters of the base station coverage area, and the position information and the RSRP of P places.
The method specifically comprises the following steps: for the kth place in the P places, the server determines the signal propagation distance in the kth measurement parameter set corresponding to the kth place according to the position information of the base station and the position information of the kth place; and the server determines the measurement path loss in the kth measurement parameter set corresponding to the kth location according to the base station antenna gain, the base station antenna power and the RSRP of the kth location, wherein k is an integer which is greater than or equal to 1 and less than or equal to P.
In one implementation of S3022, for the kth site of the P sites, a corresponding kth measurement parameter set is determined. The server determines the signal propagation distance in the kth measurement parameter set according to the following mode I; the server determines the measured path loss in the kth set of measurement parameters according to the following manner two.
In the first mode, the server determines the signal propagation distance in the kth measurement parameter set according to the position of the base station, the height of the terminal and the position information of the kth location.
With reference to the schematic diagram of the distance between the terminal and the target base station shown in fig. 6, the determining, by the terminal, the distance between the terminal and the target base station may specifically be implemented as:
first, the server determines a horizontal distance between the base station and the terminal according to the following formula (2):
Figure GDA0003590087490000121
wherein d isk1Indicating a horizontal distance between the base station and the terminal determined according to the location information of the kth location; r is the radius of the earth (usually 6371.0 km); NBLOFor the dimension of the base station, NBLaAs the longitude of the location of the base station, UELOFor the dimension of the location of the terminal, UELaThe longitude of the location of the terminal.
Next, the server determines the signal propagation distance according to the following equation (3):
Figure GDA0003590087490000122
wherein d iskRepresenting the signal propagation distance in the kth set of measurement parameters; h isBSIndicating the height of the base station (i.e., the distance from the top of the base station to the ground); h isUEIndicating the height of the terminal (i.e., the distance of the terminal from the ground).
And secondly, the server determines the measurement path loss in the measurement parameter set according to the base station antenna power, the base station antenna gain and the RSRP of the base station.
Illustratively, the server determines the measured path loss according to equation (4) as follows:
Lk＝TX+Gain-RSRPkformula (4)
Wherein L iskRepresenting the measured path loss in the kth set of measurement parameters; TX denotes base station antenna power; gain denotes base station antenna Gain, RSRPkThe RSRP corresponding to the kth site.
The server determines the signal propagation distance d according to the first mode and the second modekAnd measuring the path loss LkAnd further determining a k-th measurement parameter set (d) corresponding to the k-th locationk，Lk)。
According to the method, the server determines P measurement parameter sets corresponding to P places: (d)1，L1)，(d2，L2)……(dk，Lk)……(dP，LP)。
Based on the technical scheme shown in fig. 5, the server determines a measurement parameter set in the coverage area of the base station according to the position information of P terminals measured by the terminal in the field of the coverage area of the base station and the RSRP corresponding to P places measured by the terminal. The signal propagation conditions reflected by the P measurement parameter sets are more consistent with the signal propagation conditions in the coverage area of the base station, so that the accuracy of the determined signal propagation model is improved.
As a possible embodiment of the present application, S303 may be specifically implemented as:
and the server inputs the P measurement parameter sets into the preset mathematical model as input parameters to obtain an output result of the preset mathematical model. The output result of the preset mathematical model is the N fitting parameter sets.
Wherein, the preset mathematical model is as follows:
For A＝x:y:z
f＝@(m，d3D)A+m*log10(d3D)；
p_fit＝nlinfit(d3D，L，f，1)；
PL_fit＝A+p_fit×log10(d3D)；
R2＝corrcoef(L，PL_fit)；
RMSEi＝sqrt(sum((PL-PLfit)2)/length(PL))；
once per cycle, the server records the calculated R2 iAnd RMSEiAnd corresponding AiAnd miTo obtain a fitting parameter set
Figure GDA0003590087490000131
End
Wherein the first step of the mathematical model "For a ═ x: y: z" represents: a path loss constant a is assigned. Each pair A is assigned once, and the subsequent second step to the seventh step are executed once. Wherein, the assignment process of A is as follows: defining the initial value of A as x, taking y as step length, gradually assigning the value of A, and ending the calculation process of the first model until the value of A is z.
If A is assigned to A as AiA of the AiI.e. the value of the path loss constant in the ith set of fitting parameters.
Exemplary x-5, y-0.1, and z-50.
When the server assigns the value to the A according to the first mathematical model, the value to the A is assigned to be 5, and the server executes the following second step to seventh step according to the condition that the value is 5. The server then assigns a value of 5.1 to a, performs … … the following second through seventh steps with a-5.1 for the server to increment the value of a by 0.1 each time, and performs the following second through seventh steps. And when the value of A is 50, executing the following second step to seventh step, ending the calculation process, and outputting the recorded 450 fitting parameter sets.
