CN115694696A - Channel modeling method and device, storage medium and electronic device - Google Patents
Channel modeling method and device, storage medium and electronic device Download PDFInfo
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Abstract
The embodiment of the application provides a channel modeling method, a channel modeling device, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring outfield measurement data of a plurality of sampling points when the outfield communication equipment receives signals to obtain a plurality of outfield measurement data; respectively extracting the characteristics of the external field measurement data to obtain the channel characteristics of the sampling points; model regression processing is carried out according to the channel characteristics of the plurality of sampling points, the problem that a larger deviation is generated due to a large difference between a specific channel and a classical scene or a variance inside the classical scene when a channel in a specific scene needs to be simulated in the related technology can be solved, the deviation between a general channel model based on empirical data and actual measurement channels of different external fields is avoided, meanwhile, measurement data automatically generated in the working process of a communication system can be directly used, and extra measurement and expenditure are avoided.
Description
Technical Field
The embodiment of the application relates to the field of communication, in particular to a channel modeling method, a channel modeling device, a storage medium and an electronic device.
Background
The modeling method commonly used for wireless channel modeling at present is a method for deriving a multipath scattering model from typical scene measurement data. Such as the channel model TR38.901 commonly used in 5G systems. The method comprises the steps of firstly measuring and collecting wireless channel characteristics by using special wireless signals in some typical scenes, summarizing the wireless channel characteristics into different multipath time domain and space domain distribution characteristics after data analysis, and calculating corresponding parameters. When the channel model is used, a required scene is selected, and then the model under the corresponding scene is generated by adopting the previously calculated parameters. The method has the disadvantages that only a certain class of classical scenes can be used for generalization research, and once a channel in a specific scene needs to be simulated, a large deviation can be generated due to the large difference between the specific channel and the classical scenes or the variance inside the classical scenes.
In the related art, a solution is not proposed yet to solve the problem that a channel in a specific scene needs to be simulated, and a large deviation is generated due to a large difference between the specific channel and a classical scene or a variance inside the classical scene.
Disclosure of Invention
The embodiment of the application provides a channel modeling method, a channel modeling device, a storage medium and an electronic device, which are used for at least solving the problem that a specific channel and a classical scene have large difference or a variance inside the classical scene generates large deviation when the channel in a specific scene needs to be simulated in the related technology.
According to an embodiment of the present application, there is provided a channel modeling method including:
acquiring outfield measurement data of a plurality of sampling points when the outfield communication equipment receives signals to obtain a plurality of outfield measurement data;
respectively extracting the characteristics of the external field measurement data to obtain the channel characteristics of the sampling points;
and performing model regression processing according to the channel characteristics of the plurality of sampling points.
In an exemplary embodiment, performing the model regression process according to the channel characteristics of the plurality of sampling points includes:
performing statistical model regression or network model regression according to the channel characteristics of the plurality of sampling points, and outputting the channel characteristics of all the sampling points;
and inputting the channel characteristics of all sampling points into a multipath channel model as channel parameters to obtain a simulated channel of any point output by the multipath channel model.
In an exemplary embodiment, the performing feature extraction on the external field measurement data respectively to obtain the channel features of the sampling points includes:
performing the following operations on each external field measurement data in the plurality of external field measurement data to obtain the channel characteristics of the plurality of sampling points, wherein the external field measurement data being performed is referred to as current external field measurement data:
determining the number of multipath paths in the channel according to the current external field measurement data;
pre-estimating the time delay of the multi-path according to the path number of the channel;
calculating the time domain response of each path from the current external field measurement data according to the time delay of the multipath, and estimating the average power of the multipath according to the time domain response of each path;
clustering the multipaths according to the correlation among the multipaths in a channel to obtain a plurality of target paths;
and determining the arrival angles of the multiple target paths and the polarization characteristics of the multipath, wherein the polarization characteristics are the correlation among channels in different polarization directions, and the channel characteristics comprise the time delay of the multipath, the average power of the multipath, the arrival angle of the multipath and the polarization characteristics of the multipath.
In an exemplary embodiment, clustering the multipaths according to correlations between the multipaths in a channel to obtain a plurality of target paths includes:
calculating the correlation of the channels among the multi-paths;
clustering the multi-paths according to the correlation to obtain a multi-cluster set, wherein each cluster set comprises at least one path;
and respectively selecting the target path with the maximum average power from the multi-cluster set to obtain the plurality of target paths.
In an exemplary embodiment, obtaining the external field measurement data of the external field communication device receiving the signal at the plurality of sampling points, and obtaining the plurality of external field measurement data includes:
acquiring measurement data of an external field channel when the external field communication equipment of the plurality of sampling points receives signals;
and respectively screening the plurality of external field measurement data meeting preset conditions from the measurement data of the plurality of sampling points.
In an exemplary embodiment, the obtaining of the measurement data of the external field channel when the external field communication device of the plurality of sampling points receives the signal includes:
performing channel estimation when the external field communication equipment receives signals to obtain a wireless channel estimation value;
and acquiring quality tag data of the wireless channel estimation value and network tag data of the wireless channel estimation value, wherein the measurement data comprises the wireless channel estimation value, the quality tag data of the wireless channel estimation value and the grid tag data of the wireless channel estimation value.
In an exemplary embodiment, the step of respectively screening the external field measurement data meeting a preset condition from the measurement data of the sampling points comprises:
judging whether the measurement data of the plurality of sampling points meet preset conditions or not according to the quality label data of the wireless channel estimation value;
and deleting the data which do not meet the preset condition from the measurement data of the plurality of sampling points respectively to obtain the plurality of external field measurement data which meet the preset condition.
In an exemplary embodiment, the determining whether the measurement data of the plurality of sampling points satisfy a preset condition according to the quality label data of the wireless channel estimation value includes:
respectively judging whether the SINRs of the measurement data of the plurality of sampling points are greater than a first preset threshold, whether the Doppler frequency shift is smaller than a second preset threshold and whether the motion speed estimation value is smaller than a third preset threshold under the condition that the quality label data of the wireless channel estimation value comprises the SINR, the Doppler frequency shift and the motion speed estimation value of the current received signal;
if the judgment result is yes, determining that the preset condition is met;
and under the condition that the judgment result is negative, determining that the preset condition is not met.
