CN109150263B - Three-dimensional channel reconstruction method and device based on multi-probe darkroom - Google Patents
Three-dimensional channel reconstruction method and device based on multi-probe darkroom Download PDFInfo
- Publication number
- CN109150263B CN109150263B CN201811361585.4A CN201811361585A CN109150263B CN 109150263 B CN109150263 B CN 109150263B CN 201811361585 A CN201811361585 A CN 201811361585A CN 109150263 B CN109150263 B CN 109150263B
- Authority
- CN
- China
- Prior art keywords
- reconstructed
- dimensional channel
- probe
- sampling time
- time point
- 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
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
-
- 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
- H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
Abstract
The embodiment of the invention provides a three-dimensional channel reconstruction method and a three-dimensional channel reconstruction device based on a multi-probe darkroom, wherein the method comprises the following steps: determining an angle power spectrum and a first spatial correlation corresponding to the signal angle information of each sampling time point; solving a weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed; determining a second spatial correlation according to a preset second spatial correlation calculation formula; determining a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula; and if the correlation coefficient meets the preset condition, determining the three-dimensional channel model to be reconstructed based on the weight value of each probe in the three-dimensional channel model to be reconstructed. Therefore, in the embodiment of the invention, a set of implementation flows suitable for the three-dimensional spherical channel model equipment scheme is summarized to improve the simulation effect of the three-dimensional spherical channel model.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a method and a device for reconstructing a three-dimensional channel based on a multi-probe darkroom.
Background
In recent years, in application scenarios such as user mobile communication, living room, outdoor entertainment center, and the like, the business forms are changing and developing, but the final access mode of the user is biased to use wireless technology without exception. The traditional wireless terminal generally utilizes an Over-The-Air (OTA) performance test to perform wireless transmission and networking performance evaluation, and a reflection-free space is established through a anechoic chamber to evaluate The radio frequency of The wireless terminal and The overall performance of an antenna. In the fourth and fifth generation mobile communication systems, a Multiple-Input Multiple-Output (MIMO) antenna technology has been clarified as a core key technology to improve the spectrum efficiency of the network. Since MIMO performance is closely dependent on channel environment, it is difficult for the conventional anechoic chamber to meet its requirements. Therefore, for MIMO, the reconstruction of a three-dimensional real channel scene in an anechoic chamber is an important reference for theoretical research and implementation.
However, the current MIMO OTA (Multiple-Input Multiple-Output Over-the-Air) channel reconstruction scheme still mainly simplifies a three-dimensional actual channel model into two dimensions, only considers delay, doppler and horizontal plane azimuth components of a two-dimensional plane, but ignores spatial domain information in a vertical dimension, and the statistical property of angle information only has an azimuth component but not an elevation component, and cannot truly reflect an actual channel, and is not suitable for the performance test and evaluation of the current MIMO terminal.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for reconstructing a three-dimensional channel based on a multi-probe darkroom so as to improve the simulation effect of a three-dimensional spherical channel model.
The specific technical scheme is as follows:
in a first aspect, a multi-probe darkroom-based three-dimensional channel reconstruction method is provided, and the method comprises the following steps:
detecting signal angle information of a three-dimensional channel to be reconstructed at each sampling time point, wherein the signal angle information can comprise vertical dimension angle information and horizontal dimension angle information of the three-dimensional channel to be reconstructed at each sampling time point;
determining an angle power spectrum corresponding to the signal angle information according to a preset angle power spectrum calculation formula;
determining a first spatial correlation corresponding to the signal angle information according to a preset first spatial correlation calculation formula and the angle power spectrum;
forming a weight constraint equation set by using the first spatial correlation of the signal angle information of each sampling time point and a preset second spatial correlation calculation formula;
solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed;
determining a second spatial correlation according to a preset second spatial correlation calculation formula and a weight value of each probe in the three-dimensional channel model to be reconstructed;
determining a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula;
and if the correlation coefficient meets a preset condition, determining the three-dimensional channel model to be reconstructed based on the weight value of each probe in the three-dimensional channel model to be reconstructed.
Optionally, the step of detecting signal angle information of the three-dimensional channel to be reconstructed at each sampling time point may include:
establishing a three-dimensional coordinate system by taking the moving equipment to be tested as an origin based on the simulation scene of the channel to be reconstructed;
and determining the signal angle information of the tested equipment at each sampling time point according to the motion speed and the motion trail of the tested equipment in the simulated scene of the channel to be reconstructed.
