CN115499044A - Similarity quantization method for cross-frequency-band wireless MIMO channel - Google Patents
Similarity quantization method for cross-frequency-band wireless MIMO channel Download PDFInfo
- Publication number
- CN115499044A CN115499044A CN202211150732.XA CN202211150732A CN115499044A CN 115499044 A CN115499044 A CN 115499044A CN 202211150732 A CN202211150732 A CN 202211150732A CN 115499044 A CN115499044 A CN 115499044A
- Authority
- CN
- China
- Prior art keywords
- channel
- frequency
- calculating
- similarity
- time delay
- 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.)
- Pending
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
- H04B17/391—Modelling the propagation channel
- H04B17/3911—Fading models or fading generators
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/022—Channel estimation of frequency response
Abstract
The invention discloses a method for quantizing the similarity of a cross-frequency-band wireless MIMO channel, which covers three dimensions of space, time and frequency of the channel. The method specifically comprises the following steps: 1) Constructing a channel measurement system and a planning measurement case, and measuring impulse response of a cross-frequency band wireless MIMO channel; 2) Calculating a frequency domain similarity index, namely a time delay power spectral density correlation coefficient of each frequency band; 3) Calculating a space domain similarity index, namely an angle power spectral density correlation coefficient of a channel; 4) Calculating a time domain similarity index, namely a small-scale fading correlation coefficient of a time-varying channel; 5) And carrying out weighted combination on the indexes of the three domains to generate a final similarity measurement index of the cross-frequency channel. The similarity measurement method of the cross-frequency-band wireless MIMO channel is firstly provided from the small-scale fading characteristic of the channel, can be used for quantifying the similarity of different frequency-band channels, and has guiding significance for the deployment of a cross-frequency-band communication system; the method can also be used as a performance index for evaluating the accuracy of the cross-frequency channel model.
Description
Technical Field
The invention belongs to the technical field of wireless communication channel measurement and modeling, and relates to a similarity quantization method of a cross-frequency-band wireless MIMO (Multiple-Input Multiple-Output) channel.
Background
2019, which marks the commercial era of china entering the Fifth Generation (5G) of mobile communication, the research on the Sixth Generation (6G) of mobile communication has been started following the tradition of "commercial Generation, planning the next Generation". The 6G network will realize global seamless coverage, and satisfy reliable connection of people, machines and things anytime and anywhere, and realizing the vision necessarily accompanies the explosion of data transmission quantity. In order to meet massive data transmission and consider the situation of scarce spectrum resources, cross-frequency band cooperative transmission becomes an unavoidable trend. Therefore, the research on the channel similarity degree of different frequency bands has important practical value, and guidance opinions can be provided for the deployment of the cross-frequency-band communication system. In addition, the accuracy of the cross-band wireless MIMO channel model needs to be measured to see whether the channel model truly reflects the similarity of the actually measured channels of the two bands. The existing patent achievement includes a terminal device and a base station device for channel similarity acquisition, but the existing patent achievement does not focus on describing channel similarity index construction and calculation, but focuses on how to feed back channel similarity to reduce system overhead. Therefore, it is necessary to provide an index for comprehensively measuring the similarity of MIMO channels to fill up the gap of the related research at present.
Disclosure of Invention
The invention aims to provide a method for quantizing the similarity of a cross-frequency-band wireless MIMO channel, which solves the problem of quantizing the similarity of the cross-frequency-band wireless MIMO channel required by system deployment and model evaluation.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
a similarity quantization method of a cross-frequency band wireless MIMO channel comprises the following steps.
S1, constructing a cross-frequency band wireless MIMO channel measurement system, planning a test case, and calculating channel impulse response;
s2, calculating the time delay power spectrum density of each frequency band according to the channel impulse response;
s3, preprocessing the time delay power spectrum density, and then calculating a time delay power spectrum correlation coefficient as a measurement index of frequency domain similarity;
s4, calculating Bartlett angle power spectrums of the transmitting and receiving ends of the channels in different frequency bands, combining the Bartlett angle power spectrums and then vectorizing the combined Bartlett angle power spectrums;
s5, calculating angle power spectrum correlation coefficients of channels in different frequency bands to serve as a measurement index of spatial domain similarity;
s6, after removing the large-scale fading, calculating small-scale fading correlation coefficients of mobile channels of different frequency bands as a measurement index of time domain similarity
And S7, giving different weights to the similarity indexes of the three domains of space, time and frequency, and then combining to generate a final cross-frequency channel similarity index.
