CN116155326B - Method for estimating pseudomorphic channel under ultra-large-scale MIMO mixed field channel - Google Patents

Method for estimating pseudomorphic channel under ultra-large-scale MIMO mixed field channel Download PDF

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CN116155326B
CN116155326B CN202310418885.6A CN202310418885A CN116155326B CN 116155326 B CN116155326 B CN 116155326B CN 202310418885 A CN202310418885 A CN 202310418885A CN 116155326 B CN116155326 B CN 116155326B
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CN116155326A (en
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李汀
夏雨婷
张春洁
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Nanjing University of Posts and Telecommunications
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    • 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
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    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
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    • H04B7/0634—Antenna weights or vector/matrix coefficients
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • 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
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    • H04B7/0848—Joint weighting
    • H04B7/0854—Joint weighting using error minimizing algorithms, e.g. minimum mean squared error [MMSE], "cross-correlation" or matrix inversion
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Abstract

The invention provides a method for estimating a channel without pseudo peak under a super-large-scale MIMO mixed field channel, which comprises the steps of equally dividing a uniform line array formed by antennas intoNThe sub-arrays reflect the transmitted signals by the scatterers to the antenna array, give the received signals, perform angle estimation to obtain an angle set containing a pseudo peak, and determine a real angle after removing the angle corresponding to the pseudo peak by combining a pseudo peak judgment scheme; obtaining an angle estimation value of each subarray, judging the area of the subarray visible by a scatterer, distinguishing the angle of a far field and the angle of a near field, and determining a selection vector for describing the space non-stationary condition of a channel; determining the angle, near-field distance and far-field distance of a guide vector, and solving the gain coefficient of the path according to a least square method; constructing a MIMO mixed field channel model, and estimating a mixed field channel; the method greatly reduces the mean square error of channel estimation, effectively improves the channel estimation precision, and has good channel estimation performance.

Description

Method for estimating pseudomorphic channel under ultra-large-scale MIMO mixed field channel
Technical Field
The invention relates to a method for removing false peaks and estimating channels under a super-large-scale MIMO mixed field channel, and belongs to the technical field of MIMO channel estimation.
Background
As one of the key research directions of the next generation wireless communication system, a very large-scale multiple input multiple output (Multiple Input Multiple Output, MIMO) technology is expected to achieve higher spectral efficiency, wider coverage, and provide a lower latency and more reliable data network, and thus is attracting attention. As an important component of a wireless communication system, the application of massive MIMO technology brings many benefits to the development of wireless communication, but a premise of obtaining expected benefits is that a base station end can obtain accurate channel state information in advance, because many key technologies in the communication system depend on channel state information CSI. Abrupt changes may occur in the signal at the receiving end through the channel due to complex variability of the channel environment. In order for the receiving end to accurately recover the transmission signal, accurate CSI must be obtained between the base station and the user, so that the receiving end performs coherent demodulation, thereby improving performance of the communication system. Therefore, the research and design of the channel estimation problem are far-reaching in the improvement of the performance of the large-scale MIMO system.
In the existing channel model, most of the channel models assume that all scatterers are in a far-field area or a far-field channel model based on plane wave assumption is adopted; or in the near field region, a near field channel model based on spherical wave assumption is adopted. In practice, in a very large scale MIMO system, a mixed field channel environment may occur more easily, that is, a portion of the scatterers are located in the far field region of the very large scale array, and another portion of the scatterers may be distributed in the near field region. The communication channel at this time will consist of two path components, far field and near field. The existing channel model cannot match the characteristics of the far-field near-field mixed channel, and the existing channel model and channel estimation scheme are difficult to directly adopt.
Because of the fact that peak searching of far-field components in the mixed field may occur a false peak, the false peak may cause a larger mean square error of channel estimation. When channel estimation is performed in a super-large-scale mixed field MIMO environment, a pseudo peak exists when the peak value searches for an estimated angle, the value of the pseudo peak may be larger than the peak value corresponding to the real angle, and if the removal of the pseudo peak is not considered, the misjudgment of the angle is brought. However, the current channel model does not consider the influence of the false peak in the channel estimation, resulting in lower channel estimation accuracy and performance.
For example, chinese patent application CN202210056629.2 discloses a channel estimation method, apparatus, electronic device and storage medium, which also do not consider the case of occurrence of a false peak, and the channel estimation accuracy is low.
The above-mentioned problem is a problem that should be considered and solved in the course of de-pseudo-peak channel estimation in a very large-scale MIMO mixed field channel.
Disclosure of Invention
The invention aims to provide a method for removing false peaks in a super-large-scale MIMO mixed field channel, which solves the problems of larger mean square error and to-be-improved precision of channel estimation in the prior art without considering the false peaks.
The technical scheme of the invention is as follows:
a method for estimating the channel without pseudo peak under the ultra-large-scale MIMO mixed field channel includes such steps as,
s1, equally dividing a uniform line array formed by antennas intoNThe sub-arrays reflect the transmitted signals by the scatterers to reach the antenna array, give the received signals y, perform angle estimation to obtain an angle set containing a pseudo peak, and determine a real angle after removing the angle corresponding to the pseudo peak by combining a pseudo peak judgment scheme;
s2, carrying out one-dimensional search on the peak value by using a multiple signal classification algorithm, namely a MUSIC algorithm, obtaining an angle estimated value of each subarray, and respectively determining the angle estimated values of the subarray positioned at the center and the subarray positioned at the non-center according to the difference of the positions of the subarrays; determining the region of the subarray visible by the scatterer and distinguishing the angle of the far field from the angle of the near field;
s3, determining a selection vector for describing the space non-stationary condition of the channel in the area of the subarray visible by the scatterer
Figure SMS_1
；
S4, determining the angle and near-field distance of the guide vectorr s And far-field distance, according to the least square method, solving the gain coefficient of the path;
s5, simulating near-field and far-field paths by adopting a second-order approximate parabolic wave of the spherical wave, constructing a MIMO mixed field channel model, and estimating a mixed field channel from the constructed MIMO mixed field channel modelh hybrid-field 。
Further, in step S1, given a received signal y, an angle set including a pseudo peak is estimated by angle estimation, and after removing the angle corresponding to the pseudo peak in combination with the pseudo peak judgment scheme, a true angle is determined, specifically,
s11, judging the number S of visible scatterers according to the characteristic value of characteristic decomposition of a covariance matrix of a received signal y;
s12, under the mixed field environment, the number of the pseudo peaks brought about isf weifeng When the estimated angle takes peak value, takingf =S+f weifeng The angles corresponding to the peaks are arranged in order from small to large to obtain an angle set containing pseudo peaksθ 1 ,...,θ f ；
S13, for angle set containing pseudo peaksθ 1 ,...,θ f And judging that each angle is the angle corresponding to the false peak or the real angle, removing the angle corresponding to the false peak, and reserving the real angle.