According to another embodiment of the present application, there is also provided a channel modeling apparatus including:
the acquisition module is used for acquiring the outfield measurement data of the outfield communication equipment with a plurality of sampling points when the outfield communication equipment receives signals to obtain a plurality of outfield measurement data;
the characteristic extraction module is used for respectively extracting the characteristics of the external field measurement data to obtain the channel characteristics of the sampling points;
and the processing module is used for performing model regression processing according to the channel characteristics of the plurality of sampling points.
In an exemplary embodiment, the processing module includes:
the output sub-module is used for performing statistical model regression or network model regression according to the channel characteristics of the plurality of sampling points and outputting the channel characteristics of all the sampling points;
and the input sub-module is used for inputting the channel characteristics of all the sampling points into a multipath channel model as channel parameters to obtain a simulated channel of any point output by the multipath channel model.
In an exemplary embodiment, the feature extraction module includes:
the execution sub-module is used for executing the following operations on each external field measurement data in the plurality of external field measurement data to obtain the channel characteristics of the plurality of sampling points, wherein the external field measurement data being executed is called current external field measurement data:
determining the number of multipath paths in the channel according to the current external field measurement data;
pre-estimating the time delay of the multi-path according to the path number of the channel;
calculating the time domain response of each path from the current external field measurement data according to the time delay of the multipath, and estimating the average power of the multipath according to the time domain response of each path;
clustering the multipaths according to the correlation among the multipaths in a channel to obtain a plurality of target paths;
and determining the arrival angles of the multiple target paths and the polarization characteristics of the multipath, wherein the polarization characteristics are the correlation among channels in different polarization directions, and the channel characteristics comprise the time delay of the multipath, the average power of the multipath, the arrival angle of the multipath and the polarization characteristics of the multipath.
In an exemplary embodiment, the execution submodule is further configured to
Calculating the correlation of the channels among the multi-paths;
clustering the multi-paths according to the correlation to obtain a multi-cluster set, wherein each cluster set comprises at least one path;
and selecting the target path with the maximum average power from the multi-cluster set respectively to obtain the multiple target paths.
In an exemplary embodiment, the obtaining module includes:
the acquisition submodule is used for acquiring the measurement data of the external field channel when the external field communication equipment of the plurality of sampling points receives signals;
and the screening submodule is used for screening the plurality of external field measurement data meeting the preset conditions from the measurement data of the plurality of sampling points respectively.
In an exemplary embodiment, the obtaining sub-module is further configured to
Performing channel estimation when the external field communication equipment receives signals to obtain a wireless channel estimation value;
and acquiring quality tag data of the wireless channel estimation value and network tag data of the wireless channel estimation value, wherein the measurement data comprises the wireless channel estimation value, the quality tag data of the wireless channel estimation value and the grid tag data of the wireless channel estimation value.
In an exemplary embodiment, the screening submodule includes:
the judging unit is used for judging whether the measurement data of the plurality of sampling points meet preset conditions or not according to the quality label data of the wireless channel estimation value;
and the deleting unit is used for deleting the data which do not meet the preset condition from the measured data of the plurality of sampling points respectively to obtain the plurality of external field measured data which meet the preset condition.
In an exemplary embodiment, the determining unit is further configured to
Under the condition that the quality label data of the wireless channel estimation value comprises the signal to interference plus noise ratio, the Doppler frequency shift and the motion speed estimation value of a current receiving signal, respectively judging whether the signal to interference plus noise ratio of the measurement data of the plurality of sampling points is larger than a first preset threshold value, whether the Doppler frequency shift is smaller than a second preset threshold value and whether the motion speed estimation value is smaller than a third preset threshold value;
if the judgment result is yes, determining that the preset condition is met;
and under the condition that the judgment result is negative, determining that the preset condition is not met.
According to a further embodiment of the application, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.
According to yet another embodiment of the present application, there is also provided an electronic device, comprising a memory having a computer program stored therein and a processor configured to run the computer program to perform the steps of any of the method embodiments described above.
According to the method and the device, the outfield measurement data of the outfield communication equipment with a plurality of sampling points when the outfield communication equipment receives signals are obtained, and a plurality of outfield measurement data are obtained; respectively extracting the characteristics of the external field measurement data to obtain the channel characteristics of the sampling points; model regression processing is carried out according to the channel characteristics of the plurality of sampling points, the problem that a larger deviation is generated due to a large difference between a specific channel and a classical scene or a variance inside the classical scene when a channel in a specific scene needs to be simulated in the related technology can be solved, the deviation between a general channel model based on empirical data and actual measurement channels of different external fields is avoided, meanwhile, measurement data automatically generated in the working process of a communication system can be directly used, and extra measurement and expenditure are avoided.
Drawings
Fig. 1 is a block diagram of a hardware structure of a mobile terminal of a channel modeling method according to an embodiment of the present application;
FIG. 2 is a flow chart of a channel modeling method according to an embodiment of the present application;
fig. 3 is a block diagram of a channel construction system according to the present embodiment;
fig. 4 is a block diagram of a channel modeling apparatus according to the present embodiment;
FIG. 5 is a block diagram one of the channel modeling apparatus according to the preferred embodiment;
FIG. 6 is a block diagram two of the channel modeling apparatus according to the preferred embodiment;
fig. 7 is a block diagram three of the channel modeling apparatus according to the present preferred embodiment.
Detailed Description
Embodiments of the present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking a mobile terminal as an example, fig. 1 is a hardware structure block diagram of a mobile terminal of the channel modeling method according to the embodiment of the present application, and as shown in fig. 1, the mobile terminal may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, where the mobile terminal may further include a transmission device 106 for communication function and an input/output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to the channel modeling method in the embodiment of the present application, and the processor 102 executes the computer programs stored in the memory 104 to execute various functional applications and service chain address pool slicing processes, i.e., to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In this embodiment, a channel modeling method operating in the mobile terminal or the network architecture is provided, and fig. 2 is a flowchart of the channel modeling method according to the embodiment of the present application, as shown in fig. 2, the flowchart includes the following steps:
step S202, obtaining outfield measurement data of the outfield communication equipment with a plurality of sampling points when the outfield communication equipment receives signals, and obtaining a plurality of outfield measurement data;
step S204, respectively carrying out feature extraction on the external field measurement data to obtain channel features of the sampling points;
and step S206, performing model regression processing according to the channel characteristics of the plurality of sampling points.