The step S1 specifically includes the steps of:
s101, performing direct connection calibration on a channel detection system to obtain a direct connection calibration signal y th (t，τ)；
Step S102, obtaining a received signal y propagated by a wireless channel by using an antenna array r (t, τ); note that cross-band MIMO wireless channel measurements should meet the following requirements: 1. the bandwidth used by each frequency band is completely the same, 2, each frequency band should use an independent measuring system, the dynamic range of each measuring system is the same, and different measuring systems use the same trigger signal, 3, different frequency bands should use the same form of antenna as much as possible, 4, the single snapshot length of each frequency band should be kept as same as possible, which requires the switching mode of the radio frequency switch systems at the transmitting end and the receiving end to be kept as consistent as possible.
Step S103, calculating the impulse response h (t, tau) of the measurement channel based on inverse Fourier transform IFFT (-), expressed as
h(t，τ)＝IFFT{H(t，f)}＝IFFT{Y r (t，f)/Y th (t，f)}
Wherein Y is r (t, f) and Y th (t, f) are each y r (t, τ) and y th (t, τ) frequency domain transfer function. Channel in expressionThe dimension of the impulse response is M t ×M r ×M f ×M s Wherein M is t For number of transmitting antennas, M r For the number of receiving antennas, M f Is the number of subcarriers, M s H (t, f) is a channel transmission matrix.
The step S2 specifically includes the steps of:
calculating frequency band f according to channel impulse response c The power spectral density of time delay of (2) is expressed as:
in the formula
Is dimension M f The x 1 vector, | · | represents taking the absolute value of each element in the matrix, and m, n represent the serial numbers of the receiving and transmitting antennas, respectively.
The step S3 specifically includes the following steps, and S301 to S303 are performed separately for each compared frequency band:
and S301, extracting all delay points in the effective diameter range of the delay power spectral density.
Step S302, determining all effective diameters of the time delay power spectrum density by adopting a mode of searching local maximum values.
Step S303, selecting the delay power spectral density of the frequency band with the smaller effective diameter as a reference, and matching the effective diameters of the delay power spectral densities of all the frequency bands.
Step S304, calculating a time delay power spectral density correlation coefficient, wherein the expression is as follows:
wherein { } T Representing a transpose operation.
Step S305, calculating rho for all m, n τ,m,n And obtaining ρ τ,m,n Statistics of (2)And (4) information.
The step S4 specifically includes the following steps, which are performed separately for each compared frequency band:
step S401, calculating the angular power spectrum density and the arrival angle power spectrum density of the channel
And angle of departure power spectrum
The Bartlett method is adopted here, and the expressions are respectively:
wherein { } H Indicating Hermit transposition, and c (theta, phi) indicating a steering vector when the pitch angle is theta and the azimuth angle is phi. Because the invention is mainly developed aiming at a stable channel and the angle power spectral densities of different antenna pairs are close, the antenna pair with the highest signal-to-noise ratio is selected for calculation when the angle power spectral densities of the transceiving end are calculated.
Step S402, merging and backward quantizing the departing and arriving angle power spectrum density matrix, namely:
where vec {. Cndot } represents an operation to vectorize the matrix.
The step S5 specifically includes the following steps:
step S501, calculating angle power spectral density correlation coefficients of two frequency bands, wherein the expression is as follows:
step S502, calculating rho for all f B,f And obtaining ρ B,f The statistical information of (1).
The step S6 specifically includes the following steps, and S601 to S603 are performed for each compared frequency band individually:
step S601, calculating a channel gain after removing the small-scale fading:
where Δ τ and Δ t represent the delay resolution and the individual snapshot duration of the channel matrix, respectively. W represents the window length, typically taking a distance of 40 wavelengths.