Further, in step S12, the number of pseudo peaks in the mixed field environment isf weifeng ：
Figure SMS_2
，
Wherein,,Sfor the number of visible scatterers,nis the number of mixed signals.
Further, in step S13, each angle is determined to be the angle corresponding to the false peak or the true angle, specifically, since the existence of the false peak is in the middle of the true peak, the angles are ranked to be the smallest angleθ 1 And the maximum angleθ f The angle is a true angle, and the middle angle is judged according to the arcsin function value to obtainf weifeng And removing the pseudo peaks to obtain the real angle.
Further, in step S13, the intermediate angle is determined according to the arcsin function value, specifically, the angle γ corresponding to the dummy peak is between the two real far-field angles α and β, and satisfies 2sin γ=sin α+sin β, and the angle γ corresponding to the dummy peak
Figure SMS_3
。
Further, in step S2, the angle of the far field and the angle of the near field are distinguished, specifically,
when the angle values of adjacent subarrays visible by the same scatterer are in ascending or descending order and the angle difference value of the adjacent subarrays is not larger than a set value, the scatterer is positioned in a near field, and the angle is judged to be the angle of the near field;
when the angle values of the scatterers visible in the subarray are identical, the scatterers are positioned in the far field, and the angle is determined as the angle of the far field.
Further, in step S3, a selection vector describing the spatially non-stationary situation of the channel is determined
Figure SMS_4
Specifically, the->
Figure SMS_5
The m-th element of (2) is defined as +.>
Figure SMS_6
Wherein, the method comprises the steps of, wherein,Lfor the number of antennas to be used,Nin the case of a sub-array of arrays,
Figure SMS_7
refers to the phase parameter of the s-th scatterer.
Further, in step S4, the angle of the steering vector and the near field distance are determinedr s And far field distances, in particular,
s41, separating the angle and the distance of the guide vector, and respectively carrying out one-dimensional search by using a MUSIC algorithm to obtain an estimated value of the angle of the guide vector, wherein when the subarray at the center is visible, the angle of the guide vector is directly determined by the estimated angle of the subarray at the center; when the centrally located subarray is not visible, the angle of the steering vector is solved by the two visible subarray angles closest to the centrally located subarray;
s42, when the scatterer is positioned in the near field, estimating the distance from the subarray closest to the edge in the visible region to determine the near field distancer s ：
Figure SMS_8
Wherein, it is characterized byθ，r) Indicating the angle and distance of the scatterer to the reference point, < >>
Figure SMS_9
Representing the estimated angle +.>
Figure SMS_10
As a steering vector related to the angle and distance of the scatterer to the reference point,U n is a noise subspace>
Figure SMS_11
Representing a transpose of the matrix;
s43, when the scatterer is located in the far field, the distance of the scatterer is infinity, and the distance of the far field is determined to be infinity.
Further, in step S4, gain coefficients of S paths are obtained according to the least square method [g 1 ...g s ]：
Figure SMS_12
，
Wherein the direction matrix
Figure SMS_14
Dimension is->
Figure SMS_19
，M=（L-1）/2，LFor the number of antennas to be used,Sdirection matrix of s paths for the number of visible scatterersAConsists of a steering vector and a selection vector:
Figure SMS_21
wherein->
Figure SMS_13
Respectively the angle of the 1 st scatterer to the reference pointθ 1 And near field distancer 1 Related steering vectors, < >>
Figure SMS_16
Respectively represent the firstsAngle of individual scatterers to reference pointθ s And near field distancer s Related steering vectors, < >>
Figure SMS_18
A selection vector for describing the spatially non-stationary properties of the channel, wherein +.>
Figure SMS_20
Phase parameter representing the 1 st scatterer, < ->
Figure SMS_15
Representing the phase parameter of the s-th scatterer, y being the received signal, ">
Figure SMS_17
Representing the transpose of the matrix.
Further, in step S5, a hybrid channel is estimated from the constructed MIMO hybrid field channel modelh hybrid-field ：
Figure SMS_22
，
Wherein,,Sfor the number of visible scatterers,
Figure SMS_23
is the gain factor of the s-th path, +.>
Figure SMS_24
Represent the firstsAngle of individual scatterers to reference pointθ s And near field distancer s Related steering vectors, < >>
Figure SMS_25
Selection vector for describing spatially non-stationary characteristics of a channel, < >>
Figure SMS_26
Refers to the phase parameter of the s-th scatterer.
The beneficial effects of the invention are as follows: the method for removing the false peak channel estimation under the ultra-large-scale MIMO mixed field channel can realize the determination and effective removal of the false peak, and only retains the true peak, so that the correct angle estimation result can be accurately obtained, the subsequent estimation distance is accurate, and the channel is estimated with high precision. Compared with the existing scheme without considering the false peak, the method has the advantages that the mean square error of channel estimation is greatly reduced, the channel estimation precision is effectively improved, and the channel estimation performance is good through verification of the simulation result.