In this embodiment, the step S206 may specifically include: performing statistical model regression or network model regression according to the channel characteristics of the plurality of sampling points, and outputting the channel characteristics of all the sampling points; and inputting the channel characteristics of all sampling points into a multipath channel model as channel parameters to obtain a simulated channel of any point output by the multipath channel model.
Through the steps S202 to S206, the problem that a large deviation is generated due to a large difference between a specific channel and a classical scene or a variance in the classical scene when a channel in a specific scene is to be simulated in the related art can be solved, the deviation between a general channel model based on empirical data and actually measured channels in different external fields is avoided, and meanwhile, measurement data automatically generated in the working process of a communication system can be directly used, so that additional measurement and overhead are avoided.
In this embodiment, the step S204 may specifically include:
performing the following operations on each external field measurement data in the plurality of external field measurement data to obtain the channel characteristics of the plurality of sampling points, wherein the external field measurement data being performed is referred to as current external field measurement data:
determining the number of multipath paths in the channel according to the current external field measurement data;
pre-estimating the time delay of the multi-path according to the path number of the channel;
calculating time domain response of each path from the current external field measurement data according to the time delay of the multipath, and estimating the average power of the multipath according to the time domain response of each path;
clustering the multi-paths according to the correlation among the multi-paths in the channel to obtain a plurality of target paths, and specifically, calculating the correlation among the multi-paths; clustering the multi-paths according to the correlation to obtain a multi-cluster set, wherein each cluster set comprises at least one path; respectively selecting the target diameter with the maximum average power from the multi-cluster set to obtain a plurality of target diameters;
and determining the arrival angles of the multiple target paths and the polarization characteristics of the multipath, wherein the polarization characteristics are the correlation among channels in different polarization directions, and the channel characteristics comprise the time delay of the multipath, the average power of the multipath, the arrival angle of the multipath and the polarization characteristics of the multipath.
In this embodiment, the step S202 may specifically include:
s2021, acquiring measurement data of an external field channel when the external field communication equipment of the plurality of sampling points receives the signal;
further, the step S2021 may specifically include: performing channel estimation when the external field communication equipment receives signals to obtain a wireless channel estimation value; and acquiring quality tag data of the wireless channel estimation value and network tag data of the wireless channel estimation value, wherein the measurement data comprises the wireless channel estimation value, the quality tag data of the wireless channel estimation value and the grid tag data of the wireless channel estimation value.
S2022, screening the plurality of external field measurement data meeting preset conditions from the measurement data of the plurality of sampling points respectively.
Further, the step S2022 may specifically include:
respectively judging whether the measurement data of the plurality of sampling points meet preset conditions according to the quality label data of the wireless channel estimation value, specifically, respectively judging whether the signal to interference plus noise ratio of the measurement data of the plurality of sampling points is greater than a first preset threshold value, whether the Doppler frequency shift is smaller than a second preset threshold value and whether the motion speed estimation value is smaller than a third preset threshold value under the condition that the quality label data of the wireless channel estimation value comprises the signal to interference plus noise ratio, the Doppler frequency shift and the motion speed estimation value of a current received signal; if the judgment result is yes, determining that the preset condition is met; determining that the preset condition is not met under the condition that the judgment result is negative;
and deleting the data which do not meet the preset condition from the measurement data of the plurality of sampling points respectively to obtain the plurality of external field measurement data which meet the preset condition.
Fig. 3 is a block diagram of a channel construction system according to the present embodiment, as shown in fig. 3, including three subsystems of raw data acquisition, channel feature extraction, and model regression.
The original data acquisition subsystem is mainly used for acquiring external field channel sampling points which can be used by the channel feature extraction subsystem and the model regression subsystem, wherein:
and acquiring external field channel measurement, namely acquiring external field measurement data from wireless equipment operated by an external field. The measurement data includes a radio channel estimation value extracted from the external field received signal, quality tag data of the external field radio channel estimation value, and grid tag data of the external field radio channel estimation value. The estimation method can be a method commonly used at present and other methods, and the output values thereof include: a radio channel estimation value, quality tag data of an external field radio channel estimation value, and grid tag data of an external field radio channel estimation value. The wireless channel estimation value can be a time-frequency or frequency-domain estimation value obtained by the receiver on the pilot frequency of various signals, and other channel estimation values obtained by various methods; the quality label data can be the signal-to-interference-and-noise ratio of the current received signal, the doppler shift, the user motion velocity estimation (i.e. the above motion velocity estimation value), the data receiving accuracy and other measurement values reflecting the data receiving and channel estimation quality; the grid tag data may be longitude and latitude coordinates of other transmitters and receivers, map coordinates, sampling time stamps, or other data reflecting geographic location and time information. The quality label data and the mesh label data are optional data.
And channel data screening, namely screening corresponding data which do not meet quality requirements according to the quality label data from the outfield measurement data when the quality label data are available, and only carrying out subsequent processing on the data which meet the requirements. The quality tag data is compared with the quality tag data according to a predetermined criterion, and the unsatisfactory data is deleted. The criterion may be a single condition or a combination of conditions, which may be: the received signal-to-interference-and-noise ratio is larger than a certain threshold; the Doppler frequency shift is less than a certain threshold; the user movement speed is less than a certain threshold; the number of relevant available channel estimation values exceeds a threshold; and other conditions that can be determined to be obtained under the conditions of low mobility, high communication quality, and large amount of relevant data. This criterion is not the only application of the method to low-speed channel modeling in order to improve the accuracy of channel feature estimation for the location point. By combining the modeling of the motion and trajectory, the method can be applied to channel modeling with arbitrary velocities.