The second and third steps @ (m, d)3D)A+m*log10(d3D)；p_fit＝nlinfit(d3DL, f, 1); "means: according to the formula L ═ Ai+10×m×log10d, determining the value m of the value of the attenuation coefficient in the ith fitting parameter seti。
Wherein A isiThe value of L is the value of the path loss constant in the ith fitting parameter set, and the value of L comprises the value of the measured path loss in each measurement parameter set in the P measurement parameter sets; the value of d includes the value of the signal propagation distance in each of the P measurement parameter sets.
The fourth step "PL _ fit ═ a + p _ fit × log10 (d)3D) (ii) a "means: according to the value A of A determined in the first stepiAnd the value m of m determined in the second stepiDetermining a fitted signal propagation model as: l ═ Ai+10×mi×log10d. And bringing the d value in each measurement parameter set into a fitting signal propagation model, and determining the fitting values of P path losses corresponding to the fitting signal propagation model
Figure GDA0003590087490000141
Fifth step "R2＝corrcoef(L，PL_fit)；”Represents: according to the fitted values of P path losses determined in the fourth step
Figure GDA0003590087490000142
And values of measured path loss in the P sets of measurement parameters, determining a sample solution coefficient of the fitted signal propagation model as Ri 2。Ri 2The values of the coefficients may be determined for the samples in the ith set of fitting parameters.
Illustratively, the values of the sample block coefficients in the ith set of fitting parameters are determined by the following equation (5):
Figure GDA0003590087490000151
wherein,
Figure GDA0003590087490000152
the average value of L values in the P measurement parameter sets is obtained; l isjThe value of the measured path loss in the jth measurement parameter set in the P measurement parameter sets is obtained;
Figure GDA0003590087490000153
is according to L ═ Ai+10×mi×log10d, and djA determined value of fitted path loss; djIs the value of the signal propagation distance in the jth measurement parameter set.
Sixth step "RMSEi＝sqrt(sum((PL-PLfit)2) Length (PL); "means: according to the fitted values of P path losses determined in the fourth step
Figure GDA0003590087490000154
And the values of the measured path loss in the P measurement parameter sets, and determining the root mean square error of the fitted signal propagation model as RMSEi。RMSEiIs the value of the root mean square error in the ith set of fitting parameters.
Illustratively, the numerical value of the root mean square error in the ith set of fitting parameters is determined by the following equation (6):
Figure GDA0003590087490000155
wherein L isjThe value of the measured path loss in the jth measurement parameter set in the P measurement parameter sets is obtained;
Figure GDA0003590087490000156
according to the formula L ═ Ai+10×mi×log10d and djA determined fitted path loss; djIs the value of the signal propagation distance in the jth measurement parameter set.
The seventh step represents: the server is according to Ai、mi、Ri 2And RMSEiDetermining a set of fitting parameters
Figure GDA0003590087490000157
And the server repeatedly executes the first step to the seventh step until the value of A is equal to z.
As a possible embodiment of the present application, based on the technical solution described in fig. 5, as shown in fig. 7, S304 may be specifically implemented as:
s3041, the server determines N first subsets and N second subsets according to the N fitting parameter sets.
Each of the N fitting parameter sets corresponds to a first subset and a second subset. The first subset includes: the value of the path loss constant, the value of the attenuation coefficient, and the value of the sample block coefficient. The second subset includes: the value of the path loss constant, the value of the attenuation coefficient, and the value of the root mean square error.
Illustratively, the ith first subset of the N subsets is:
Figure GDA0003590087490000158
the ith second subset of the N subsets is: (A)i,mi,RMSEi)
S3042, the server determines a first fitting function according to the N first subsets.
In one implementation of S3042, the first fitting function is determined according to the following equation (7):
Figure GDA0003590087490000161
wherein R is2Is a sample block coefficient; a is a path loss constant; m is an attenuation coefficient; b. and c and d are parameter values to be determined.
Specifically, the server respectively compares the value of A, the value of m and the value of R in each first subset2Is substituted into the above equation (7), and the value b of the parameter b is determined by fitting in combination with the first fitted curve shown in FIG. 8rValue c of parameter crAnd the value d of the parameter dr。
The server sends br、crAnd drSubstituting into equation (7) above, a first fitting function is determined as: r is2＝br×A2+cr×A×m+dr。
br、cr、drIs a constant determined in a fitting manner based on the values of the path loss constant, the attenuation coefficient, and the sample block coefficient in the N fitting parameter sets.
S3043, the server determines a second fitting function according to the N second subsets.
In one implementation of S3043, the server determines the second fitting function according to the following equation (8):
Figure GDA0003590087490000162
wherein RMSE is the root mean square error; a is a path loss constant; m is an attenuation coefficient; e. f and g are parameter values to be determined.