Drawings
Fig. 1 is a flow chart of a method for estimating a channel with a spurious peak removal in a super-large-scale MIMO mixed field channel according to an embodiment of the present invention;
FIG. 2 is an explanatory diagram of a mixed field in a wireless communication system in the embodiment;
fig. 3 is a schematic diagram showing the comparison of channel estimation of the method for removing the pseudo-peak in the ultra-large-scale MIMO mixed field channel and the scheme for not removing the pseudo-peak in the existing method;
FIG. 4 is a graph of mean square error of channel estimation under the influence of different numbers of scatterers of a mixed field in an embodiment;
FIG. 5 is a schematic illustration of different scattering environments in an embodiment, where (a) is a schematic illustration of environment 1, (b) is a schematic illustration of environment 2, (c) is a schematic illustration of environment 3, and (d) is a schematic illustration of environment 4;
fig. 6 is a graph showing mean square error versus scattering environment for various embodiments.
Description of the embodiments
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
An embodiment provides a method for de-pseudo channel estimation in a super-large-scale MIMO mixed field channel, as shown in fig. 1, comprising the following steps,
s1, equally dividing a uniform line array formed by antennas intoNThe sub-arrays reflect the transmitted signals by the scatterers to reach the antenna array, give the received signals y, perform angle estimation to obtain an angle set containing a pseudo peak, and determine a real angle after removing the angle corresponding to the pseudo peak by combining a pseudo peak judgment scheme;
in step S1, given a received signal y, an angle set including a false peak is estimated by angle estimation, and after removing the angle corresponding to the false peak in combination with the false peak judgment scheme, a true angle is determined, specifically,
s11, judging the number S of visible scatterers according to the characteristic value of characteristic decomposition of a covariance matrix of a received signal y;
in step S11, since the characteristic value corresponding to the signal subspace is larger and the characteristic value corresponding to the noise subspace is smaller and tends to 0, the number S of visible scatterers can be determined by distinguishing the signal subspace from the noise subspace according to the magnitude of the characteristic value.
S12, under the mixed field environment, the number of the pseudo peaks brought about isf weifeng When the estimated angle takes peak value, takingf =S+f weifeng The angles corresponding to the peaks are arranged in order from small to large to obtain an angle set containing pseudo peaksθ 1 ,...,θ f ；
In step S12, the number of pseudo peaks in the mixed field environment isf weifeng ：
Figure SMS_27
，
Wherein S is the number of visible scatterers, and n is the number of mixed signals.
S13, for angle set containing pseudo peaksθ 1 ,...,θ f And judging that each angle is the angle corresponding to the false peak or the real angle, removing the angle corresponding to the false peak, and reserving the real angle.
In step S13, the intermediate angle is determined according to the arcsin function value, specifically, the angle γ corresponding to the dummy peak is between the two real far-field angles α and β, and satisfies 2sin γ=sin α+sin β, and the angle γ corresponding to the dummy peak
Figure SMS_28
. For angle set containing false peaksθ 1 ,...,θ f Since the existence of the pseudo peaks is in the middle of the true peak, the minimum angles are orderedθ 1 And the maximum angleθ f Must be true angle due to the pseudo-peak gamma andθ 1 、θ f has the following functional relation: 2sin γ=sinθ 1 +sinθ f Therefore, it isThe intermediate angle is determined based on the arcsin function, e.g. whenθ mid =arcsin[（sinθ 1 +sinθ f ）/2]In the time-course of which the first and second contact surfaces,θ mid the corresponding peak is a pseudo peak, which is removed; obtainingf weifeng And removing the pseudo peaks to obtain the real angle.
Case 1: if the scatterer S=3, take
Figure SMS_29
The estimated 6 angle values are ordered from small to large as peak valuesθ 1 ,θ 2 ,θ 3 ,θ 4 ,θ 5 ,θ 6 Wherein the angle values comprise 3 true angle values and 3 false angle values caused by false peaks, and the existence of the false peaks always occurs in the middle of the true peaks, thereforeθ 1 Andθ 6 it must be a true value of the angle,θ 2 andθ 5 must be the false estimated value corresponding to the false peak, and also the angle value corresponding to the false peak should be arcsinθ 1 +sinθ 6 ）/2]From this, it can be judged thatθ 3 Andθ 4 which is the true angle and which is the angle to which the dummy peak corresponds. If the obtained arcsin function valueθ 3 If they are consistent with each otherθ 3 Namely the 3 rd real angle is the 3 rd real angle,θ 4 is the 3 rd pseudo-peak angle. Conversely, if the obtained arcsin function valueθ 4 If they are consistent with each otherθ 4 At the 3 rd true angle of the angle,θ 3 the 3 rd dummy peak corresponds to an angle.
Case 2: if the scatterer S=4, take
Figure SMS_30
The estimated 10 angle values are sequentially sequenced from small to large as peak valuesθ 1 ,θ 2 ,...,θ 9 ,θ 10 Wherein the angle values comprise 4 real angle values and 6 false angle values caused by false peaks, and the false angle values are generated by the false angle valuesThe presence of peaks always occurs in the middle of the true peak, soθ 1 Andθ 10 it must be a true value of the angle,θ 2 andθ 9 must be the false estimated value corresponding to the false peak, andθ 2 is fixed atθ 1 And another true angle value, i.e. 2sinθ 2 =sinθ 1 +sinθCan obtainθ=arcsin（2sinθ 2 -sinθ 1 ) May correspond toθ 3 Or (b)θ 4 Or (b)θ 5 If it is found thatθValue and value ofθ 3 If they are consistent with each otherθ 3 For the 3 rd true value of the value,θ 4 andθ 5 is a false peak. If found outθValue and value ofθ 4 Or (b)θ 5 Corresponding to the same. Another true angle value is obtained in the same manner as above.