The channel characteristic extraction subsystem is mainly used for extracting the multipath time delay, the arrival angle and the polarization correlation characteristic of a channel from data meeting the quality requirement, wherein:
and estimating the multipath path number of the channel, namely estimating the path number of the multipath which can be distinguished in the wireless channel data which is analyzed currently. The estimation method may adopt a Signal Parameter Estimation (ESPRIT) or multi-Signal Classification (MUSIC) Algorithm based on a rotation invariant technique, and other high-precision spectrum estimation algorithms and their variants. The detection principle of the path number can be a criterion that the path is determined as a path if the energy is greater than a preset threshold, or a criterion that the total energy of the finally determined paths is greater than a total energy percentage threshold.
And multipath time delay estimation, namely estimating the time delay of each path according to the output multipath number. The estimation method may employ the ESPRIT or MUSIC algorithm as well as other high-precision spectral estimation algorithms and variants thereof.
And estimating the time domain response of each path, calculating the time domain response of each path from the wireless channel data according to the estimated time delay, including the amplitude and the phase on each receiving antenna, and estimating the average power value of each path. Least squares estimation may be used, or signal estimation algorithms known in the art, such as maximum likelihood, maximum prior probability, etc., may be used.
And clustering each path channel, clustering and clustering each path with high correlation, and replacing each path with a path channel to maintain low correlation among the processed paths. The correlation can be calculated according to the output of the delay response estimation of each path, and after clustering according to the correlation, one path in the cluster can be selected, or a path representing the whole cluster is obtained in a weighted average mode of each path in the cluster. And updates the channel number estimate.
Estimating the horizontal arrival angle of each path, and estimating the information of the arrival angle of each path channel in the horizontal direction for each path channel separated by clustering each path channel. The above algorithm for estimating the angle of arrival after obtaining the correlation matrix may also be a multi-step iterative search algorithm, and may be a spectrum estimation algorithm, such as ESPRIT and MUSIC algorithms, and their variants.
Estimating the vertical arrival angle of each path, and estimating the information of the arrival angle in the vertical direction of each path channel separated by clustering each path channel. The above algorithm for estimating the angle of arrival after obtaining the correlation matrix may also be a multi-step iterative search algorithm, and may be a spectrum estimation algorithm, such as ESPRIT and MUSIC algorithms, and their variants.
And estimating the polarization direction correlation, namely estimating the correlation among channels in different polarization directions for each path channel separated by clustering each path channel.
The model regression subsystem utilizes the channel characteristics extracted by the channel characteristic extraction subsystem to generate different models, and is suitable for different scenes, wherein:
generating a point model, inputting the channel path number, time delay, power of each path, vertical and horizontal arrival angle, polarization correlation and other characteristics output by the channel characteristic extraction subsystem into a multipath channel model as parameters to generate a channel; and storing the parameters as a whole sampling point of the channel, and outputting the parameters to statistical model regression and grid model regression. The Channel Model used herein may be any Model that can calculate a Channel coefficient according to a multipath parameter, such as a multipath Channel Model defined in 3gpp tr38.901, or a Spatial Channel Model (SCM), an extended Spatial Channel Model (SCME), and other Channel models, and the parameter obtained in the foregoing steps is used in a Cluster Delay Model (CDL) instead of a parameter previously fitted according to a classical scene, so that a Channel Model that is directly aligned to a specific external field environment and has excellent fitting performance to the external field real Channel can be obtained. And simultaneously storing the result of the channel characteristic parameter and the data tag information as a sampling point for subsequent model regression.
And (4) performing statistical model regression, namely performing statistical model regression on a plurality of integral channel sampling points output by the point model to obtain probability distribution of channel parameters, such as time delay, power, spatial angle distribution and the like. The probability distribution is used to generate channel parameters and a channel model for locations in the external field that are not measured. The probability distribution used may be in the current classical channel model, or any other means-enhanced a priori external field parameter distribution model.
And (4) grid model regression, namely performing grid model regression on a plurality of integral channel sampling points output by the point model, and merging and storing the extracted channel parameters and the grid channels adopting the points. The predicted channel parameters and channel model are output for the network without measured data using the information of the mesh and the measured data.
The external field communication device can be any multi-antenna transceiving device with wireless transceiving function. Such devices include, but are not limited to: 5G base stations, 4G base stations, wiFi nodes, and so on. The processing flow comprises the following steps:
the external field channel measurement is obtained, and when the external field communication equipment receives signals, measurement processes such as channel estimation, signal quality measurement, transmitter receiver position estimation and the like need to be carried out. And outputs its measurement estimate in a prescribed format. The Reference Signal for detection may be a Sounding Reference Signal (SRS), or a Demodulation Reference Signal (DMRS) of a data and control channel, or other Reference Signal sequences known by the receiving end and used for channel estimation. The output values include: the wireless channel estimation value, the signal-to-interference-and-noise ratio measurement value, the Doppler frequency shift of the transmitter, the geographic latitude and longitude coordinates of the transmitter and the time stamp of the wireless channel estimation data.
Wireless channel estimation value: the result of the frequency domain channel estimation obtained using the frequency domain reference signal for transmitter u is H u (k, t, m, n, p), where k is the number of Frequency domain subcarriers in OFDM (Orthogonal Frequency Division multiplexing), t is the time of time domain sampling, m is the antenna element number in the horizontal direction in the receiver antenna panel, n is the antenna element number in the vertical direction in the receiver antenna panel, and p is the antenna element polarization number in different polarization directions in the receiver antenna panel. Each number ranges from 1 to the number represented by its corresponding capitalized symbol. Wherein, T is the total time domain sampling time number, M is the total antenna oscillator number in the horizontal direction in the receiver antenna panel, N is the total antenna oscillator number in the vertical direction in the receiver antenna panel, and P is the total co-polarization direction number in the receiver antenna panel. If the input channel estimate is a time domain channel, it may be transformed into a frequency domain channel value by a fourier transform.
Data channel screening, filtering out inputs that do not meet predefined requirements: data samples with signal to interference plus noise ratios below 10dB, doppler shift above 300Hz, and samples with time upsampling times less than 20 times are filtered out. The above numbers and thresholds may be adjusted based on empirical data or other criteria.