The server respectively stores the corresponding A value in each second subset,The value of m and the value of RMSE are substituted into the above equation (8), and the value e of the determination parameter e is fitted in combination with a second fitted curve as shown in FIG. 9MValue f of parameter fMAnd the value g of the parameter gM。
The server will fit the determined eM、fMAnd gMSubstituting into equation (8) above, a second fitting function is determined as: RMSE ═ eM×A2+fM×A×m+gM。
Wherein e isM、fMAnd gMIs a constant determined in a fitting manner according to the values of the path loss constant, the attenuation coefficient and the root mean square error in the N fitting parameter sets.
It should be understood that, in the above implementation, the server fits the first fitting function and the second fitting function by the given equations (7) and (8), and the N fitting parameter sets determined in S303, and the mathematical relationship between the sample solution coefficient and the path loss constant and the attenuation coefficient, and the mathematical relationship between the root mean square error and the path loss constant and the attenuation coefficient may be accurately determined by the first fitting function and the second fitting function.
In one implementation of S305, the server determines values of a and m according to equation (9) as follows:
(A,m)＝max(R2) Andd min (RMSE) formula (9)
Wherein, max (R)2) Represents R2Maximum value, min (RMSE) means that RMSE takes minimum value, max (R)2) Andd min (RMSE) represents R2Take the maximum and RMSE the minimum.
In one implementation of S305, the server may determine the coefficient R for the sample2And the Root Mean Square Error (RMSE) is distributed with different adjusting parameters for further improving the accuracy of the finally determined signal propagation model, and the adjusting parameters are used for expressing the sample coefficient R2And the degree of influence of the root mean square error on the evaluation of the fitting results.
Illustratively, the server determines the values of a and m by equation (10) as shown below.
(A,m)＝max(sR×R2-sMXRMSE formula (10)
Wherein s isRIs R2The adjustment parameter of (2) is a constant of a fixed value; sMIs an adjustment parameter of the RMSE, and is a constant of a fixed value.
Exemplary, sR＝0.8，sM＝0.2。
In the embodiment of the present application, the determination device of the signal propagation model may be divided into functional modules or functional units according to the above method examples, for example, each functional module or functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module may be implemented in a form of hardware, or may be implemented in a form of a software functional module or a functional unit. The division of the modules or units in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
An embodiment of the present application provides an apparatus for determining a signal propagation model, as shown in fig. 10, the apparatus includes:
a processing unit 401 configured to determine P measurement parameter sets; wherein, P measurement parameter sets correspond to P places in the coverage area of the base station one by one, and the measurement parameter sets comprise: the value of the signal propagation distance and the value of the measured path loss, P being a positive integer.
The processing unit 401 is further configured to determine N fitting parameter sets according to the P measurement parameter sets, where the fitting parameter sets include: the value of the path loss constant, the value of the attenuation coefficient, the value of the sample solution coefficient, and the value of the root mean square error, N being a positive integer.
The processing unit 401 is further configured to determine a first fitting function and a second fitting function according to the N fitting parameter sets; the dependent variable of the first fitting function is a sample coefficient, and the independent variable of the first fitting function is a path loss constant and an attenuation coefficient; the dependent variable of the second fitting function is the root mean square error, and the independent variables of the second fitting function are the path loss constant and the attenuation coefficient.
The processing unit 401 is further configured to determine a target value of the path loss constant and a target value of the attenuation coefficient according to the first fitting function and the second fitting function.
The processing unit 401 is further configured to determine a signal propagation model according to a target value of the path loss constant and a target value of the attenuation coefficient.
Optionally, the signal propagation model is: l ═ ATarget+10×mTarget×log10d。
Wherein L is the path loss, ATargetIs the target value of the path loss constant, mTargetD is the signal propagation distance, which is the target value of the attenuation coefficient.
Optionally, the value a of the path loss constant in the ith fitting parameter setiThe ith numerical value in the value set of the path loss constant is obtained, and the value set of the path loss constant comprises N different numerical values; wherein i is more than or equal to 0 and less than or equal to N.
The value m of the attenuation coefficient in the ith set of fitting parametersiAccording to AiAnd the P sets of measured parameters are determined in a fitting manner.
The values R of the sample coefficients in the ith set of fitting parametersi 2According to AiAnd miAnd (4) determining.
Numerical value RMSE of root mean square error in ith fitting parameter setiAccording to AiAnd miAnd (4) determining.
Optionally, the values R of the sample block coefficients in the ith set of fitting parametersi 2According to AiAnd miThe determination comprises the following steps:
r in the ith set of fitting parametersi 2According to the formula:
Figure GDA0003590087490000181
and (4) determining.
Wherein,
Figure GDA0003590087490000182
is the average of the values of the measured path loss in the P sets of measurement parameters. L is a radical of an alcoholjThe measured path loss value in the jth measurement parameter set in the P measurement parameter sets is obtained.
Figure GDA0003590087490000183
According to the formula L ═ Ai+10×mi×log10d, and djA determined value of fitted path loss; djJ is more than or equal to 0 and less than or equal to N, and is the value of the signal propagation distance in the jth measurement parameter set.
Numerical value RMSE of root mean square error in ith fitting parameter setiAccording to AiAnd miThe determination comprises the following steps:
RMSE in the ith set of fitting parametersiAccording to the formula:
Figure GDA0003590087490000184
and (4) determining.
Optionally, the first fitting function is: r2＝br×A2+cr×A×m+dr。
Wherein R is2Is a sample block coefficient; a is a path loss constant; m is an attenuation coefficient; br、cr、drIs a constant determined in a fitting manner based on the values of the path loss constant, the attenuation coefficient, and the sample block coefficient in the N fitting parameter sets.
The second fitting function is: RMSE ═ eM×A2+fM×A×m+gM。
Wherein RMSE is the root mean square error; e.g. of the typeM、fMAnd gMThe constant is determined in a fitting mode according to the numerical value of the path loss constant, the numerical value of the attenuation coefficient and the numerical value of the root mean square error in the N fitting parameter sets.
Optionally, the target value of the path loss constant and the target value of the attenuation coefficient are used to make the dependent variable of the first fitting function take the maximum value, and the dependent variable of the second fitting function take the minimum value.
Based on the apparatus shown in fig. 10, as shown in fig. 11, the apparatus further includes a communication unit 402.
A communication unit 402, configured to obtain coverage capability parameters of a base station and location information of the P locations, where the coverage capability parameters include: base station location information, base station antenna gain, and base station antenna power.
For the kth location point of the P location points, the processing unit 401 is further configured to determine, according to the base station position information and the position information of the kth location point, a signal propagation distance in a kth measurement parameter set corresponding to the kth location point; and a processing unit 401, configured to determine, according to the base station antenna gain, the base station antenna power, and the RSRP at the kth location, a measurement path loss in a kth measurement parameter set corresponding to the kth location, where k is an integer greater than or equal to 1 and less than or equal to P.
When implemented by hardware, the communication unit 402 in the embodiment of the present application may be integrated on a communication interface, and the processing unit 401 may be integrated on a processor. The specific implementation is shown in fig. 12.
Fig. 12 shows a schematic diagram of still another possible structure of the apparatus for determining a signal propagation model according to the embodiment. The apparatus for determining the signal propagation model includes: a processor 502 and a communication interface 503. The processor 502 is configured to control and manage the actions of the signal propagation model determination device, for example, to perform the steps performed by the processing unit 401 described above, and/or to perform other processes for the techniques described herein. The communication interface 503 is used for supporting the communication between the apparatus for determining a signal propagation model and other network entities, for example, performing the steps performed by the communication unit 402. The means for determining a signal propagation model may further comprise a memory 501 and a bus 504, the memory 501 being arranged to store program code and data of the means for determining a signal propagation model.
The memory 501 may be a memory in the signal propagation model determination device, and the like, and the memory may include a volatile memory, such as a random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk; the memory may also comprise a combination of memories of the kind described above.
The processor 502 described above may be implemented or performed with the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor may be a central processing unit, general purpose processor, digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, among others.
The bus 504 may be an Extended Industry Standard Architecture (EISA) bus or the like. The bus 504 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 12, but this is not intended to represent only one bus or type of bus.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
The present application provides a computer program product containing instructions, which when run on a computer causes the computer to execute the method for determining a signal propagation model in the above method embodiments.
The present invention also provides a computer-readable storage medium, in which instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute a method for determining a signal propagation model in a method flow shown in the above method embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a register, a hard disk, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, any suitable combination of the above, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (9)