Optionally, the step of solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed may include:
judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at a uniform variable speed or not;
if the tested equipment of the three-dimensional channel to be reconstructed moves at a uniform variable speed, determining signal angle information corresponding to sampling time points of fixed time slots and signal angle information corresponding to each sampling time point between the fixed time slots;
solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a first weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot;
and solving the weight constraint equation set according to a preset trend surface smooth difference algorithm to obtain a second weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
Optionally, the step of solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed may include:
judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed or not;
if the tested equipment in the three-dimensional channel to be reconstructed does non-uniform variable speed motion, determining signal angle information corresponding to a sampling time point of a fixed time slot and signal angle information corresponding to each adopted time point between the fixed time slots;
solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a third weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot;
and solving the weight constraint equation set according to a preset spline interpolation algorithm to obtain a fourth weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
In a second aspect, a multi-probe darkroom based three-dimensional channel reconstruction apparatus is provided, the apparatus comprising:
the detection module is used for detecting signal angle information of the three-dimensional channel to be reconstructed at each sampling time point, wherein the signal angle information can comprise vertical dimension angle information and horizontal dimension angle information of the three-dimensional channel to be reconstructed at each sampling time point;
the angle power spectrum determination module is used for determining an angle power spectrum corresponding to the signal angle information according to a preset angle power spectrum calculation formula;
the first spatial correlation determination module is used for determining first spatial correlation corresponding to the signal angle information according to a preset first spatial correlation calculation formula and the angle power spectrum;
the weight constraint equation set forming module is used for forming a weight constraint equation set by the first spatial correlation of the signal angle information of each sampling time point and a preset second spatial correlation calculation formula;
the weighted value obtaining module is used for solving the weighted constraint equation set according to a preset convex optimization algorithm to obtain a weighted value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed;
the second spatial correlation determination module is used for determining second spatial correlation according to a preset second spatial correlation calculation formula and the weight value of each probe in the three-dimensional channel model to be reconstructed;
a correlation coefficient determining module, configured to determine a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula;
and the to-be-reconstructed three-dimensional channel model determining module is used for determining the to-be-reconstructed three-dimensional channel model based on the weight value of each probe in the to-be-reconstructed three-dimensional channel model if the correlation coefficient meets the preset condition.
Optionally, the detection module may include:
the three-dimensional coordinate system establishing submodule is used for establishing a three-dimensional coordinate system by taking the moving tested equipment as an origin based on the simulation scene of the channel to be reconstructed;
and the signal angle information determining submodule is used for determining the signal angle information of the tested equipment at each sampling time point according to the motion speed and the motion track of the tested equipment in the simulated scene of the channel to be reconstructed.
Optionally, the weight value obtaining module includes:
the first judgment submodule is used for judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at a uniform variable speed or not;
a signal angle information determination submodule between first fixed time slots, configured to determine, if the device under test of the three-dimensional channel to be reconstructed is in uniform variable speed motion, signal angle information corresponding to a sampling time point of a fixed time slot and signal angle information corresponding to each sampling time point between fixed time slots;
the first weight value obtaining submodule is used for solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a first weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot;
and the second weight value obtaining submodule is used for solving the weight constraint equation set according to a preset trend surface smooth difference algorithm to obtain a second weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
Optionally, the weight value obtaining module includes:
the second judgment submodule is used for judging whether the equipment to be detected in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed or not;
a second fixed time slot signal angle information determining submodule, configured to determine, if the device to be tested in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed, signal angle information corresponding to a sampling time point of a fixed time slot and signal angle information corresponding to each sampling time point between the fixed time slots;
the third weight value determining submodule is used for solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a third weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot;
and the fourth weight value determining submodule is used for solving the weight constraint equation set according to a preset spline interpolation algorithm to obtain a fourth weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
The embodiment of the invention provides a method and a device for reconstructing a three-dimensional dynamic channel based on a multi-probe darkroom, which are characterized in that signal angle information of a three-dimensional channel to be reconstructed at each sampling time point is detected; determining an angle power spectrum and a first spatial correlation corresponding to the signal angle information according to a preset angle power spectrum calculation formula and a first spatial correlation calculation formula; forming a weight constraint equation set by using a first spatial correlation of the signal angle information of each sampling time point and a preset second spatial correlation calculation formula; solving a weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed; determining a second spatial correlation according to a preset second spatial correlation calculation formula and a weight value of each probe in the three-dimensional channel model to be reconstructed; determining a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula; and if the correlation coefficient meets the preset condition, determining the three-dimensional channel model to be reconstructed based on the weight value of each probe in the three-dimensional channel model to be reconstructed. Therefore, in the embodiment of the invention, the three-dimensional channel model to be reconstructed can be used for reconstructing the three-dimensional dynamic channel so as to improve the simulation effect of the three-dimensional spherical channel model.
Of course, it is not necessary for any product or method of practicing the invention to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic top view of a three-dimensional spherical channel model test area according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a three-dimensional channel reconstruction method based on a multi-probe darkroom according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a three-dimensional coordinate system for simulating a high-speed rail in a scene according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a three-dimensional channel reconstruction apparatus based on a multi-probe darkroom according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a multi-probe darkroom-based three-dimensional channel reconstruction method and a multi-probe darkroom-based three-dimensional channel reconstruction device, aiming at improving the simulation effect of a three-dimensional spherical channel model, and the following detailed description is respectively given.
First, a three-dimensional channel reconstruction method based on a multi-probe darkroom provided by the embodiment of the present invention is described below.
The execution main body of the multi-probe darkroom-based three-dimensional channel reconstruction method provided by the embodiment of the invention can be equipment for simulating a three-dimensional channel.
The channel may be a transmission channel of a signal in a practical application scenario. The signal transmission channel corresponding to the dynamically changing application scenario may be referred to as a three-dimensional dynamic channel. When a signal sent by a base station is transmitted in a three-dimensional dynamic channel, the three-dimensional dynamic channel can perform a series of attenuation and phase change on the transmitted signal; different three-dimensional dynamic channels have different attenuations and phase changes of signals transmitted in the channels, so that signals received by terminal equipment in actual dynamic application scenes are different.