Step S602, obtaining the path loss by utilizing the antenna gain of the transmitting and receiving end and the fitting of the receiving and transmitting power:
wherein d = τ LOS ·c，τ LOS Obtaining from the time-delayed power spectral density;
representation fitting by least squares
And d k Is described in (1).
Step S603, removing the path loss from the channel gain to obtain a small-scale fading value:
step S604, solving Pearson correlation coefficients of small-scale fading of two frequency bands:
wherein
ρ S When the frequency band is a negative number, the small-scale fading of the two frequency bands is in negative correlation, when the frequency band is a positive number, the positive correlation is represented, and 0 represents linear independence.
Step S605, calculate rho for all mobility measured routes S And obtaining ρ S The statistical information of (2).
Step S701, merging the three indexes as follows:
wherein alpha is 1 、α 2 And alpha 3 Respectively representing the weight of the similarity index of the frequency domain, the space domain and the time domain, and satisfying alpha 1 +α 2 +α 3 ＝1。
The similarity calculation method of the cross-frequency-band wireless MIMO channel has the following advantages:
based on the acquired channel impulse response, the similarity indexes are calculated respectively aiming at the frequency domain, the space domain and the time domain, the similarity among all frequency bands can be comprehensively compared, the indexes have definite physical significance and value range, the quantification of the similarity is realized, the research on the channel similarity degree of different frequency bands can be supported, and a basis is provided for measuring the accuracy of a cross-frequency band wireless MIMO channel model.
Drawings
Fig. 1 is a schematic flowchart of a similarity calculation method for a cross-band wireless MIMO channel according to the present invention;
FIG. 2 is a graph of time delay power spectral densities for different test frequency bands in accordance with the present invention;
FIG. 3-1 is a schematic diagram of the power spectral density of the delay before matching according to the present invention;
FIG. 3-2 is a schematic diagram of the matched time delay power spectral density of the present invention;
fig. 4 is a Cumulative Distribution Function (CDF) of the delay power spectral density correlation coefficient in a Line of Sight (Sight, loS) scene according to the present invention.
Fig. 5 is the CDF of the angle power spectral density correlation coefficient under LoS in accordance with the present invention.
Detailed Description
In order to better understand the purpose, structure and function of the present invention, the following describes the similarity calculation method of a cross-band wireless MIMO channel in detail with reference to the accompanying drawings.
S1, constructing a cross-frequency-band wireless MIMO channel measurement system, planning a test case, and calculating channel impulse response;
s2, calculating the time delay power spectrum density of each frequency band according to the channel impulse response;
s3, calculating a time delay power spectrum correlation coefficient after certain pretreatment is carried out on the time delay power spectrum density, and using the time delay power spectrum correlation coefficient as a measurement index of frequency domain similarity;
s4, calculating angle power spectrums of channels in different frequency bands, and vectorizing the angle power spectrums;
s5, calculating angle power spectrum correlation coefficients of channels in different frequency bands to serve as a measurement index of spatial domain similarity;
and S6, calculating small-scale fading correlation coefficients of channels in different frequency bands to serve as a measurement index of time domain similarity.
And S7, giving different weights to the similarity indexes of the three domains of space, time and frequency, and then combining to generate a final cross-frequency channel similarity index.
Further, the step S1 specifically includes the following steps:
step S101, direct connection calibration is carried out on the channel detection system to obtain a direct connection calibration signal.
The transmitted signal is denoted as x (t), the impulse response of the measurement system and the cable is denoted as g (t), and the impulse response of the channel is denoted as h (t). X (f), G (f) and H (f) respectively represent frequency domain transmission functions obtained by Fourier transform of X (t), G (t) and H (t). Y is th (f) Representing the channel transmission function when the transmitting and receiving ends are directly connected: y is th (t)＝x (t) g (t), where denotes convolution. Y is r (f) Represents the channel transfer function when wireless transmission is performed using an antenna: y is th (f)＝X(f)·G(f)。
The time domain signal and the frequency domain signal obtained by antenna air interface transmission are as follows:
y r (t)＝x(t)*g(t)*h(t)
Y r (f)＝X(f)·G(f)·H(f)
step S102, obtaining a received signal y (t, τ) propagated by a wireless channel by using an antenna array, and paying attention to the fact that cross-band MIMO wireless channel measurement needs to ensure that channel acquisition environments of multiple frequency bands are as consistent as possible, and channel measurement equipment and antenna configuration should be the same as possible, so that propagation channels of two frequency bands have comparability. The requirements to be met are summarized below: 1. the frequency bands use the same bandwidth, 2, each frequency band should use an independent measuring system, the dynamic range of each measuring system is the same, and different measuring systems use the same trigger signal, 3, different frequency bands should use the same shape of antenna as much as possible, 4, the single snapshot length of each frequency band should be kept the same as much as possible, which requires the switching mode of the radio frequency switch systems at the transmitting end and the receiving end to be kept as consistent as possible, or the difference is small.