1. A method for determining a signal propagation model, the method comprising:
the server determines RSRPs of P places according to a mapping relation between signal to interference plus noise ratio (SINR) and received reference signal strength (RSRP) in a coverage area of the base station and SINR values of the P places in the coverage area of the base station;
the server determines P measurement parameter sets according to the RSRP of the P places and the position information of the P places; wherein, the P measurement parameter sets correspond to P sites in a coverage area of a base station one by one, and the measurement parameter sets include: a value of signal propagation distance and a value of measurement path loss, P being a positive integer;
the server determines N fitting parameter sets according to the P measurement parameter sets, wherein the fitting parameter sets comprise: the value of the path loss constant, the value of the attenuation coefficient, the value of the sample solution coefficient, and the value of the root mean square error, where N is a positive integer;
the server determines a first fitting function and a second fitting function according to the N fitting parameter sets; wherein the dependent variable of the first fitting function is the sample block coefficient, and the independent variable of the first fitting function is the path loss constant and the attenuation coefficient; the dependent variable of the second fitting function is the root mean square error, and the independent variable of the second fitting function is the path loss constant and the attenuation coefficient;
the server determines a target value of the path loss constant and a target value of the attenuation coefficient according to the first fitting function and the second fitting function;
the server determines a signal propagation model according to the target value of the path loss constant and the target value of the attenuation coefficient;
wherein the value A of the path loss constant in the ith set of fitting parametersiThe ith numerical value in the value set of the path loss constant is obtained, and the value set of the path loss constant comprises N different numerical values; wherein i is more than or equal to 0 and less than or equal to N;
the value m of the attenuation coefficient in the ith set of fitting parametersiAccording to AiAnd the P sets of measurement parameters are determined in a fitting manner;
the values R of the sample coefficients in the ith set of fitting parametersi 2According to the formula:
Figure FDA0003590087480000011
determining;
wherein,
Figure FDA0003590087480000012
is the average of the values of the measured path losses in the P sets of measurement parameters; l isjThe value of the measured path loss in the jth measurement parameter set in the P measurement parameter sets is obtained;
Figure FDA0003590087480000013
according to the formula L ═ Ai+10×mi×log10d, and djA determined value of fitted path loss; d is saidjJ is more than or equal to 0 and less than or equal to N, and is the value of the signal propagation distance in the jth measurement parameter set;
root mean square error value RMSE in ith fitting parameter setiAccording to the formula:
Figure FDA0003590087480000021
and (4) determining.
2. The method of determining a signal propagation model of claim 1, wherein the signal propagation model is: l ═ ATarget+10×mTarget×log10d；
Wherein L is the path loss, ATargetIs a target value of the path loss constant, mTargetD is the signal propagation distance, which is the target value of the attenuation coefficient.
3. A method of determining a signal propagation model according to claim 1 or 2, characterized in that the first fitting function is: r2＝br×A2+cr×A×m+dr；
Wherein R is2Determining a block coefficient for the sample; a is the path loss constant; m is the attenuation coefficient; br、cr、drA constant determined in a fitting manner according to the values of the path loss constant, the attenuation coefficient and the sample solution coefficient in the N fitting parameter sets;
the second fitting function is: RMSE ═ eM×A2+fM×A×m+gM；
Wherein RMSE is the root mean square error; e.g. of the typeM、fMAnd gMThe constant is determined in a fitting mode according to the numerical value of the path loss constant, the numerical value of the attenuation coefficient and the numerical value of the root mean square error in the N fitting parameter sets.
4. A method of determining a signal propagation model according to claim 3, wherein the target value for the path loss constant and the target value for the attenuation coefficient are used to maximize the dependent variable of the first fitting function and minimize the dependent variable of the second fitting function.
5. The method of determining the signal propagation model of claim 4, wherein the server determines P sets of measurement parameters based on the RSRP of the P sites and the location information of the P sites, comprising:
for a k place in the P places, the server determines a signal propagation distance in a k measurement parameter set corresponding to the k place according to the base station position information and the position information of the k place; and the server determines the measurement path loss in the kth measurement parameter set corresponding to the kth location according to the base station antenna gain, the base station antenna power and the RSRP at the kth location, wherein k is an integer greater than or equal to 1 and less than or equal to P.