For example, when a user uses a mobile phone or other terminal device (such as iPad) on a high-speed rail, the external environment where a train in motion passes is an application scene that changes every moment. Due to the fact that attenuation and phase of signals transmitted in the dynamic application scene are continuously changed, the terminal equipment used by a user on a high-speed rail can receive the signals sometimes, and cannot receive the signals sometimes.
The method for reconstructing the three-dimensional channel based on the multi-probe darkroom provided by the embodiment of the invention is used for reconstructing the three-dimensional dynamic channel, and simulating a certain three-dimensional dynamic channel through a three-dimensional channel multi-probe model. Wherein, the three-dimensional channel multi-probe model can contain the pre-probeThe method comprises the steps of setting a plurality of probes, wherein each probe comprises a virtual antenna pair, and each probe is provided with a detection component simulating a three-dimensional channel. In the embodiment of the invention, all probes in the three-dimensional channel model to be reconstructed are distributed on a spherical model with a cubic annular probe sequence according to preset positions. Wherein, the central detection area of the spherical model is provided with the terminal device to be detected, as shown in fig. 1. Therefore, the probes in the spherical model can be used as transmitting antennas, and the actual signal angles of different directions and different attenuations of the terminal equipment in the three-dimensional channel to be reconstructed are simulated through the mutual position relation of the probes. Wherein, A in FIG. 1ivAnd AjHRepresenting different probes in the spherical model, Δ θ is the azimuth angle between the two probes.
Referring to fig. 2, the three-dimensional channel reconstruction method based on a multi-probe darkroom provided by the embodiment of the present invention specifically includes the following steps:
s201, detecting signal angle information of the three-dimensional channel to be reconstructed at each sampling time point.
The signal angle information may include vertical dimension angle information and horizontal dimension angle information of the three-dimensional channel to be reconstructed at each sampling time point.
In implementation, a three-dimensional channel to be reconstructed affects the transmission angle of a signal, and when the signal is transmitted in the three-dimensional channel, not only vertical dimension angle information but also horizontal dimension angle information exists. The angle information of the signal which is received by the terminal equipment and is attenuated by the three-dimensional channel is called as the signal angle information of the three-dimensional channel to be reconstructed. In practical application, the arrival angle information of the signal of the terminal device in a specific application scene changes along with the motion state and the position, so that the device for simulating the three-dimensional channel in the embodiment of the invention detects the signal angle information of the three-dimensional channel to be reconstructed at each sampling time point, thereby realizing proper reduction of the calculation amount of the simulation device. Optionally, the step of detecting, by the device for simulating the three-dimensional channel, signal angle information of the three-dimensional channel to be reconstructed at each sampling time point may include the following steps:
the method comprises the following steps: and establishing a three-dimensional coordinate system by taking the moving equipment to be tested as an origin based on the simulation scene of the channel to be reconstructed.
In implementation, based on the simulation scene of the three-dimensional channel to be reconstructed, the device for simulating the three-dimensional channel uses the terminal device to be tested in the simulation scene of the three-dimensional channel to be reconstructed as the origin of the three-dimensional coordinate system, and establishes the three-dimensional coordinate system. For example, assuming that the simulation scene of the three-dimensional channel to be reconstructed is an external environment where a high-speed rail is located, and a mobile phone device used by a user on the high-speed rail is a device to be tested, a three-dimensional coordinate system is established with the mobile phone device of the user as an origin, as shown in fig. 3. Where l in fig. 3 is the length of the rail and v is the speed of movement of the high-speed rail.
Step two: and determining the signal angle information of the tested equipment at each sampling time point according to the motion speed and the motion track of the tested equipment in the simulated scene of the channel to be reconstructed.
During implementation, a designer uses a preset movement speed and a preset movement track of the tested equipment as the movement speed and the movement track of the tested equipment in a simulation scene of a channel to be reconstructed according to an actual experience value; and determining the signal angle information of the tested equipment at each preset sampling time point through the three-dimensional coordinate system established in the first step.
S202, determining an angle power spectrum corresponding to the signal angle information according to a preset angle power spectrum calculation formula.
In implementation, after the device simulating the three-dimensional channel detects the signal angle information of the three-dimensional channel to be reconstructed at each sampling time point in step S201, the device simulating the three-dimensional channel may determine the angle power spectrum corresponding to the signal angle information at each sampling time point according to a preset angle power spectrum calculation formula. Since the signal angle information includes the vertical dimension angle information and the horizontal dimension angle information of the three-dimensional channel to be reconstructed at each sampling time point, the process of solving the angle power spectrum corresponding to the signal angle information by using the step is to respectively consider the angle power spectrum corresponding to the vertical dimension angle information of each sampling time point and the angle power spectrum corresponding to the horizontal dimension angle information of each sampling time point.
In the embodiment of the present invention, the angle power spectrum corresponding to the signal angle information may be calculated by using the following formula:
wherein the content of the first and second substances,
is an angle
Angle power spectrum, independent variable angle
Has a value range of [ -pi, pi [ -pi [ ]]β is a normalization coefficient, sigma is an angle diffusion parameter,
it may be vertical dimension angle information of each sampling time point or horizontal dimension angle information of each sampling time point.