Similarly, the method is still applicable to the situation that the far-near field scatterers with different numbers are mixed according to the general idea of channel estimation in the mixed field environment. The method can search out all peaks possibly containing false peaks, list the angles from small to large, distinguish the peaks corresponding to the true angle values of the channels from a plurality of false peaks according to the arcsin function relation between the true angle and the misjudgment angle, finish the correct judgment of the angles, and further improve the accuracy of channel estimation.
S2, carrying out one-dimensional search on the peak value by using a multiple signal classification algorithm, namely a MUSIC algorithm, obtaining an angle estimated value of each subarray, and respectively determining the angle estimated values of the subarray positioned at the center and the subarray positioned at the non-center according to the difference of the positions of the subarrays; determining the region of the subarray visible by the scatterer and distinguishing the angle of the far field from the angle of the near field;
in step S2, when estimating the angle for each sub-array, since the steering vectors in the far-near field mixed field model are required to be written in the same form, the steering vectors are first split into two matrices for multiplication, i.e. the steering vectors are expressed as
Figure SMS_31
Matrix->
Figure SMS_32
Angle and angle ofθSum distancerIn relation, wherein C represents a complex domain, the dimension is +.>
Figure SMS_33
，M=（L-1）/2，LThe number of antennas. Matrix->
Figure SMS_34
Only with angleθIn relation, wherein C represents a complex domain, the dimension is +.>
Figure SMS_35
，M=（L-1)/2, andLthe number of antennas. And has the following steps:
Figure SMS_36
，
wherein,,
Figure SMS_37
for matrix elements that are related only to the angle of the scatterer to the reference point,jin units of imaginary numbers,
Figure SMS_38
，θfor the angle of the scatterer to the reference point, +.>
Figure SMS_39
As a function of the wavelength of the signal,dis the distance between the adjacent array elements,rwhich is the distance of the signal to the intermediate antenna.
For far field signals, the distance is nowrIs infinite.
From signal subspace theory, when the spatial spectrum function
Figure SMS_40
The peak value is taken, wherein,n（θ，r) Andm（θ) Matrix obtained by splitting the guide vectors respectively, < >>
Figure SMS_41
Representing transposes of matrices, i.e.n H （θ，r）M（θ）n（θ，r) When=0, due ton（θ，r) Not equal to 0, so the condition for taking the peak isM（θ) Is a singular matrix of the number of the pixels,M（θ) Decreasing rank, wherein->
Figure SMS_42
，U n Is a noise subspace. Noise subspaceU n Rank of 2M+1-SDue to the number of scattering bodiesS≤MWherein, the method comprises the steps of, wherein,M=（L-1）/2，Lis the number of antennas, so the noise subspaceU n Rank of greater than or equal toM+1, can be obtainedM（θ) In full order, only when the angle parameter takes the actual value,M（θ) And (5) reducing rank. So when solving the angle, only one-dimensional search is needed, the spatial spectrum function is as follows: />
Figure SMS_43
Wherein, the method comprises the steps of, wherein,
Figure SMS_44
the value of the determinant is represented.
In step S2, the region of the subarray where the scatterer is visible is determined: when the angles of all the subarrays are obtained, the angle values of the adjacent subarrays visible by the same scatterer have a certain ascending or descending order, and the angle difference of the adjacent subarrays is smaller, so that the subarray area visible by the scatterer can be judged according to the obtained angle values, and the corresponding relation between the scatterer and the subarray can be obtained.
In step S2, distinguishing the angle of the far field from the angle of the near field, specifically, when the angle values of adjacent subarrays visible by the same scatterer have increasing or decreasing order, and the angle difference value of the adjacent subarrays is not greater than a set value, the scatterer is located in the near field, and the angle is determined as the angle of the near field; when the angle values of the scatterers visible in the subarray are identical, the scatterers are positioned in the far field, and the angle is determined as the angle of the far field. Since the mixed field model has only 2 cases of near field and far field, the angle judgment has only the above 2 cases of near field angle and far field angle.
S3, determining a selection vector for describing the space non-stationary condition of the channel in the area of the subarray visible by the scatterer
Figure SMS_45
；
In step S3, a selection vector describing the spatially non-stationary situation of the channel is determined
Figure SMS_46
Specifically, the->
Figure SMS_47
Is the first of (2)mThe individual elements are defined as->
Figure SMS_48
Wherein, the method comprises the steps of, wherein,Lfor the number of antennas to be used,Nis subarray (sub array)>
Figure SMS_49
Refers to the phase parameter of the s-th scatterer. For example, a diffusersThe visible region is subarrays 1-5, and the corresponding element of subarrays 1-5 is 1, i.e. +.>
Figure SMS_50
WhereinM n For the nth sub-array antenna number, the remaining invisible sub-array corresponding elements are 0. By increasing the selection vector, the spatially non-stationary nature of the channel can be accurately described.
S4, determining the angle and near-field distance of the guide vectorr s And far-field distance, according to the least square method, solving the gain coefficient of the path;
in step S4, the angle of the steering vector and the near field distance are determinedr s And far field distances, in particular,
s41, separating the angle and the distance of the guide vector, and respectively carrying out one-dimensional search by using a MUSIC algorithm to obtain an estimated value of the angle of the guide vector, wherein when the subarray at the center is visible, the angle of the guide vector is directly determined by the estimated angle of the subarray at the center; when the centrally located subarray is not visible, the angle of the steering vector is solved by the two visible subarray angles closest to the centrally located subarray;
s42, when the scatterer is positioned in the near field, estimating the distance from the subarray closest to the edge in the visible region to determine the near field distancer s ：
Figure SMS_51
Wherein, it is characterized byθ，r) Indicating the angle and distance of the scatterer to the reference point, < >>
Figure SMS_52
Representing the estimated angle +.>
Figure SMS_53
As a steering vector related to the angle and distance of the scatterer to the reference point,U n is a noise subspace>
Figure SMS_54
Representing a transpose of the matrix;
s43, when the scatterer is located in the far field, the distance of the scatterer is infinity, and the distance of the far field is determined to be infinity.