The extraction of the channel multipath number can adopt an ESPRIT algorithm. If the input channel estimate is a time domain channel, it may be transformed into a frequency domain channel value by a fourier transform, including:
firstly, estimating a frequency domain correlation matrix in an original channel sample, and arranging channels on different antenna units at different sampling moments as column vectors according to frequency positions of subcarriers of the channels:
H u,f (t,m,n,p)＝[H u (1,t,m,n,p)…H u (K,t,m,n,p)] T ；
and then estimating a cross-correlation matrix of the frequency domain channel:
and (3) further stabilizing the statistical result by adopting a Forward-Back scheme, and reducing the noise:
where J is an inverse diagonal matrix of dimension K and conj (-) is an element-by-element conjugate operation. And performing SVD decomposition on the data:
wherein the svd () function is a singular value decomposition operation on a matrix, Λ u,f ＝diag{λ 1 … λ K Contains the matrix R u,f,FB K singular values of (a). From these singular values, the minimum representation length when the number of paths is i is calculated:
the final path number estimate is then:
and multipath time delay estimation, wherein the multipath time delay estimation can adopt a spectrum estimation mode to obtain accurate and independent time delay which is less than the sampling rate of a communication system.
When the ESPRIT method is used, the following is estimated:
order matrix U L Is a matrix U u,f Which contains the first L eigenvectors, a K × L matrix. On this basis, two new matrices are generated:
U 1 ＝[I,0]U L ；
U 2 ＝[0,I]U L ；
where I is a unit diagonal matrix of size (K-1) × (K-1), and 0 is the full 0 vector of (K-1) × 1. Eigenvalue decomposition is performed on the following matrix according to the following formula:
where eig () is the eigenvalue decomposition of the matrix.
Π is a vector comprising L eigenvalues, wherein the L-th eigenvalue is γ l Then, the delay estimate of the first path is obtained as:
wherein f is Δ,H The arg () is an operation of solving the complex argument for the frequency domain interval of two channel sampling points in the original data.
And (4) estimating the time domain response of each path, and acquiring the response value and the power value of each path on the time delay by using the time delay information of each path acquired in the previous step and the original frequency domain channel estimation. The algorithm using least squares estimation is as follows:
estimating a matrix with the size of K multiplied by L according to the parameters obtained in the previous step as follows:
the least square estimation of each path channel in the time domain is as follows:
the power distribution of each diameter can be obtained as follows:
grouping channels of all paths, calculating the correlation value of the channels among all paths, grouping the paths with high correlation, and replacing with a single-path channel. The method comprises the following steps of putting each path into an original set, and executing the following steps:
step 1, calculating the correlation R (i, j) between the paths;
step 2, selecting a path k with the maximum power from the paths in the current set;
step 3, classifying the paths j with the R (k, j) > Threshold into a cluster, and deleting the paths j from the original set;
step 4, repeating the step 2,3 until no diameter exists in the set;
and 5, selecting one path with the strongest power from each cluster, and deleting the rest paths.
The above Threshold may be chosen to be 0.5 or adjusted to any value based on experience and other criteria. The original estimated path number is updated to the clustered value.
Estimating the horizontal reaching angle of each path, acquiring the angle power of a horizontal space domain by using a multi-step search method, and calculating the independent paths after clustering as follows:
firstly, calculating a correlation matrix of the channel of the first path in the horizontal direction, and arranging the channel of the first path in the horizontal direction as a vector:
h l,hor (t,n,p)＝[h l (t,1,n,p)…h l (t,M,n,p)] T ；
obtaining a correlation matrix estimate:
dividing into coarse estimation and fine estimation, and repeating the coarse estimation according to the given initial estimation stepLong and long
To recalculate the refined power spectrum and then use a finer search to obtain a finer estimate. The angle power spectrum obtained by the rough estimation is calculated according to the following formula:
wherein
To comprise an interval of
A matrix of direction vectors.
Then, according to the angle information represented by each point, the point with the maximum power is obtained:
a direction estimate is made.
In [ AoA ] l,coarse -ASA l,coarse ,AoA l,coarse +ASA l,coarse ]In accordance with a given step length
To recalculate the fine power spectrum:
Φ is the matrix containing the beam vectors of all sample points. A more accurate estimate can be obtained by recalculating AoA and ASA according to the same formula as the coarse estimate.
The above fine estimation may be performed for a number of iterations.
The angle estimation is achieved vertically for each path, the processing procedure is the same as the previous step, and the direction is changed from horizontal to vertical.
And (4) polarization direction correlation estimation, namely calculating a correlation value between two groups of channel estimation values with different polarizations, and finally obtaining the correlation value.
And generating a point model, inputting the information of each path, power, time delay, vertical arrival angle, horizontal arrival angle and correlation information of each path generated in the previous step into a corresponding channel generation model, and generating a channel coefficient. The used channel model is a multi-path channel model defined in 3GPP TR38.901, and in a Cluster Delay model (CDL), parameters obtained in the steps are used for replacing parameters which are fitted according to a classical scene in advance, so that a channel model which is right against a specific external field environment and has excellent fitting performance with an external field real channel can be obtained. And simultaneously storing the result of the channel characteristic parameter and the data tag information as a sampling point for subsequent model regression.
And (4) performing regression on the statistical model, and predicting the possible values of the points which are not measured in the network by estimating the probability distribution of the channel characteristics when the sampling points in the point model are accumulated to a certain number.
Firstly, according to the estimated value of each sampling point, the multipath time delay expansion and the power spectrum of the horizontal and vertical space angles are calculated. The calculation is based on a mathematical definition of the values. Then, assuming that the calculated value is X, and considering that it conforms to the lognormal distribution in the classical scenario according to the model in 3gpp tr38.901, and the subscript u represents different sampling points, the mean and variance estimates of the distribution can be obtained as follows:
by using the obtained distribution, any random channel sampling characteristic value conforming to the distribution can be generated, a multi-path parameter is generated, and a channel coefficient is generated in a multi-path channel generation model, such as a 3GPP TR38.901 multi-path channel generation model.