Step S103, based on IFFT (-) of inverse Fourier transform, the impulse response h (t, tau) of the measurement channel is calculated and expressed as
h m，n (t，τ)＝IFFT{H m，n (t，f)}＝IFFT{Y r (t，f)/Y th (t，f)}，m＝1，...，M r ，n＝1，...，M t
Wherein Y is r (t, f) and Y th (t, f) each y r (t, t) and y th (t, τ) frequency domain transfer function. The dimension of channel impulse response in the expression is M t ×M r ×M f ×M s . Wherein M is t For number of transmitting antennas, M r For the number of receiving antennas, M f Is the number of sub-carriers, M s For fast channel beat, H (t, f) is the channel transmission matrix.
The step S2 specifically includes the following steps:
calculating frequency band f according to channel impulse response c The power spectral density of time delay of (2) is expressed as:
in the formula
Is dimension M f The x 1 vector, | · | represents taking the absolute value of each element in the matrix, and m, n represent the serial numbers of the receiving and transmitting antennas, respectively. Will M s Averaging the data of each snapshot to improve the signal-to-noise ratio and ensure M s Individual snapshots were measured in a static scene. The time-delayed power spectral densities of the two test frequency bands are shown in figure 2.
The step S3 specifically includes the following steps, and S301 to S303 are performed separately for each compared frequency band:
and S301, extracting all delay points in the effective diameter range of the delay power spectral density. The effective range is generally from 50 points before the peak value of the time delay power spectral density to 150 time delay points after the peak value of the time delay power spectral density, and can be adjusted according to actual conditions.
Step S302, determining all effective diameters of the time delay power spectrum density by adopting a mode of searching local maximum values. MATLAB may be employed @R And (5) controlling a precision parameter to screen the effective diameter by a medium findpeaks function, so that the number of the final effective diameter is similar to that of the effective diameter obtained by visual observation.
Step S303, selecting the time delay power spectrum density of the frequency band with less effective diameter as a reference, and matching the effective diameters of the time delay power spectrum densities of all the frequency bands: and aiming at each effective path of the reference time delay power spectrum density, selecting one path which is closest to the reference time delay power spectrum density in the time delay power density of the other frequency band. If no multipath exists in a certain range, setting the power of the time delay point of another frequency band to be 0; if the multipath time delay exists, the multipath time delay on the matching is set to be infinite, and the subsequent matching process is prevented from being searched again. The time delay power spectral densities of the two frequency bands are matched to eliminate the influence of different channel measurement equipment on the result. The power spectral density of the time delay before and after matching is shown in fig. 3-1 and fig. 3-2.
Step S304, calculating a time delay power spectral density correlation coefficient, wherein the expression is as follows:
wherein { } T Representing a transpose operation.
Step S305, calculating rho for all m, n τ,m,n And obtaining ρ τ,m,n Including CDF, mean and variance, etc. The CDF of the delay power spectral density correlation coefficient under LoS is shown in fig. 4, and fig. 4 also shows that the measured data is compared with the frequency domain similarity indexes of two standardized models, which can be used for the estimation and correction of the channel model.