6. An apparatus for determining a signal propagation model, the apparatus comprising:
the processing unit is used for determining the RSRPs of P places according to the mapping relation between the signal to interference plus noise ratio (SINR) in the coverage area of the base station and the received reference signal strength (RSRP) and SINR values of the P places in the coverage area of the base station;
the processing unit is further configured to determine P measurement parameter sets according to the RSRPs of the P sites; wherein, the P measurement parameter sets correspond to P sites in a coverage area of a base station one by one, and the measurement parameter sets include: a value of signal propagation distance and a value of measurement path loss, P being a positive integer;
the processing unit is further configured to determine N fitting parameter sets according to the P measurement parameter sets, where the fitting parameter sets include: a value of a path loss constant, a value of an attenuation coefficient, a value of a sample solution coefficient, and a value of a root mean square error, N being a positive integer;
the processing unit is further configured to determine a first fitting function and a second fitting function according to the N fitting parameter sets; wherein the dependent variable of the first fitting function is the sample-dependent coefficient, and the independent variable of the first fitting function is the path loss constant and the attenuation coefficient; the dependent variable of the second fitting function is the root mean square error, and the independent variable of the second fitting function is the path loss constant and the attenuation coefficient;
the processing unit is further configured to determine a target value of the path loss constant and a target value of the attenuation coefficient according to the first fitting function and the second fitting function;
the processing unit is further configured to determine a signal propagation model according to the target value of the path loss constant and the target value of the attenuation coefficient;
wherein the value A of the path loss constant in the ith set of fitting parametersiThe ith numerical value in the value set of the path loss constant is obtained, and the value set of the path loss constant comprises N different numerical values; wherein i is more than or equal to 0 and less than or equal to N;
the attenuation in the ith set of fitting parametersValue m of coefficientiAccording to AiAnd the P sets of measurement parameters are determined in a fitting manner;
the values R of the sample coefficients in the ith set of fitting parametersi 2According to the formula:
Figure FDA0003590087480000031
determining;
wherein,
Figure FDA0003590087480000032
is the average of the values of the measured path losses in the P sets of measurement parameters; l is a radical of an alcoholjThe value of the measured path loss in the jth measurement parameter set in the P measurement parameter sets is obtained;
Figure FDA0003590087480000033
according to the formula L ═ Ai+10×mi×log10d, and djA determined value of fitted path loss; d isjJ is more than or equal to 0 and less than or equal to N, and is the value of the signal propagation distance in the jth measurement parameter set;
RMSE value of root mean square error in ith fitting parameter setiAccording to the formula:
Figure FDA0003590087480000041
and (4) determining.
7. The apparatus for determining a signal propagation model according to claim 6, wherein the first fitting function is: r2＝br×A2+cr×A×m+dr；
Wherein R is2Determining a block coefficient for the sample; a is the path loss constant; m is the attenuation coefficient; br、cr、drA constant determined in a fitting manner according to the values of the path loss constant, the attenuation coefficient and the sample solution coefficient in the N fitting parameter sets;
the second fitting function is: RMSE ═ eM×A2+fM×A×m+gM；
Wherein RMSE is the root mean square error; e.g. of the typeM、fMAnd gMThe constant is determined in a fitting mode according to the numerical value of the path loss constant, the numerical value of the attenuation coefficient and the numerical value of the root mean square error in the N fitting parameter sets.
8. An apparatus for determining a signal propagation model, comprising: a processor and a communication interface; the communication interface is coupled to the processor, which is configured to execute a computer program or instructions to implement the method of determining a signal propagation model according to any of claims 1-5.
9. A computer-readable storage medium having instructions stored thereon, wherein the instructions, when executed by a computer, cause the computer to perform the method of determining a signal propagation model of any of claims 1 to 5.
CN201910828985.XA 2019-09-03 2019-09-03 Method and device for determining signal propagation model Active CN110636516B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910828985.XA CN110636516B (en) 2019-09-03 2019-09-03 Method and device for determining signal propagation model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910828985.XA CN110636516B (en) 2019-09-03 2019-09-03 Method and device for determining signal propagation model