S203, determining a first spatial correlation corresponding to the signal angle information according to a preset first spatial correlation calculation formula and an angle power spectrum.
In implementation, after the angle power spectrum corresponding to the signal angle information determined in step S202, the obtained angle power spectrum is substituted into a preset first spatial correlation calculation formula, so as to determine the first spatial correlation corresponding to the signal angle information.
In the embodiment of the present invention, the following formula may be adopted to calculate the first spatial correlation corresponding to the signal angle information:
where ρ isTargetIs the first spatial correlation, λ is the wavelength, and PAS (φ) is the angular power corresponding to the angular information of the signalSpectrum, dVAPIs the distance between the pair of virtual antennas of the probe, phi is the signal angle information, phiaThe included angle between the direct direction of the probe and the horizontal direction is shown.
And S204, forming a weight constraint equation set by using the first spatial correlation of the signal angle information of each sampling time point and a preset second spatial correlation calculation formula.
In implementation, in the three-dimensional channel model to be reconstructed, the antenna probes discretely distributed at each preset position, and the spatial correlation simulated in the test region of the three-dimensional spherical channel model in the embodiment of the present invention is referred to as a second spatial correlation.
In the embodiment of the present invention, the following formula may be adopted to calculate the second spatial correlation:
where ρ isOTA(dVAP，φa) For the second spatial correlation, i is each number of probes in the three-dimensional channel model to be reconstructed, and the value of i can be 1,2, …, K, phiaThe angle between the perpendicular direction of the probe and the horizontal direction, omegaiFor the weight, phi, of each probe in the three-dimensional channel model to be reconstructediIs the azimuth angle of the ith probe.
The absolute value error of the second spatial correlation and the first spatial correlation is calculated, so as to determine a weight constraint equation set, which may be specifically expressed as the following formula:
||ω||1＝1，0≤ωi≥1
in implementation, the signal angle information in the three-dimensional channel to be reconstructed is time-varying, and the signal angle information not only contains vertical dimension angle information, but also contains horizontal dimension angle information; therefore, in the embodiment of the present invention, the weight constraint equation set needs to be solved for the vertical dimension angle information and the horizontal dimension angle information, respectively, and the weight of each probe in the three-dimensional channel model to be reconstructed in the vertical dimension direction and the horizontal dimension direction is solved, so as to simulate the signal angle information received by the terminal device in the actual three-dimensional channel.
S205, solving a weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed.
In implementation, the three-dimensional dynamic channel to be reconstructed can be distinguished as follows: in a three-dimensional channel to be reconstructed, whether the tested equipment moves at a uniform variable speed or a non-uniform variable speed; and solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value of each probe in the three-dimensional channel model to be reconstructed at each sampling time point.
The embodiment of the invention particularly provides two methods for solving the weight value of each probe in a three-dimensional channel model to be reconstructed at each sampling time point.
Firstly, a weight constraint equation set can be solved according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed, and the method specifically includes the following steps:
the method comprises the following steps: and judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at a uniform variable speed or not.
In the implementation, whether the device to be tested in the three-dimensional channel to be reconstructed moves at a uniform speed or not can be judged according to the movement speed and the movement track of the device to be tested in the simulation scene of the channel to be reconstructed in step S101; if the tested equipment of the three-dimensional channel to be reconstructed moves at a uniform variable speed, solving the weight value of each probe in the three-dimensional channel model to be reconstructed corresponding to each sampling time point by adopting the first method provided by the embodiment of the invention; if the tested equipment of the three-dimensional channel to be reconstructed does not move at a uniform variable speed, solving the weight value of each probe in the three-dimensional channel model to be reconstructed corresponding to each sampling time point by adopting the second method provided by the embodiment of the invention.
Step two: and if the tested equipment of the three-dimensional channel to be reconstructed moves at a uniform variable speed, determining the signal angle information corresponding to the sampling time point of the fixed time slot and the signal angle information corresponding to each sampling time point between the fixed time slots.
In implementation, a fixed time slot may be preset according to practical experience, for example, the fixed time slot may be 10 seconds; then, the signal angle information of all the sampling time points detected in the above steps is selected regularly according to a preset fixed time slot, for example, 1 second is used as a sampling time point, and the detected signal angle information is an array
Then, determining the signal angle information corresponding to the fixed time slot sampling time point, namely determining the signal angle information corresponding to the sampling time points of the first second, the eleventh second and the twenty-first second at intervals of 10 seconds; similarly, the signal angle information corresponding to the time point between the first second and the eleventh second which is the sampling time point is the signal angle information corresponding to each sampling time point between the fixed time slots.
Step three: and solving a weight constraint equation set according to a preset convex optimization algorithm to obtain a first weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot.
In implementation, the signal angle information corresponding to the sampling time point of the fixed time slot determined in the step one is substituted into a step S204 to form a weight constraint equation set by using a first spatial correlation and a preset second spatial correlation calculation formula of the signal angle information of each sampling time point, and a first weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot is solved.