The step S4 specifically includes the following steps, which are performed separately for each compared frequency band:
step S401, calculating the angular power spectrum density and the arrival angle power spectrum density of the channel
And angle of departure power spectrum
The Bartlett method is adopted here, and the expressions are respectively:
wherein { } H Indicating the Hermit transpose, c (theta, phi) indicates the steering vector for a pitch angle theta and an azimuth angle phi. Because this patent is mainly developed to steady channel, the angle power spectral density of different antenna pairs is closeTherefore, when calculating the angular power spectral density at the transmitting and receiving ends, the antenna pair with the highest signal-to-noise ratio may be selected for calculation.
Step S402, merging and backward quantizing the departure and arrival angle power spectrum density matrixes, namely:
where vec {. Cndot } represents an operation to vectorize the matrix.
The step S5 specifically includes the following steps:
step S501, calculating angle power spectral density correlation coefficients of two frequency bands, wherein the expression is as follows:
step S502, calculating rho for all f B,f And obtaining ρ B,f CDF, mean and variance, etc. The CDF of the angular power spectral density correlation coefficient in the LoS scenario is shown in fig. 5. Fig. 5 also shows the spatial similarity index comparison between the channel measurement data and the two standardized models, which can be used for the estimation and correction of the channel model.
The step S6 specifically includes the following steps, and S601 to S603 are performed for each compared frequency band individually:
step S601, calculating a channel gain after removing the small-scale fading:
where Δ τ and Δ t represent the delay resolution and the individual snapshot duration of the channel matrix, respectively. W represents the window length, typically 40 wavelengths.
Step S602, obtaining the path loss by utilizing the antenna gain of the transmitting and receiving end and the fitting of the receiving and transmitting power:
wherein d = τ LOS ·c，τ LOS Obtained from the time delay power spectral density, c represents the speed of light in vacuum;
representation by least squares fitting
And d.
Step S603, removing the path loss from the channel gain to obtain a small-scale fading value:
step S604, solving Pearson correlation coefficients of small-scale fading of two frequency bands:
wherein
ρ S When the frequency band is a negative number, the small-scale fading of the two frequency bands is in negative correlation, when the frequency band is a positive number, the positive correlation is represented, and 0 represents linear independence.
Step S605, calculate rho for all mobility measured routes S And obtaining ρ S Such as CDF, mean, variance, etc.
Step S701, merging the three indexes as follows:
ρ a ＝α 1 ρ τ,m,n +α 2 ρ B,f +α 3 ρ S
wherein alpha is 1 、α 2 And alpha 3 Respectively representing the weight of the similarity indexes of the frequency domain, the space domain and the time domain, and satisfying alpha 1 +α 2 +α 3 ＝1。α 1 、α 2 And alpha 3 The value of (c) can be set as desired. For example, when performing compression feedback on a multi-frequency codebook of an FDD system by using cross-frequency channel similarity, we mainly concern the similarity of space-frequency dimensions of different frequency band channels, and therefore set α 1 ＝0.5，α 2 ＝0.5，α 3 ＝0。
In summary, the invention provides a similarity index for analyzing a cross-frequency band wireless MIMO channel from three dimensions of space, time and frequency, and provides three indexes of a time delay power spectral density correlation coefficient, an angle power spectral density correlation coefficient and a small-scale fading correlation coefficient respectively. The method quantifies the similarity of the channels, can support the research of the similarity degree of the channels of different frequency bands, provides guidance opinions for the deployment of a cross-frequency band communication system, and provides a judgment standard for measuring the accuracy of a cross-frequency band wireless MIMO channel model.
It will be understood that the present invention is described in terms of actual channel measurement data and that various changes, modifications, and equivalents may be made to these features and embodiments without departing from the invention, as will be apparent to those skilled in the art. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (8)
1. A similarity quantization method of cross-band wireless MIMO channel is characterized by comprising the following steps:
s1, constructing a cross-frequency-band wireless MIMO channel measurement system, planning a test case, and calculating channel impulse response;
s2, calculating the time delay power spectrum density of each frequency band according to the channel impulse response;
s3, extracting effective data points of the time delay power spectrum density to calculate a time delay power spectrum correlation coefficient as a measurement index of frequency domain similarity;
s4, calculating Bartlett angle power spectrums of the transmitting and receiving ends of the channels in different frequency bands, combining the Bartlett angle power spectrums and then vectorizing the combined Bartlett angle power spectrums;
s5, calculating angle power spectrum correlation coefficients of channels in different frequency bands to serve as a measurement index of spatial domain similarity;
s6, after large-scale fading is removed, calculating small-scale fading correlation coefficients of mobile channels of different frequency bands to serve as a measurement index of time domain similarity;
and S7, endowing the similarity indexes of the three domains of space, time and frequency with different weights, and then combining to generate the final cross-frequency channel similarity index.