In step S4, gain coefficients of S paths are obtained according to least square methodg 1 ...g s ]：
Figure SMS_55
，
Wherein the direction matrix
Figure SMS_57
Dimension is->
Figure SMS_62
，M=（L-1）/2，LFor the heavenThe number of lines, S, is the number of visible scatterers, and the direction matrix of S pathsAConsists of a steering vector and a selection vector:
Figure SMS_64
wherein->
Figure SMS_58
Respectively the angle of the 1 st scatterer to the reference pointθ 1 And near field distancer 1 Related steering vectors, < >>
Figure SMS_60
Respectively represent the firstsAngle of individual scatterers to reference pointθ s And near field distancer s Related steering vectors, < >>
Figure SMS_61
A selection vector for describing the spatially non-stationary properties of the channel, wherein +.>
Figure SMS_63
Refers to the phase parameter of the 1 st scatterer, ">
Figure SMS_56
Finger numbersThe phase parameters of the individual scatterers are, y is the received signal, ">
Figure SMS_59
Representing the transpose of the matrix.
S5, simulating near-field and far-field paths by adopting a second-order approximate parabolic wave of the spherical wave, constructing a MIMO mixed field channel model, and estimating a mixed field channel from the constructed MIMO mixed field channel modelh hybrid-field 。
In step S5, a channel is estimated from the constructed MIMO mixed field channel modelh hybrid-field ：
Figure SMS_65
，
Wherein,,s is the number of visible scatterers,g s is the firstsThe gain coefficients of the paths are such that,
Figure SMS_66
represent the firstsAngle of individual scatterers to reference pointθ s And near field distancer s Related steering vectors, < >>
Figure SMS_67
Selection vector for describing spatially non-stationary characteristics of a channel, < >>
Figure SMS_68
Finger numbersPhase parameters of the individual scatterers.
In step S5, as shown in fig. 2, electromagnetic field radiation in the wireless communication system may be divided into a far field and a near field, and different channels are generated by different fields, and the following description is given for constructing a MIMO mixed field channel model:
first, for far-field channels, i.e. when the distance of the scatterer from the base station is greater than the rayleigh distance, the far-field channelh far-field Is assumed by plane waves, i.e
Figure SMS_69
Wherein, the method comprises the steps of, wherein,F s is the number of far-field path components,g s ，θ s respectively the firstsGain coefficient and angle of the strip path, +.>
Figure SMS_70
Is a steering vector that is related to angle only:
Figure SMS_71
wherein d is the spacing between adjacent antennas, < > and->
Figure SMS_72
J is the imaginary unit for the wavelength of the signal,M=（L-1)/2 andLfor the number of antennas>
Figure SMS_73
Is the first tosMatrix elements of the steering vector that are related to the angle of the path.
Then, for the near field channel, i.e. when the distance of the scatterer from the base station is not greater than the rayleigh distanceh near-field Is assumed by adopting the second-order approximate parabolic wave of the spherical wave,
Figure SMS_74
wherein, the method comprises the steps of, wherein,N s is the number of near-field path components,g s is the firstsThe gain coefficients of the paths are such that,θ s andr s the angle and distance of the first path are represented respectively,
Figure SMS_75
is a steering vector related to both angle and distance, its elements are defined as:
Figure SMS_76
wherein, the method comprises the steps of, wherein,dexp is an exponential function based on a natural constant e, which is the interval between adjacent antennas, ++>
Figure SMS_77
As a function of the wavelength of the signal,jin units of imaginary numbers,M=（L-1)/2 andLfor the number of antennas, m refers to the m-th antenna array element number m.
Thus, channel model of mixed fieldh hybrid-field ：h hybrid-field =h far-field +h near-field 。
To unify the expression form of the steering vector, the hypothesized steering vector of the plane wave in the far-field channel is used
Figure SMS_78
Written in the same form as the approximation of a spherical wave hypothesis using a parabolic wave model, i.e
Figure SMS_79
Wherein, the method comprises the steps of, wherein,dexp is an exponential function based on a natural constant e, which is the interval between adjacent antennas, ++>
Figure SMS_80
J is the imaginary unit for the wavelength of the signal,M=（L-1)/2 andLfor the number of antennas, m refers to the m-th antenna array element number m.
Due to the far-field steering vector at this point
Figure SMS_81
Distance in (a)r s Is infinite, i.er s = infinity, at this time the distance-related term in the steering vector +.>
Figure SMS_82
Trend towards 0, far field steering vector +.>
Figure SMS_83
Only with angleθ s Related to the following.
The base station is composed ofL=2MA uniform linear array of +1 antennas with spacing between adjacent antennas ofdThe signal transmitted by the user equipment is reflected along the line-of-sight path or by the scatterer to the antenna array, the embodiment taking into account only the last hop of the scatterer. When considering the non-stationary condition of the channel, the uniform linear array is equally divided intoNSub-arrays, assuming the number of antennas per sub-array isP n ，P n =L/NModeling far-field channels
Figure SMS_84
And near field channel model
Figure SMS_85
According to the relation between the mixed field channel model and the far field channel model and the near field channel model:h hybrid-field =h far-field +h near-field substituting into mixed field channel model, adding selection vector to describe space non-stationary characteristic of channel to obtain mixed field signalAnd (3) a channel model:
Figure SMS_86
，
wherein,,Sfor the number of scatterers,g s for the gain factor of the s-th path,
Figure SMS_89
representing the inner product of Hadamard->
Figure SMS_91
Selection vector representing spatially non-stationary characteristics describing the channel,/->
Figure SMS_94
Refers to the phase parameter of the s-th scatterer, < >>
Figure SMS_88
For the last jump of the scatterer s to the reference point angle and distance,/for the angle and distance of the last jump of the scatterer s to the reference point>
Figure SMS_90
For angle-and distance-dependent guiding vectors, the guiding vector in the mixed field is +.>
Figure SMS_93
Guiding vector in near field +.>
Figure SMS_95
The expressions of (2) are identical, i.e. the elements thereof are defined as +.>
Figure SMS_87
Wherein, the method comprises the steps of, wherein,dexp is an exponential function based on a natural constant e, which is the interval between adjacent antennas, ++>
Figure SMS_92
J is the imaginary unit for the wavelength of the signal,M=（L-1)/2 and is the number of antennas, m means the m-th antenna array element number m.