Step four: and solving a weight constraint equation set according to a preset trend surface smooth difference algorithm to obtain a second weight value corresponding to each sampling time point between fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
In implementation, when the equipment to be tested of the three-dimensional channel to be reconstructed performs uniform variable speed motion, as long as the fixed time slot is reasonably selected, the second weight values corresponding to the adopted time points of each probe in the three-dimensional channel model to be reconstructed between the fixed time slots have a certain rule; therefore, the second weight value corresponding to each adopted time point between the fixed time slots of each probe can be solved by adopting a preset trend surface smooth difference algorithm; then, detecting whether a second weight value corresponding to each sampling time point between fixed time slots of each probe in the three-dimensional channel model to be reconstructed, which is solved by the preset trend surface smooth difference algorithm, is correct through test statistics in the preset trend surface smooth difference algorithm, and if the second weight value is incorrect, re-taking the value of the fixed time slot preset in the step two; generally, the fixed time slot is reduced to half of the preset fixed time slot, and then the step of determining the signal angle information corresponding to the sampling time point of the fixed time slot and the signal angle information corresponding to each sampling time point between the fixed time slots is executed in the step two; and if the second weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed, which is solved by adopting the preset trend surface smooth difference algorithm, is detected to be correct through the test statistic in the preset trend surface smooth difference algorithm, determining the second weight value as the weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
In the embodiment of the present invention, the following formula may be adopted to calculate the second weight value corresponding to each sampling time point between fixed time slots of each probe in the three-dimensional channel model to be reconstructed:
Zx,y＝b0+b1x+b2y+b3x2+b2xy
+b5y2+b6x3+b7x2y+b8xy2+b9y3
wherein Z isx,yIs a second weight value, x and y areCoordinate angle information value of signal angle information, biFor coefficients, i can take the value 0,1,2,3,4,5,6,7,8, 9.
Fitting the data points according to the principle of a least square method, testing the fitting degree of the trend surface, and testing by using F distribution as in the multivariate regression analysis, wherein the test statistic is as follows:
wherein U is regression sum of squares, Q is residual sum of squares, and P is polynomial Zx,yNumber of terms (but not including constant b)0) And n is the number of raw points used.
When F is present>At 0.9, the trend surface fit is significant, indicating that the functional relationship Z is utilizedx,yObtaining that a second weight value corresponding to each sampling time point between fixed time slots of each probe in the three-dimensional channel model to be reconstructed is correct; when F is less than or equal to 0.9, the trend surface fitting is not obvious, and the functional relation Z is usedx,yAnd if the second weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed is incorrect, reducing the second weight value to half of the initial preset fixed time slot to be re-valued, and re-calculating the weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
Secondly, solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed, which specifically includes the following steps:
the method comprises the following steps: and judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at non-uniform variable speed or not.
As for determining whether the device under test in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed, the same steps as the above method for determining whether the device under test in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed may be referred to, and are not described herein again.
Step two: and if the tested equipment in the three-dimensional channel to be reconstructed does non-uniform variable speed motion, determining signal angle information corresponding to the sampling time points of the fixed time slots and signal angle information corresponding to each sampling time point between the fixed time slots.
In the method for determining the signal angle information corresponding to the sampling time point of the fixed time slot and the signal angle information corresponding to each sampling time point between the fixed time slots in this step, the steps of determining the signal angle information corresponding to the sampling time point of the fixed time slot and the signal angle information corresponding to each sampling time point between the fixed time slots are the same as those of the method for determining the weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed in the first step, and are not repeated here.
Step three: and solving a weight constraint equation set according to a preset convex optimization algorithm to obtain a third weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot.
In this step, according to a preset convex optimization algorithm, a weight constraint equation set is solved to obtain a third weight value corresponding to a sampling time point of each probe in the three-dimensional channel model to be reconstructed at a fixed time slot, and reference may be made to step three in the first method for determining a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed, which is not described herein again.
Step four: and solving a weight constraint equation set according to a preset spline interpolation algorithm to obtain a fourth weight value corresponding to each sampling time point between fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
In implementation, when the device to be tested in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed, solving the fourth weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed cannot be a uniform calculation formula, so as to solve the fourth weight value corresponding to each sampling time point between the fixed time slots, and then, respectively solving the fourth weight value corresponding to each sampling time point between each fixed time slot, specifically, calculating the fourth weight value corresponding to each sampling time point between each fixed time slot by using the following formula:
assuming that the calculation function of the fourth weight value corresponding to each sampling time point between each fixed time slot is s (x), then s (x) the second derivative values at 0 to n sampling time points of the fixed time slot are:
S″i(xi)＝Mj
then in the interval xj-1,xj]Above, S ″)j(xi) Is obviously a linear function, and knowing:
S″i(xi-1)＝Mj-1
S″i(xi)＝Mj
it is obvious that each interval S ″, can be obtained by linear interpolationi(xi) Is used as a linear expression of (1). Integrating twice to obtain Sj(x) A fourth weight value corresponding to each sampling time point between the fixed time slots can be obtained.
Further, the operations are performed on each sampling time point between the next section of fixed time slot, and a fourth weight value corresponding to each sampling time point between different time slots is obtained through local fitting.
S206, determining second spatial correlation according to a preset second spatial correlation calculation formula and the weight value of each probe in the three-dimensional channel model to be reconstructed.