2. The method of claim 1, wherein the step S1 specifically includes the following steps:
s101, performing direct connection calibration on a channel detection system to obtain a direct connection calibration signal y th (t,τ)；
Step S102, obtaining a received signal y propagated by a wireless channel by using an antenna array r (t,τ)；
Step S103, based on IFFT (-) of inverse Fourier transform, the impulse response h (t, tau) of the measurement channel is calculated and expressed as
h(t,τ)＝IFFT{H(t,f)}＝IFFT{Y r (t,f)/Y th (t,f)}
Wherein Y is r (t, f) and Y th (t, f) are each y r (t, t) and y th (t, τ) frequency domain transfer function; the dimension of the channel impulse response in the expression is M t ×M r ×M f ×M s Wherein M is t For number of transmitting antennas, M r For the number of receiving antennas, M f Is the number of sub-carriers, M s H (t, f) is a channel transmission matrix.
3. The method of claim 2, wherein the cross-band MIMO wireless channel measurement meets the following requirements: 1) The bandwidth used by each frequency band is completely the same; 2) Each frequency band uses an independent measuring system, the dynamic range of each measuring system is the same, and different measuring systems use the same trigger signal; 3) The antennas with the same shape are used in different frequency bands; 4) The individual snapshot lengths of the individual frequency bands remain the same.
4. The method of claim 1, wherein the step S2 specifically comprises the following steps:
calculating frequency band f according to channel impulse response c The power spectral density of (a) is expressed as:
in the formula
Is dimension M f X 1 vector, | · | represents taking absolute value of each element in the matrix, M, n represent sequence numbers of receiving and transmitting antennas, respectively, M f Is the number of sub-carriers, M s Is the number of channel snapshots, and h (t, τ) is the channel impulse response.
5. The method of claim 1, wherein the step S3 specifically includes the following steps, and S301 to S303 are performed separately for each compared frequency band:
s301, extracting all time delay points in an effective diameter range of the time delay power spectrum density;
step S302, determining all effective diameters of the time delay power spectrum density by adopting a mode of searching local maximum values;
step S303, selecting the time delay power spectrum density of the frequency band with the least effective diameter as a reference, and matching the effective diameters of the time delay power spectrum densities of all the frequency bands;
step S304, calculating a time delay power spectral density correlation coefficient, wherein the expression is as follows:
wherein { } T It is shown that the transpose operation,
respectively representing frequency bands f 1 、f 2 The time delay power spectral density of (d);
step S305, calculating rho for all m, n τ,m,n And obtaining ρ τ,m,n The statistical information of (2).
6. The method as claimed in claim 1, wherein the step S4 specifically includes the following steps, which are performed separately for each compared bin:
step S401, calculating the angular power spectrum density and the arrival angle power spectrum density of the channel
And angle of departure power spectrum
The Bartlett method is adopted, and the expressions are respectively as follows:
wherein { } H Representing Hermit transposition, c (theta, phi) representing a steering vector when the pitch angle is theta and the azimuth angle is phi, H (t, f) H m,n Denotes the m-th transmitting antenna andchannel transmission matrix between n receiving antennas, M f Is the number of sub-carriers;
step S402, merging and backward quantizing the departure and arrival angle power spectrum density matrixes, namely:
where vec {. Cndot } represents an operation to vectorize the matrix.
7. The method of claim 6, wherein the step S5 specifically comprises the following steps:
step S501, calculating angle power spectral density correlation coefficients of two frequency bands, wherein the expression is as follows:
step S502, calculate ρ for all f B,f And obtaining ρ B,f The statistical information of (2).