The method for removing the false peak channel estimation under the ultra-large-scale MIMO mixed field channel can realize the determination and effective removal of the false peak, and only retains the true peak, so that the correct angle estimation result can be accurately obtained, the subsequent estimation distance is accurate, and the channel is estimated with high precision. Compared with the existing scheme without pseudo peaks, the method greatly reduces the mean square error of channel estimation, effectively improves the channel estimation precision and has good channel estimation performance.
According to the method for removing the false peaks under the ultra-large-scale MIMO mixed field channel, under the ultra-large-scale mixed field MIMO environment, the existence of the false peaks is proved, and then when spectral peak searching is carried out on the estimated angles of the sub-arrays, the false peaks are firstly removed, the true peaks are left, the correct angle estimation result is truly and accurately obtained, so that the subsequent estimation distance is accurate, and the channel is correctly estimated. Taking the example that two scatterers appear in the mixed field channel and are located in the far field, the presence of a spurious peak is demonstrated as follows:
assuming that two scatterers are located in the far field region in the mixed field, the angles of the far field in the steering vector are alpha and beta, respectively, without loss of generality, assuming alpha < beta, for the scatterers located in the far field, the matrix is thenn f ：
Figure SMS_98
. When the spatial spectral function takes a peak, i.e.>
Figure SMS_100
，/>
Figure SMS_102
Wherein, the method comprises the steps of, wherein,U n is a noise subspace. The method can obtain: />
Figure SMS_96
，/>
Figure SMS_99
Thus, it is->
Figure SMS_101
. Due to->
Figure SMS_103
At this time, there is an angle γ such that
Figure SMS_97
Wherein, vector t is:
Figure SMS_104
。
matrix is formedtSum matrixm（α）、m（β) Matrixn f Matrix to be solvedm（γ) Substituted into the relation
Figure SMS_105
In the process, the angle gamma and the angle are obtained by solvingαAnd beta: gamma satisfies 2sin gamma=sinα+sin beta andα<γ<beta. At this time, the liquid crystal display device,m（γ) Satisfy->
Figure SMS_106
I.e. +.>
Figure SMS_107
. Due to the matrixtNot equal to 0, only when +.>
Figure SMS_108
In the case of singular matrices, < >>
Figure SMS_109
If true, spatial spectrum function->
Figure SMS_110
During searching, a pseudo peak appears, the corresponding angle is gamma, gamma is between two real far-field angles alpha and beta, and 2sin gamma=sin alpha+sin beta is satisfied. That is, when performing an angle search in the mixed field channel, there is a false peak between the spectrum peaks of two real angles, and the false peak may be higher than the peak corresponding to the real angle value.
When the two signals are mixed, the number of pseudo peaks is
Figure SMS_111
The number of pseudo peaks when mixing the three signals +.>
Figure SMS_112
For n signal mixes, there will be a false peak between every two signal peaks, i.e. the total number of false peaks is +.>
Figure SMS_113
From combination->
Figure SMS_114
Is a mathematical calculation formula of (a): />
Figure SMS_115
Wherein, the method comprises the steps of, wherein,m<n. Order them=2, thennThe total number of pseudo peaks is +.>
Figure SMS_116
Wherein, the method comprises the steps of, wherein,nthe ∈A represents the factorial of a positive integer +.>
Figure SMS_117
。
According to the channel estimation method for removing the false peaks under the ultra-large-scale MIMO mixed field channel, the fact that the false peaks appear when the estimated angles are used for carrying out peak value searching is considered, a general channel estimation scheme aiming at the existence of the false peaks is provided, when the estimated angles are used for carrying out one-dimensional searching, the false peaks and the peak values corresponding to the real angles are searched, the real angles of the channel are judged according to the functional relation between the angles corresponding to the false peaks and the real angles, the angles corresponding to the false peaks are removed, the real peaks are reserved to obtain the real and accurate angle estimation result, and therefore channel estimation is carried out by using the real angles, and the channel estimation precision is effectively improved.
After the angle of each subarray is accurately determined, the angles of a far field and a near field are distinguished, then the angle estimation value and the characteristic value are combined, the area of the subarray visible by the scatterer is judged, the corresponding relation between the scatterer and the subarray can be obtained, then the angle of a guiding vector is determined, the distance of the near field is obtained, the distance of the corresponding far field is infinity, and finally, the distance is estimated, and the path gain is estimated by a least square method, so that a channel estimation result is obtained. In the method, in a channel environment with a scatterer positioned in a near field and a far field, a second-order approximate parabolic wave of a spherical wave is used for simulating a near field path and a far field path, and the distance of a far field guiding vector is set to be infinity. The angle and the distance can be split by adopting a method under a near field environment, the angle is estimated first, and then the distance is estimated, and the method has the advantages that: compared with the classical two-dimensional MUSIC algorithm, the angle and the distance of the guide vector are searched separately, and the number of complex multiplication is greatly reduced and the complexity is reduced by performing one-dimensional search twice.
According to the channel estimation method for removing the false peaks under the ultra-large-scale MIMO mixed field channel, the angle is estimated first by splitting the angle and the distance, and then the distance is estimated, so that the complexity of channel estimation is reduced. In determining the angle in the steering vector, the angle of the steering vector is determined directly from the angle estimated for the centered subarray when the centered subarray is visible. When the centrally located sub-array is not visible, the angle in the steering vector cannot be estimated directly from the central sub-array, and the estimated angle is more accurate by using the two visible sub-arrays closest to the centrally located sub-array.