In implementation, the fourth weight value corresponding to each sampling time point between fixed time slots of each probe in the three-dimensional channel model to be reconstructed determined in step S206 may be substituted into the second spatial correlation calculation formula provided in step S204 to calculate the second spatial correlation.
And S207, determining a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula.
In practice, the calculation formula of the correlation coefficient may be expressed as the following formula:
wherein, X is the second spatial correlation, Y is the first spatial correlation, Var [ X ] is the variance of X, Var [ Y ] is the variance of Y, Cov (X, Y) is X, the covariance of Y, i is the number of the adopted time points, and the value of i can be 1,2, …, n.
And S208, if the correlation coefficient meets the preset condition, determining the three-dimensional channel model to be reconstructed based on the weight value of each probe in the three-dimensional channel model to be reconstructed.
Wherein the preset condition is that a threshold value of a correlation coefficient between the first spatial correlation and the second spatial correlation is 0.8.
In implementation, if the correlation coefficient r (X, Y) between the first spatial correlation and the second spatial correlation in step S207 is less than 0.8, and the correlation coefficient does not satisfy the preset condition, step S205 is executed to reduce the preset initial fixed time slot to one half, and recalculate the weight value of each probe in the three-dimensional channel model to be reconstructed; if the correlation coefficient r (X, Y) between the first spatial correlation and the second spatial correlation in step S207 is greater than or equal to 0.8, the correlation coefficient satisfies a preset condition, and the weight value of each probe in the three-dimensional channel model to be reconstructed calculated in step S205 is determined as the weight value of each probe in the three-dimensional channel model to be reconstructed.
The embodiment of the invention provides a method and a device for reconstructing a three-dimensional channel based on a multi-probe darkroom, which are characterized in that signal angle information of a three-dimensional channel to be reconstructed at each sampling time point is detected; determining an angle power spectrum and a first spatial correlation corresponding to the signal angle information according to a preset angle power spectrum calculation formula and a first spatial correlation calculation formula; forming a weight constraint equation set by using a first spatial correlation of the signal angle information of each sampling time point and a preset second spatial correlation calculation formula; solving a weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed; determining a second spatial correlation according to a preset second spatial correlation calculation formula and a weight value of each probe in the three-dimensional channel model to be reconstructed; determining a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula; and if the correlation coefficient meets the preset condition, determining the three-dimensional channel model to be reconstructed based on the weight value of each probe in the three-dimensional channel model to be reconstructed. Therefore, in the embodiment of the invention, a set of implementation flows suitable for the three-dimensional spherical channel model equipment scheme is summarized to improve the simulation effect of the three-dimensional spherical channel model.
Corresponding to the embodiment of the method shown in fig. 2, fig. 4 is a schematic structural diagram of an apparatus for three-dimensional channel reconstruction based on a multi-probe darkroom according to an embodiment of the present invention, where the apparatus may include:
a detection module 401, configured to detect signal angle information of a three-dimensional channel to be reconstructed at each sampling time point;
an angle power spectrum determining module 402, configured to determine an angle power spectrum corresponding to the signal angle information according to a preset angle power spectrum calculation formula;
a first spatial correlation determining module 403, configured to determine a first spatial correlation corresponding to the signal angle information according to a preset first spatial correlation calculation formula and the angle power spectrum;
a weight constraint equation set forming module 404, configured to form a weight constraint equation set by using a first spatial correlation of the signal angle information at each sampling time point and a preset second spatial correlation calculation formula;
a weight value obtaining module 405, configured to solve a weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed;
a second spatial correlation determination module 406, configured to determine a second spatial correlation according to a preset second spatial correlation calculation formula and a weight value of each probe in the three-dimensional channel model to be reconstructed;
the correlation coefficient determining module is used for determining a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula;
and a to-be-reconstructed three-dimensional channel model determining module 406, configured to determine, if the correlation coefficient meets a preset condition, a to-be-reconstructed three-dimensional channel model based on a weight value of each probe in the to-be-reconstructed three-dimensional channel model.
In the embodiment of the present invention, the signal angle information may include vertical dimension angle information and horizontal dimension angle information of the three-dimensional channel to be reconstructed at each sampling time point.
In an embodiment of the present invention, the detecting module may include:
the three-dimensional coordinate system establishing submodule is used for establishing a three-dimensional coordinate system by taking the moving tested equipment as an origin based on the simulation scene of the channel to be reconstructed;
and the signal angle information determining submodule is used for determining the signal angle information of the tested equipment at each sampling time point according to the motion speed and the motion track of the tested equipment in the simulated scene of the channel to be reconstructed.
In an embodiment of the present invention, the weight value obtaining module may include:
the first judgment submodule is used for judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at a uniform speed or not;
the signal angle information determining submodule between the first fixed time slots is used for determining signal angle information corresponding to the sampling time points of the fixed time slots and signal angle information corresponding to each sampling time point between the fixed time slots if the tested equipment of the three-dimensional channel to be reconstructed moves at a uniform variable speed;
the first weight value obtaining submodule is used for solving a weight constraint equation set according to a preset convex optimization algorithm to obtain a first weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at a fixed time slot;
and the second weight value obtaining submodule is used for solving a weight constraint equation set according to a preset trend surface smooth difference algorithm to obtain a second weight value corresponding to each sampling time point between fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
In an embodiment of the present invention, the weight value obtaining module may include:
the second judgment submodule is used for judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed or not;
a second fixed time slot signal angle information determining submodule, configured to determine, if the device to be tested in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed, signal angle information corresponding to the sampling time point of the fixed time slot and signal angle information corresponding to each sampling time point between the fixed time slots;
the third weight value determining submodule is used for solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a third weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at a fixed time slot;
and the fourth weight value determining submodule is used for solving a weight constraint equation set according to a preset spline interpolation algorithm to obtain a fourth weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed between fixed time slots.