8. The method of claim 1, wherein the step S6 specifically includes the following steps, and the steps S601 to S603 are performed separately for each compared frequency band:
step S601, calculating a channel gain after removing the small-scale fading:
where Δ τ and Δ t represent the delay resolution and the individual snapshot duration of the channel matrix, respectively, W represents the window length, M t For number of transmitting antennas, M r For the number of receiving antennas, M f Is the number of sub-carriers;
step S602, obtaining the path loss by utilizing the antenna gain of the transmitting and receiving end and the fitting of the receiving and transmitting power:
wherein d = τ LOS ·c，τ LOS Obtaining from the time-delayed power spectral density;
representation fitting by least squares
And d k The expression of (1);
step S603, removing the path loss from the channel gain to obtain a small-scale fading value:
step S604, solving Pearson correlation coefficients of small-scale fading of two frequency bands:
wherein
ρ S When the number is negative, the small-scale fading of the two frequency bands is in negative correlation, positive number represents positive correlation, and 0 represents linear independence;
step S605, calculating rho for all mobility measured routes S And obtaining ρ S The statistical information of (1).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211150732.XA CN115499044A (en) | 2022-09-21 | 2022-09-21 | Similarity quantization method for cross-frequency-band wireless MIMO channel |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211150732.XA CN115499044A (en) | 2022-09-21 | 2022-09-21 | Similarity quantization method for cross-frequency-band wireless MIMO channel |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115499044A true CN115499044A (en) | 2022-12-20 |
Family
ID=84469681
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211150732.XA Pending CN115499044A (en) | 2022-09-21 | 2022-09-21 | Similarity quantization method for cross-frequency-band wireless MIMO channel |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115499044A (en) |
-
2022
- 2022-09-21 CN CN202211150732.XA patent/CN115499044A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Decurninge et al. | CSI-based outdoor localization for massive MIMO: Experiments with a learning approach | |
| Costa et al. | Multiple-input multiple-output channel models: theory and practice | |
| CN103069759B (en) | The method of mimo channel status information estimation and receptor | |
| CN101541078B (en) | Method, system and device for estimating TDOA | |
| CN107015198B (en) | Indoor positioning method based on irregular arrangement of antennas | |
| US20080194207A1 (en) | System and method for estimating the multi-path delays in a signal using a spatially blind antenna array | |
| WO2022165872A1 (en) | Path parameter extraction method for millimeter wave 3d mimo channel | |
| CN104618041B (en) | A kind of channel data back method and device | |
| CN103424735B (en) | Based on the near-field sources localization method of minimum description length, Apparatus and system | |
| CN110809247B (en) | OFDM frequency domain error estimation and positioning precision evaluation method for indoor Wi-Fi positioning | |
| CN101489238B (en) | Time difference measuring method, system and apparatus | |
| WO2015124071A1 (en) | Dual-stream beamforming method and device | |
| WO2023000614A1 (en) | Wireless positioning parameter estimation method, apparatus and system, computer device, and storage medium | |
| CN105991175B (en) | A kind of transmission of pilot signal, receiving handling method and device | |
| CN105681232A (en) | Large-scale MIMO channel estimation method based on shared channel and compressed sensing | |
| CN112954791B (en) | Channel State Information (CSI) positioning method based on subcarrier screening | |
| CN115499044A (en) | Similarity quantization method for cross-frequency-band wireless MIMO channel | |
| CN101682472A (en) | Apparatus and method for evaluating conditions of propagation paths | |
| Tian et al. | MIMO CSI-based super-resolution AoA estimation for Wi-Fi indoor localization | |
| CN113630720B (en) | Indoor positioning method based on WiFi signal strength and generation countermeasure network | |
| KR101295131B1 (en) | A time domain equalizing apparatus and method using the omp algorithm in ofdm systems | |
| CN110299982B (en) | Multi-path channel side-writing method of Wi-Fi equipment based on limited bandwidth | |
| Aquino et al. | A Review of Direction of Arrival Estimation Techniques in Massive MIMO 5G Wireless Communication Systems | |
| Ndao et al. | Development and test of a trans-horizon communication system based on a MIMO architecture | |
| CN113938360A (en) | Distributed MIMO system covariance matrix estimation method based on fingerprint positioning |
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 |