And (4) grid model regression, wherein the channel characteristics output by the point model and grid label information are stored together. When the channel of the grid needs to be output, the method of step 10 is adopted to output the channel coefficient. And generating a time-space two-dimensional grid index by using the tag data, generating a space grid dimension by using the longitude and latitude of the transmitter, and generating a time grid dimension by using the timestamp. The mesh granularity is set according to the actual data volume and the requirements. When the channel parameters of any point need to be output, firstly, a grid set which has data in all grids and is closest to the grid set in spatial position is searched, and then on the basis, the closest grid in time is searched on the grid set. Outputting the point model parameters of the grid to the multipath channel model, and generating the channel coefficient according to the method of step 10.
The external field communication device can be any multi-antenna transceiving device with wireless transceiving function. Such devices include, but are not limited to: 5G base stations, 4G base stations, wiFi nodes, and so on. The processing flow comprises the following steps:
the external field channel measurement is obtained, and when the external field communication equipment receives signals, measurement processes such as channel estimation, signal quality measurement, transmitter receiver position estimation and the like need to be carried out. And outputs its measurement estimate in a prescribed format. The Reference Signal for detection may be a Sounding Reference Signal (SRS), or a Demodulation Reference Signal (DMRS) of a data and control channel, or other Reference Signal sequences known by the receiving end and used for channel estimation. The output values include: the wireless channel estimation value, the received reference signal strength, the movement speed of the transmitter, the geographic latitude and longitude coordinates of the transmitter and the time stamp of the wireless channel estimation data.
Wireless channel estimation value: the result of the frequency domain channel estimation obtained using the frequency domain reference signal for transmitter u is H u (k, t, m, n, p), where k is the number of frequency domain subcarriers in OFDM, t is the time of time domain sampling, m is the number of antenna elements in the horizontal direction in the antenna panel of the receiver, and n is the number of antenna elements in the receiverAnd the antenna element numbers in the vertical direction in the antenna panel are p, and the antenna element polarization numbers in different polarization directions in the antenna panel of the receiver are p. Each number ranges from 1 to the number represented by its corresponding capitalized symbol. Wherein, T is the total time domain sampling time number, M is the total antenna oscillator number in the horizontal direction in the receiver antenna panel, N is the total antenna oscillator number in the vertical direction in the receiver antenna panel, and P is the total co-polarization direction number in the receiver antenna panel. If the input channel estimate is a time domain channel, it may be transformed into a frequency domain channel value by a fourier transform.
Data channel screening, filtering out inputs that do not meet the implementation definition requirements: data samples with reference signal strength below-110 dB, motion speed greater than 10 km per hour, and samples with temporal up-sampling times less than 20 are filtered out. The above numbers and thresholds may be adjusted based on empirical data or other criteria.
And (3) extracting the multipath number of the channel, wherein the maximum multipath number of the fixed channel is L =20 according to an empirical value.
And multipath time delay estimation, wherein the multipath time delay estimation can adopt a spectrum estimation mode to obtain accurate and independent time delay which is less than the sampling rate of a communication system. When the ESPRIT method is used, it is estimated as follows:
firstly, estimating a frequency domain correlation matrix in an original channel sample, arranging channels on different antenna units at different time points according to frequency positions of subcarriers of the channels as column vectors:
H u,f (k,t,m,n,p)＝[H u (1,t,m,n,p)…H u (K,t,m,n,p)] T ；
and then estimating a cross-correlation matrix of the frequency domain channel:
and (3) further stabilizing the statistical result by adopting a Forward-Back scheme, and reducing the noise:
where J is an inverse diagonal matrix of dimension K and conj (-) is an element-by-element conjugate operation. And performing SVD decomposition on the data:
[U u,f ,Λ u,f ,V u,f ]＝svd(R u,f,FB )；
order matrix U L Is a matrix U u,f Which includes the first L =20 eigenvectors, and a matrix of size K × L. On this basis, two new matrices are generated:
U 1 ＝[I,0]U L ；
U 2 ＝[0,I]U L ；
where I is a unit diagonal matrix of size (K-1) × (K-1), and 0 is the full 0 vector of (K-1) × 1. Eigenvalue decomposition is performed on the following matrix according to the following formula:
Π is a vector comprising L eigenvalues, wherein the L-th eigenvalue is γ l Then, the delay estimate of the first path is obtained as:
wherein f is Δ,H The frequency domain interval of two channel sampling points in the original data.
The above eig (), svd (), arg () have the same meaning as the above embodiment.
And estimating the time domain response of each path. And obtaining the response value and the power value of each path on the time delay by using the time delay information of each path obtained in the previous step and the original frequency domain channel estimation. The algorithm using the least squares estimation is as follows:
estimating a matrix with the size of K multiplied by L according to the parameters obtained in the previous step as follows:
the least square estimation of each path channel in the time domain is as follows:
the power distribution of each path can be obtained as follows:
clustering channels of all paths, calculating correlation values of channels among all paths, clustering all paths with high correlation, replacing the paths with a single-path channel, putting all paths into an original set, and executing the steps as follows:
step 1, calculating the correlation R (i, j) between the paths;
step 2, selecting a path k with the maximum power from the paths in the current set;
step 3, classifying the paths j with the R (k, j) > Threshold into a cluster, and deleting the paths j from the original set;
step 4, repeating the step 2,3 until no diameter exists in the set;
and 5, averaging the time of each cluster through the power weighting of each path in each cluster to obtain the final time delay of the representative path, and obtaining the final power of the representative path by the power through the average power of each path.
The above Threshold may be chosen to be 0.5 or adjusted to any value based on experience and other criteria. And updating the original estimated diameter number to a clustered value.
Estimating the horizontal reaching angle of each path, acquiring the angle power of a horizontal space domain by using a multi-step search method, and calculating the independent paths after clustering as follows:
firstly, calculating a correlation matrix of the channel of the first path in the horizontal direction, and arranging the channel of the first path in the horizontal direction as a vector:
h l,hor (t,n,p)＝[h l (t,1,n,p) … h l (t,M,n,p)] T ；
obtaining a correlation matrix estimate
The above, after obtaining the correlation matrix, runs a spectrum estimation algorithm, such as ESPRIT and MUSIC algorithms, and their variants, thereon to obtain the angle estimates for each path.
And (4) vertically estimating the angle of each path, and changing the direction from horizontal to vertical in the same processing procedure as the previous step.