For specific implementation and related explanation of each step of the method, reference may be made to the method embodiment shown in fig. 2, which is not described herein again.
The embodiment of the invention provides a device for reconstructing a three-dimensional channel based on a multi-probe darkroom, which is characterized in that signal angle information of a three-dimensional channel to be reconstructed at each sampling time point is detected; determining an angle power spectrum and a first spatial correlation corresponding to the signal angle information according to a preset angle power spectrum calculation formula and a first spatial correlation calculation formula; forming a weight constraint equation set by using a first spatial correlation of the signal angle information of each sampling time point and a preset second spatial correlation calculation formula; solving a weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed; determining a second spatial correlation according to a preset second spatial correlation calculation formula and a weight value of each probe in the three-dimensional channel model to be reconstructed; determining a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula; and if the correlation coefficient meets the preset condition, determining the three-dimensional channel model to be reconstructed based on the weight value of each probe in the three-dimensional channel model to be reconstructed. Therefore, in the embodiment of the invention, a set of implementation flows suitable for the three-dimensional spherical channel model equipment scheme is summarized to improve the simulation effect of the three-dimensional spherical channel model.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component. In yet another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, which, when being executed by a processor, implements the steps of any one of the above-mentioned multi-probe darkroom-based three-dimensional channel reconstruction methods.
In yet another embodiment, a computer program product containing instructions is provided, which when run on a computer, causes the computer to perform any of the above-described multi-probe darkroom based three-dimensional channel reconstruction methods.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (4)
1. A three-dimensional channel reconstruction method based on a multi-probe darkroom is characterized by comprising the following steps:
detecting signal angle information of a three-dimensional channel to be reconstructed at each sampling time point, wherein the signal angle information comprises vertical dimension angle information and horizontal dimension angle information of the three-dimensional channel to be reconstructed at each sampling time point;
determining an angle power spectrum corresponding to the signal angle information according to a preset angle power spectrum calculation formula;
determining a first spatial correlation corresponding to the signal angle information according to a preset first spatial correlation calculation formula and the angle power spectrum;
forming a weight constraint equation set by using the first spatial correlation of the signal angle information of each sampling time point and a preset second spatial correlation calculation formula;
solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed;
determining a second spatial correlation according to a preset second spatial correlation calculation formula and a weight value of each probe in the three-dimensional channel model to be reconstructed;
determining a correlation coefficient between the first spatial correlation and the second spatial correlation according to a preset correlation coefficient calculation formula;
if the correlation coefficient meets a preset condition, determining the three-dimensional channel model to be reconstructed based on the weight value of each probe in the three-dimensional channel model to be reconstructed;
wherein, the step of forming a weight constraint equation set by using the first spatial correlation of the signal angle information at each sampling time point and a preset second spatial correlation calculation formula includes:
calculating a first spatial correlation corresponding to the signal angle information by adopting the following formula:
where ρ isTargetIs the first spatial correlation, λ is the wavelength, PAS (φ) is the angular power spectrum corresponding to the angular information of the signal, dVAPIs the distance between the pair of virtual antennas of the probe, phi is the signal angle information, phiaThe included angle between the direct projection direction of the probe and the horizontal direction is formed;
the second spatial correlation is calculated using the following formula:
where ρ isOTA(dVAP，φa) For the second spatial correlation, i is the number of probes in the three-dimensional channel model to be reconstructed, and the value of i is 1,2, …, K, omegaiFor the weight, phi, of each probe in the three-dimensional channel model to be reconstructediAzimuth angle of the ith probe;
solving an absolute value error of the second spatial correlation and the first spatial correlation, and determining a weight constraint equation set, which is specifically expressed as the following formula:
||ω||1＝1，0≤ωi≤1。
2. the method of claim 1, wherein the step of detecting the signal angle information of the three-dimensional channel to be reconstructed at each sampling time point comprises:
establishing a three-dimensional coordinate system by taking the moving equipment to be tested as an origin based on the simulation scene of the channel to be reconstructed;
and determining the signal angle information of the tested equipment at each sampling time point according to the motion speed and the motion trail of the tested equipment in the simulated scene of the channel to be reconstructed.