According to the method for estimating the channel with the spurious peaks removed under the ultra-large-scale MIMO mixed field channel, all peaks possibly containing the spurious peaks can be searched out in a spatial spectrum function, angles of the peaks are listed from small to large, the peaks corresponding to the true angle values of the channel are distinguished from the spurious peaks according to the arcsin function relation between the true angle and the misjudgment angle, correct judgment of the angles is completed, and further accuracy of channel estimation is improved.
The experimental results of the method for estimating the channel with the spurious peaks removed under the ultra-large-scale MIMO mixed field channel in the embodiment are as follows:
fig. 3 is a schematic diagram showing the comparison of channel estimation of the super-large-scale MIMO mixed field channel and the channel estimation of the scheme without removing the pseudo-peak in the prior art. As shown in fig. 3, as the signal-to-noise ratio increases, the mean square error of both channel estimates decreases; however, compared with the existing method, the method for removing the false peak has smaller mean square error, higher accuracy and better channel estimation performance in the channel estimation, and the method for removing the false peak has the necessity without considering that the false peak can influence the accuracy of the channel estimation in the mixed field.
Fig. 4 is a schematic diagram of the mean square error of channel estimation under the influence of different numbers of scatterers of a mixed field in an embodiment. As can be seen from fig. 4, the mean square error of the channel estimation of the embodiment method is smaller under the influence of different numbers of scatterers, and the mean square error of the channel estimation decreases with the increase of the signal-to-noise ratio. The embodiment method can realize channel estimation with high accuracy.
FIG. 5 is a schematic illustration of different scattering environments in an embodiment. In fig. 5, the sub-arrays 1 to 8 are shown in the same manner as the visible regions of the scatterers S1 located in the near field are shown in the environment 1 (a) and the environment 2 (b), the visible regions of the scatterers S2 and S3 located in the far field are shown in the sub-arrays 3 to 10 and 6 to 14, respectively, and the visible regions of the scatterers S2 and S3 located in the far field are shown in the sub-arrays 1 to 8 and 8 to 15, respectively. In fig. 5, (c) the environment 3 and (d) the environment 4 have the visible regions of the scatterers S2 and S3 located in the far field, respectively, 1 to 8 and 8 to 15, and (c) the environment 4 has the visible regions of the scatterer S1 located in the near field, respectively, 3 to 10 and 1 to 5.
FIG. 6 is a graph showing the mean square error versus the signal to noise ratio for four different scattering environments, namely, the mean square error (Mean Square Error, MSE) of the channel estimates for different scattering environments, for environment 1, environment 2, environment 3, and environment 4 of FIG. 5, where MSE passes
Figure SMS_118
To calculate, among othershFor real channel->
Figure SMS_119
For the mixed field channel estimated in step S5,/i>
Figure SMS_120
Representing the range of the vectorA number. As shown in fig. 6, in the case of the environment 1 and the environment 2, the mean square errors of the channels are almost coincident, and according to the analysis of the arrangement conditions of the two, the sub-arrays adopted for estimating the angle and the distance of the near-field scatterer are the same, and the most accurate sub-arrays can be used for estimating the channel of the environment 1 and the environment 2, so that the mean square error of the channel estimation of the environment 1 is minimum, the visible areas of the scatterers located in the far field are different from the visible areas of the environment 1 and the environment 2, but the difference of the far-field angles is not very large, and therefore, the difference of the visible areas of the far-field does not affect the mean square error of the channel estimation. The scatterer visible subarray areas located in the far field provided in the environment 2, the environment 3, and the environment 4 are the same, but the scatterer visible areas located in the near field are different. In environment 2 the scatterer estimates the angle with the subarray in the center, the most marginal subarray estimates the distance, and in environment 3 the scatterer estimates the angle with the subarray in the center, but the subarray estimating the distance is less accurate than in environment 1, so the channel mean square error of environment 3 is greater than that of environment 2. The angle estimation value of the environment 4 is firstly less accurate than that of the environments 2 and 3, so that the mean square error of the channel estimation is maximum. As can be seen from fig. 6, the mean square error of the channel estimation in the case of environment 1, environment 2, environment 3 and environment 4 is smaller, and the mean square error of the channel estimation decreases with the increase of the signal-to-noise ratio.
Simulation results show that compared with a channel estimation scheme without considering pseudo peaks, the method for removing pseudo peaks under the ultra-large-scale MIMO mixed field channel of the embodiment greatly reduces the mean square error of channel estimation, effectively improves the channel estimation precision, has good channel estimation performance, and can well estimate the channel by considering the non-stable condition of the channel under the mixed field environment.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.