And (4) polarization direction correlation estimation, namely calculating a correlation value between two groups of channel estimation values with different polarizations, and finally obtaining the correlation value.
And generating a point model, inputting the information of each path, power, time delay, vertical arrival angle, horizontal arrival angle and correlation information of each path generated in the previous step into a corresponding channel generation model, and generating a channel coefficient. The used channel model is input into an SCME channel model, and the parameters obtained in the previous steps are used for replacing the parameters which are fitted according to the classical scene in advance, so that the channel model which is just aligned to a specific external field environment and has excellent external field real channel fitting performance can be obtained. And simultaneously storing the result of the channel characteristic parameter and the data tag information as a sampling point for subsequent model regression.
And (5) performing statistical model regression. When a certain number of sampling points in the point model are accumulated, the probability distribution of the channel characteristics is estimated, and the possible value of the point which is not measured in the network is predicted.
Firstly, according to the estimated value of each sampling point, the multipath time delay expansion and the power spectrum of the horizontal and vertical space angles are calculated. The calculation is based on a mathematical definition of the values. Then, assuming that the calculated value is X, and the calculated value is considered to conform to the lognormal distribution in the classical scenario according to the 3gpp tr38.901 model, and the subscript u represents different sampling points, the mean and variance estimates of the distribution can be obtained as follows:
by utilizing the obtained distribution, any random channel sampling characteristic value conforming to the distribution can be generated, multipath parameters are generated, and the multipath parameters are input into an SCME multipath channel generation model and other random multipath channel generation models to generate channel coefficients.
And (4) grid model regression, wherein the channel characteristics output by the point model and grid label information are stored together. When the channel of the grid needs to be output, the method of step 10 is adopted to output the channel coefficient. And generating a two-dimensional grid index of space and time by using the tag data, generating a grid dimension of space by using the longitude and latitude of the transmitter, and generating a grid dimension of time by using the timestamp. The mesh granularity is set according to the actual data volume and the requirements. When the channel parameters of any point need to be output, firstly, a grid set which has data in all grids and is closest to the grid set in spatial position is searched, and then on the basis, the closest grid in time is searched on the grid set. Averaging the parameters between the 3 grids closest to each other to obtain the output channel parameters, and outputting the parameters to the multi-path channel model to generate the channel coefficients.
In the embodiment, the data analysis is carried out on the wireless channel data acquired on site, and then the channel model is generated, so that the mismatching and errors of a generalized model based on a classical scene to a specific scene are effectively avoided, and the accurate research on a system, an algorithm and a problem is facilitated on the basis. The time domain spectrum estimation and channel classification clustering can effectively eliminate or inhibit the expansion introduced by a filter and other processing modules in communication equipment, accurately separate irrelevant paths in a channel and approach to a real external field channel. The adopted point model, the statistical model and the grid model can establish proper models under different sampling data volumes, different data integrity degrees and different application scenes, and support modeling and simulation from a link to a system level.
According to another embodiment of the present application, there is also provided a channel modeling apparatus, and fig. 4 is a block diagram of the channel modeling apparatus according to the present embodiment, as shown in fig. 4, including:
the acquiring module 42 is configured to acquire outfield measurement data of the outfield communication device at multiple sampling points when receiving the signal, so as to obtain multiple outfield measurement data;
a feature extraction module 44, configured to perform feature extraction on the external field measurement data to obtain channel features of the multiple sampling points;
and the processing module 46 is configured to perform model regression processing according to the channel characteristics of the plurality of sampling points.
Fig. 5 is a block diagram of a channel modeling apparatus according to the preferred embodiment, and as shown in fig. 5, the processing module 46 includes:
the output sub-module 52 is configured to perform statistical model regression or network model regression according to the channel characteristics of the multiple sampling points, and output the channel characteristics of all the sampling points;
and the input sub-module 54 is configured to input the channel characteristics of all the sampling points as channel parameters into a multipath channel model, so as to obtain a simulated channel of any point output by the multipath channel model.
Fig. 6 is a block diagram ii of the channel modeling apparatus according to the preferred embodiment, and as shown in fig. 6, the feature extraction module 44 includes:
an execution sub-module 62, configured to perform the following operations on each of the plurality of external field measurement data, to obtain channel characteristics of the plurality of sampling points, where the external field measurement data being executed is referred to as current external field measurement data:
determining the number of multipath paths in the channel according to the current external field measurement data;
pre-estimating the time delay of the multi-path according to the path number of the channel;
calculating the time domain response of each path from the current external field measurement data according to the time delay of the multipath, and estimating the average power of the multipath according to the time domain response of each path;
clustering the multipaths according to the correlation among the multipaths in a channel to obtain a plurality of target paths;
and determining the arrival angles of the multiple target paths and the polarization characteristics of the multipath, wherein the polarization characteristics are the correlation among channels in different polarization directions, and the channel characteristics comprise the time delay of the multipath, the average power of the multipath, the arrival angle of the multipath and the polarization characteristics of the multipath.
In an exemplary embodiment, the execution submodule 62 is further configured to
Calculating the correlation of the channels among the multi-paths;
clustering the multi-paths according to the correlation to obtain a multi-cluster set, wherein each cluster set comprises at least one path;
and respectively selecting the target path with the maximum average power from the multi-cluster set to obtain the plurality of target paths.
Fig. 7 is a block diagram three of the channel modeling apparatus according to the preferred embodiment, and as shown in fig. 7, the obtaining module 42 includes:
the obtaining sub-module 72 is configured to obtain measurement data of an external field channel when the external field communication device of the multiple sampling points receives a signal;
and the screening submodule 74 is configured to screen the external field measurement data meeting the preset condition from the measurement data of the plurality of sampling points, respectively.
In an exemplary embodiment, the obtaining submodule 72 is further configured to
Performing channel estimation when the external field communication equipment receives signals to obtain a wireless channel estimation value;
and acquiring quality tag data of the wireless channel estimation value and network tag data of the wireless channel estimation value, wherein the measurement data comprises the wireless channel estimation value, the quality tag data of the wireless channel estimation value and the grid tag data of the wireless channel estimation value.