3. The method according to claim 1, wherein the step of solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed comprises:
judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at a uniform variable speed or not;
if the tested equipment of the three-dimensional channel to be reconstructed moves at a uniform variable speed, determining signal angle information corresponding to sampling time points of fixed time slots and signal angle information corresponding to each sampling time point between the fixed time slots;
solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a first weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot;
and solving the weight constraint equation set according to a preset trend surface smooth difference algorithm to obtain a second weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
4. The method according to claim 1, wherein the step of solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a weight value corresponding to each sampling time point of each probe in the three-dimensional channel model to be reconstructed comprises:
judging whether the tested equipment in the three-dimensional channel to be reconstructed moves at a non-uniform variable speed or not;
if the tested equipment in the three-dimensional channel to be reconstructed does non-uniform variable speed motion, determining signal angle information corresponding to sampling time points of fixed time slots and signal angle information corresponding to each sampling time point between the fixed time slots;
solving the weight constraint equation set according to a preset convex optimization algorithm to obtain a third weight value corresponding to the sampling time point of each probe in the three-dimensional channel model to be reconstructed at the fixed time slot;
and solving the weight constraint equation set according to a preset spline interpolation algorithm to obtain a fourth weight value corresponding to each sampling time point between the fixed time slots of each probe in the three-dimensional channel model to be reconstructed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811361585.4A CN109150263B (en) | 2018-11-15 | 2018-11-15 | Three-dimensional channel reconstruction method and device based on multi-probe darkroom |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811361585.4A CN109150263B (en) | 2018-11-15 | 2018-11-15 | Three-dimensional channel reconstruction method and device based on multi-probe darkroom |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109150263A CN109150263A (en) | 2019-01-04 |
| CN109150263B true CN109150263B (en) | 2020-09-01 |
Family
ID=64805859
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811361585.4A Active CN109150263B (en) | 2018-11-15 | 2018-11-15 | Three-dimensional channel reconstruction method and device based on multi-probe darkroom |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109150263B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110086514B (en) * | 2019-03-08 | 2020-11-13 | 中国科学院大学 | Signal synthesis method, device and system |
| CN112235823B (en) * | 2020-10-14 | 2023-01-03 | 东南大学 | Three-dimensional air interface test probe selection method based on cultural genetic algorithm |
| CN115085777A (en) * | 2021-03-16 | 2022-09-20 | 中国信息通信研究院 | OTA dynamic channel implementation method based on probe weight fitting |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8493981B2 (en) * | 2010-11-03 | 2013-07-23 | Broadcom Corporation | Switch module |
| US20170222708A1 (en) * | 2014-10-07 | 2017-08-03 | Lg Electronics Inc. | Feedback reporting method for 3d beamforming in wireless communication system, and apparatus therefor |
| CN104601209B (en) * | 2015-01-26 | 2017-12-15 | 西安电子科技大学 | A kind of cooperative multi-point transmission method suitable for 3D mimo systems |
| CN107683571B (en) * | 2015-06-07 | 2022-05-31 | Lg电子株式会社 | Channel measuring method in wireless communication system and apparatus thereof |
| CN105406979B (en) * | 2015-10-12 | 2019-01-08 | 中国人民解放军空军航空大学 | Improved threedimensional model network transfer method |
| CN106209284B (en) * | 2016-07-07 | 2018-10-16 | 北京邮电大学 | A kind of creation method and device of MIMO OTA channels |
-
2018
- 2018-11-15 CN CN201811361585.4A patent/CN109150263B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109150263A (en) | 2019-01-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109150263B (en) | Three-dimensional channel reconstruction method and device based on multi-probe darkroom | |
| US20170134899A1 (en) | Mitigating signal noise for fingerprint-based indoor localization | |
| CN112218330B (en) | Positioning method and communication device | |
| CN106851821B (en) | Indoor three-dimensional positioning method based on wireless communication base station | |
| CN110996328B (en) | Unmanned aerial vehicle base station deployment position determining method and device and electronic equipment | |
| JP6059635B2 (en) | Received power estimation apparatus and received power estimation method | |
| CN110636516B (en) | Method and device for determining signal propagation model | |
| CN110350988A (en) | The OTA Performance Test System of intelligent wearable device under disturbed condition | |
| JP2013205398A (en) | Sending source estimation method and sending source estimation apparatus utilizing the same | |
| Priebe et al. | Calibrated broadband ray tracing for the simulation of wave propagation in mm and sub-mm wave indoor communication channels | |
| Rumney et al. | Recent advances in the radiated two-stage MIMO OTA test method and its value for antenna design optimization | |
| CN108828570B (en) | Distance measurement method and distance measurement device based on path loss factor dynamic estimation | |
| CN111313987A (en) | Neural network-based path loss prediction method suitable for smart home | |
| Deme et al. | Hata-okumura model computer analysis for path loss determination at 900mhz for maiduguri, nigeria | |
| CN108495265B (en) | Indoor positioning method and device and computing equipment | |
| JP5809616B2 (en) | Received power estimation apparatus, received power estimation method, and program | |
| CN111711966B (en) | Channel simulation method and device | |
| CN110708702A (en) | Method and device for determining signal propagation model | |
| Loh et al. | Intelligent base station placement in urban areas with machine learning | |
| CN117176265B (en) | Scene channel simulation playback method, system and equipment | |
| Glazunov et al. | A spherical probability distribution model of the user-induced mobile phone orientation | |
| Navarro et al. | Delay spread in mmwave bands for indoor using Game Engines 3D Ray based Tools | |
| Yu et al. | Modelling and simulation of channel power delay profile under indoor stair environment | |
| Choi et al. | A Realistic Path Loss Model for Cell-Free Massive MIMO in Urban Environments | |
| CN117148398B (en) | Two-network-integrated station distribution geometric configuration assessment method, system and equipment |
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 |