Publications (2)

Publication Number Publication Date
CN110636516A CN110636516A (en) 2019-12-31
CN110636516B true CN110636516B (en) 2022-06-07

Family

ID=68970850

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910828985.XA Active CN110636516B (en) 2019-09-03 2019-09-03 Method and device for determining signal propagation model

Country Status (1)

Country Link
CN (1) CN110636516B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113068208B (en) * 2020-01-02 2022-12-23 中国移动通信集团广东有限公司 Training method of SPM (statistical processing System) combined model, wireless signal simulation method and server
CN112462354B (en) * 2021-02-02 2021-04-23 浙江三维通信科技有限公司 Positioning method based on single base station, electronic device and storage medium
CN112929916B (en) * 2021-03-19 2023-04-07 中国联合网络通信集团有限公司 Method and device for constructing wireless propagation model
CN113438658B (en) * 2021-06-22 2022-12-23 中国联合网络通信集团有限公司 Method and device for determining coverage area of base station

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1759326A (en) * 2003-03-11 2006-04-12 皇家飞利浦电子股份有限公司 Multiple-input multiple-output communication system
CN101146312A (en) * 2007-10-17 2008-03-19 中兴通讯股份有限公司 An automatic selection method for radio transmission model
US7929375B2 (en) * 2007-06-26 2011-04-19 The United States Of America As Represented By The Secretary Of The Navy Method and apparatus for improved active sonar using singular value decomposition filtering
CN102118761A (en) * 2009-12-30 2011-07-06 中兴通讯股份有限公司 Method and device for correcting propagation model
WO2011160190A1 (en) * 2010-06-24 2011-12-29 Cohda Wireless Pty Ltd Estimation of a multipath signal in a wireless communication system
CN102665273A (en) * 2012-04-19 2012-09-12 中国科学技术大学苏州研究院 Wireless sensor network node positioning method
CN103592624A (en) * 2013-11-22 2014-02-19 中国人民解放军理工大学 Distance measuring method based on strength of received signal
CN106127590A (en) * 2016-06-21 2016-11-16 重庆邮电大学 A kind of information Situation Awareness based on node power of influence and propagation management and control model
CN108151835A (en) * 2017-12-27 2018-06-12 河海大学常州校区 A kind of rating method of the pumping plant information intelligent monitoring terminal based on multi-parameter detection
CN109068332A (en) * 2018-09-26 2018-12-21 中国联合网络通信集团有限公司 Data transmission method and device