In an exemplary embodiment, the screening submodule 74 includes:
the judging unit is used for judging whether the measurement data of the plurality of sampling points meet preset conditions or not according to the quality label data of the wireless channel estimation value;
and the deleting unit is used for deleting the data which do not meet the preset condition from the measured data of the plurality of sampling points respectively to obtain the plurality of external field measured data which meet the preset condition.
In an exemplary embodiment, the determining unit is further configured to
Under the condition that the quality label data of the wireless channel estimation value comprises the signal to interference plus noise ratio, the Doppler frequency shift and the motion speed estimation value of a current receiving signal, respectively judging whether the signal to interference plus noise ratio of the measurement data of the plurality of sampling points is larger than a first preset threshold value, whether the Doppler frequency shift is smaller than a second preset threshold value and whether the motion speed estimation value is smaller than a third preset threshold value;
if the judgment result is yes, determining that the preset condition is met;
and under the condition that the judgment result is negative, determining that the preset condition is not met.
Embodiments of the present application further provide a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.
In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present application further provide an electronic device comprising a memory having a computer program stored therein and a processor configured to execute the computer program to perform the steps of any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.
It will be apparent to those skilled in the art that the various modules or steps of the present application described above may be implemented using a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may be implemented using program code executable by the computing devices, such that they may be stored in a memory device and executed by the computing devices, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into separate integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the principle of the present application shall be included in the protection scope of the present application.
Claims (11)
1. A method for channel modeling, comprising:
acquiring outfield measurement data of a plurality of sampling points when the outfield communication equipment receives signals to obtain a plurality of outfield measurement data;
respectively extracting the characteristics of the external field measurement data to obtain the channel characteristics of the sampling points;
and performing model regression processing according to the channel characteristics of the plurality of sampling points.
2. The method of claim 1, wherein performing model regression processing based on the channel characteristics of the plurality of sampling points comprises:
performing statistical model regression or network model regression according to the channel characteristics of the plurality of sampling points, and outputting the channel characteristics of all the sampling points;
and inputting the channel characteristics of all sampling points into a multipath channel model as channel parameters to obtain a simulated channel of any point output by the multipath channel model.
3. The method of claim 1, wherein the performing feature extraction on the external field measurement data respectively to obtain the channel features of the sampling points comprises:
performing the following operations on each external field measurement data in the plurality of external field measurement data to obtain the channel characteristics of the plurality of sampling points, wherein the external field measurement data being performed is referred to as current external field measurement data:
determining the number of multipath paths in the channel according to the current external field measurement data;
pre-estimating the time delay of the multi-path according to the path number of the channel;
calculating the time domain response of each path from the current external field measurement data according to the time delay of the multipath, and estimating the average power of the multipath according to the time domain response of each path;
clustering the multipaths according to the correlation among the multipaths in a channel to obtain a plurality of target paths;
and determining the arrival angles of the multiple target paths and the polarization characteristics of the multipath, wherein the polarization characteristics are the correlation among channels in different polarization directions, and the channel characteristics comprise the time delay of the multipath, the average power of the multipath, the arrival angle of the multipath and the polarization characteristics of the multipath.
4. The method of claim 3, wherein clustering the multipaths according to correlations between the multipaths in a channel to obtain a plurality of target paths comprises:
calculating the correlation of the channels among the multi-paths;
clustering the multi-paths according to the correlation to obtain a multi-cluster set, wherein each cluster set comprises at least one path;
and respectively selecting the target path with the maximum average power from the multi-cluster set to obtain the plurality of target paths.
5. The method of claim 1, wherein obtaining the outfield measurement data of the outfield communication device receiving the signal at the plurality of sampling points comprises:
acquiring measurement data of an external field channel when the external field communication equipment of the plurality of sampling points receives signals;
and respectively screening out the external field measurement data meeting preset conditions from the measurement data of the sampling points.
6. The method of claim 5, wherein obtaining measurement data of the outfield channel when the outfield communication device of the plurality of sampling points receives the signal comprises:
when the external field communication equipment receives signals, channel estimation is carried out to obtain a wireless channel estimation value;
and acquiring quality tag data of the wireless channel estimation value and network tag data of the wireless channel estimation value, wherein the measurement data comprises the wireless channel estimation value, the quality tag data of the wireless channel estimation value and the grid tag data of the wireless channel estimation value.
7. The method of claim 6, wherein the step of respectively screening the plurality of external field measurement data satisfying a preset condition from the measurement data of the plurality of sampling points comprises:
judging whether the measurement data of the plurality of sampling points meet preset conditions or not according to the quality label data of the wireless channel estimation value;
and deleting the data which do not meet the preset condition from the measurement data of the plurality of sampling points respectively to obtain the plurality of external field measurement data which meet the preset condition.
8. The method of claim 7, wherein determining whether the measurement data of the plurality of sampling points satisfy a preset condition according to the quality label data of the wireless channel estimation value comprises:
under the condition that the quality label data of the wireless channel estimation value comprises the signal to interference plus noise ratio, the Doppler frequency shift and the motion speed estimation value of a current receiving signal, respectively judging whether the signal to interference plus noise ratio of the measurement data of the plurality of sampling points is larger than a first preset threshold value, whether the Doppler frequency shift is smaller than a second preset threshold value and whether the motion speed estimation value is smaller than a third preset threshold value;
if the judgment result is yes, determining that the preset condition is met;
and under the condition that the judgment result is negative, determining that the preset condition is not met.
9. An apparatus for channel modeling, comprising:
the acquisition module is used for acquiring the outfield measurement data of the outfield communication equipment with a plurality of sampling points when the outfield communication equipment receives signals to obtain a plurality of outfield measurement data;
the characteristic extraction module is used for respectively extracting the characteristics of the external field measurement data to obtain the channel characteristics of the sampling points;
and the processing module is used for performing model regression processing according to the channel characteristics of the plurality of sampling points.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any one of claims 1 to 8 when executed.
11. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 8.
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| CN118174756A (en) * | 2024-05-14 | 2024-06-11 | 南方科技大学 | Random signal system-oriented general sense integrated precoding method, equipment and medium |
| CN118174756B (en) * | 2024-05-14 | 2024-07-26 | 南方科技大学 | Random signal system-oriented general sense integrated precoding method, equipment and medium |
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