Claims (6)

1. A method for removing false peak channel estimation under ultra-large scale MIMO mixed field channel is characterized in that: comprises the steps of,
s1, equally dividing a uniform line array formed by antennas intoNThe sub-arrays reflect the transmitted signals by the scatterers to reach the antenna array, give the received signals y, perform angle estimation to obtain an angle set containing a pseudo peak, and determine a real angle after removing the angle corresponding to the pseudo peak by combining a pseudo peak judgment scheme;
in step S1, given a received signal y, an angle set including a false peak is estimated by angle estimation, and after removing the angle corresponding to the false peak in combination with the false peak judgment scheme, a true angle is determined, specifically,
s11, judging the number S of visible scatterers according to the characteristic value of characteristic decomposition of a covariance matrix of a received signal y;
s12, under the mixed field environment, the number of the pseudo peaks brought about isf weifeng When the estimated angle takes peak value, takingf =S+ f weifeng The angles corresponding to the peaks are arranged in order from small to large to obtain an angle set containing pseudo peaksθ 1 ,...,θ f ；
S13, for angle set containing pseudo peaksθ 1 ,...,θ f Judging that each angle is the angle corresponding to the false peak or the real angle, removing the angle corresponding to the false peak, and reserving the real angle;
in step S13, each angle is determined to be the angle corresponding to the false peak or the true angle, specifically, the angles are the smallest in order since the existence of the false peak is in the middle of the true peakθ 1 And the maximum angleθ f The angle is a true angle, and the middle angle is judged according to the arcsin function value to obtainf weifeng After the pseudo peaks are removed, obtaining a real angle;
wherein the middle angle is judged according to the arcsin function value, specifically, the angle gamma corresponding to the pseudo peak is between two real far-field angles alpha and beta, and satisfies 2sin gamma=sinα+sinβ, then the angle corresponding to the pseudo-peak
Figure QLYQS_1
；
S2, carrying out one-dimensional search on the peak value by using a multiple signal classification algorithm, namely a MUSIC algorithm, obtaining an angle estimated value of each subarray, and respectively determining the angle estimated values of the subarray positioned at the center and the subarray positioned at the non-center according to the difference of the positions of the subarrays; determining the region of the subarray visible by the scatterer and distinguishing the angle of the far field from the angle of the near field;
s3, determining a selection vector for describing the space non-stationary condition of the channel in the area of the subarray visible by the scatterer
Figure QLYQS_2
；
S4, determining the angle and near-field distance of the guide vectorr s And far-field distance, according to the least square method, solving the gain coefficient of the path, specifically, according to the angle and distance between the scatterer and the reference point, and the selection vector of the space non-stationary characteristic, calculating the gain coefficient;
s5, simulating near-field and far-field paths by adopting a second-order approximate parabolic wave of the spherical wave, constructing a MIMO mixed field channel model, and estimating a mixed field channel from the constructed MIMO mixed field channel modelh hybrid-field：
Figure QLYQS_3
，
Wherein,,Sfor the number of visible scatterers,
Figure QLYQS_4
is the gain factor of the s-th path, +.>
Figure QLYQS_5
Represent the firstsAngle of individual scatterers to reference pointθ s And near field distancer s Related steering vectors, < >>
Figure QLYQS_6
Selection vector for describing spatially non-stationary characteristics of a channel, < >>
Figure QLYQS_7
Refers to the phase parameter of the s-th scatterer.
2. The method for de-pseudo-peak channel estimation in a super-MIMO mixed field channel of claim 1, wherein: in step S12, the number of pseudo peaks in the mixed field environment isf weifeng ：
Figure QLYQS_8
，
Wherein,,Sfor the number of visible scatterers,nis the number of mixed signals.
3. The method for de-pseudo-peak channel estimation in a super-MIMO mixed field channel of claim 1, wherein: in step S2, the angle of the far field and the angle of the near field are distinguished, specifically,
when the angle values of adjacent subarrays visible by the same scatterer are in ascending or descending order and the angle difference value of the adjacent subarrays is not larger than a set value, the scatterer is positioned in a near field, and the angle is judged to be the angle of the near field;
when the angle values of the scatterers visible in the subarray are identical, the scatterers are positioned in the far field, and the angle is determined as the angle of the far field.
4. A method for de-pseudo-peak channel estimation in a super-MIMO mixed field channel as claimed in any one of claims 1 to 3, wherein: in step S3, a selection vector describing the spatially non-stationary situation of the channel is determined
Figure QLYQS_9
Specifically, the method comprises the steps of,
Figure QLYQS_10
the m-th element of (2) is defined as +.>
Figure QLYQS_11
Wherein, the method comprises the steps of, wherein,Lfor the number of antennas to be used,Nis subarray (sub array)>
Figure QLYQS_12
Refers to the phase parameter of the s-th scatterer.
5. A method for de-pseudo-peak channel estimation in a super-MIMO mixed field channel as claimed in any one of claims 1 to 3, wherein: in step S4, the angle of the steering vector and the near field distance are determinedr s And far field distances, in particular,
s41, separating the angle and the distance of the guide vector, and respectively carrying out one-dimensional search by using a MUSIC algorithm to obtain an estimated value of the angle of the guide vector, wherein when the subarray at the center is visible, the angle of the guide vector is directly determined by the estimated angle of the subarray at the center; when the centrally located subarray is not visible, the angle of the steering vector is solved by the two visible subarray angles closest to the centrally located subarray;
s42, when the scatterer is positioned in the near field, estimating the distance from the subarray closest to the edge in the visible region to determine the near field distancer s ：
Figure QLYQS_13
Wherein, it is characterized byθ，r) Indicating the angle and distance of the scatterer to the reference point, < >>
Figure QLYQS_14
Representing the estimated angle +.>
Figure QLYQS_15
As a steering vector related to the angle and distance of the scatterer to the reference point,U n is noiseSubspace (sub-space)>
Figure QLYQS_16
Representing a transpose of the matrix;
s43, when the scatterer is located in the far field, the distance of the scatterer is infinity, and the distance of the far field is determined to be infinity.
6. A method for de-pseudo-peak channel estimation in a super-MIMO mixed field channel as claimed in any one of claims 1 to 3, wherein: in step S4, gain coefficients of S paths are obtained according to least square methodg 1 ...g s ]：
Figure QLYQS_17
，
Wherein the direction matrix
Figure QLYQS_20
Dimension is->
Figure QLYQS_22
，M=（L-1）/2，LFor the number of antennas to be used,Sdirection matrix of s paths for the number of visible scatterersAConsists of a steering vector and a selection vector:
Figure QLYQS_24
wherein->
Figure QLYQS_19
Respectively the angle of the 1 st scatterer to the reference pointθ 1 And near field distancer 1 Related steering vectors, < >>
Figure QLYQS_21
Respectively represent the firstsAngle of individual scatterers to reference pointθ s And near field distancer s Related steering vectors, < >>
Figure QLYQS_25
A selection vector for describing the spatially non-stationary properties of the channel, wherein +.>
Figure QLYQS_26
Phase parameter representing the 1 st scatterer, < ->
Figure QLYQS_18
Representing the phase parameter of the s-th scatterer,yfor receiving signals +.>
Figure QLYQS_23
Representing the transpose of the matrix.
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