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1759326A (en) * 2003-03-11 2006-04-12 皇家飞利浦电子股份有限公司 Multiple-input multiple-output communication system
US7929375B2 (en) * 2007-06-26 2011-04-19 The United States Of America As Represented By The Secretary Of The Navy Method and apparatus for improved active sonar using singular value decomposition filtering
CN101146312A (en) * 2007-10-17 2008-03-19 中兴通讯股份有限公司 An automatic selection method for radio transmission model
CN102118761A (en) * 2009-12-30 2011-07-06 中兴通讯股份有限公司 Method and device for correcting propagation model
WO2011160190A1 (en) * 2010-06-24 2011-12-29 Cohda Wireless Pty Ltd Estimation of a multipath signal in a wireless communication system
CN102665273A (en) * 2012-04-19 2012-09-12 中国科学技术大学苏州研究院 Wireless sensor network node positioning method
CN103592624A (en) * 2013-11-22 2014-02-19 中国人民解放军理工大学 Distance measuring method based on strength of received signal
CN106127590A (en) * 2016-06-21 2016-11-16 重庆邮电大学 A kind of information Situation Awareness based on node power of influence and propagation management and control model
CN108151835A (en) * 2017-12-27 2018-06-12 河海大学常州校区 A kind of rating method of the pumping plant information intelligent monitoring terminal based on multi-parameter detection
CN109068332A (en) * 2018-09-26 2018-12-21 中国联合网络通信集团有限公司 Data transmission method and device

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering;Elizaveta Rastorgueva-Foi;《 2018 21st International Conference on Information Fusion (FUSION)》;20180906;全文 *
无线传感器网络中基于RSS和DOA融合的三维定位算法;王浩;《现代计算机(专业版)》;20170215;全文 *

Also Published As

Publication number Publication date
CN110636516A (en) 2019-12-31

Similar Documents

Publication Publication Date Title
CN110636516B (en) Method and device for determining signal propagation model
US11635484B2 (en) Frequency transformed radiomap data set
JP4934441B2 (en) Wireless node position estimation method, system, and processing apparatus therefor
CN112218330B (en) Positioning method and communication device
WO2012032376A1 (en) Signal strength profiling
US10444319B2 (en) Determining of model parameters for positioning purposes
KR20170060129A (en) Positioning method and system for wireless communication networks
CN108259097B (en) Method and device for correcting wireless propagation model based on MR data
Wölfle et al. Enhanced localization technique within urban and indoor environments based on accurate and fast propagation models
CN110708702B (en) Method and device for determining signal propagation model
US20170013474A1 (en) Frequency assignment device, frequency assignment method and wireless communication system
Bernardin et al. Cell radius inaccuracy: a new measure of coverage reliability
JP7016303B2 (en) Radiation power estimation method
Podevijn et al. Performance comparison of RSS algorithms for indoor localization in large open environments
CN110856100B (en) Method and device for terminal positioning and positioning model construction based on 5G signal
CN109150263A (en) A kind of method and device that the three dimensional channel based on Multi probe darkroom is rebuild
JP2018032939A (en) Quality estimation device and quality estimation method
CN114448531B (en) Channel characteristic analysis method, system, medium, equipment and processing terminal
CN101924594B (en) Method for judging radio testing environment under the condition of external field
CN108093414B (en) Method and device for evaluating coverage effectiveness of cell
Wang et al. A novel indoor ranging method using weighted altofrequent RSSI measurements
US20140323151A1 (en) Distance estimation
CN108419248B (en) Test data processing method and device
TWI511481B (en) Method for speeding up the total isotropic sensitivity measurement on mobile terminals
CN104635206A (en) Wireless positioning method and device

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