CN116418634A - Cascade channel estimation method, equipment and storage medium - Google Patents

Cascade channel estimation method, equipment and storage medium Download PDF

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CN116418634A
CN116418634A CN202310355086.9A CN202310355086A CN116418634A CN 116418634 A CN116418634 A CN 116418634A CN 202310355086 A CN202310355086 A CN 202310355086A CN 116418634 A CN116418634 A CN 116418634A
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angle
matrix
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channel
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张川
尤优
薛宇飞
黄永明
尤肖虎
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Network Communication and Security Zijinshan Laboratory
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Abstract

The embodiment of the invention provides a cascade channel estimation method, equipment and a storage medium, wherein the method comprises the following steps: in the current iteration process, determining a first vector with the largest correlation with the residual vector in the perception matrix; determining a second vector based on the preset deviation grid value and the first vector; determining target elements of the optimized cascade channel model and the target channel matrix based on the constructed cascade channel model and the second vector; updating residual vectors, and determining a first vector in the next iteration process until the number of the first vectors in different columns determined in all the iteration processes is equal to a preset threshold; based on the target channel matrix, a result of the concatenated channel estimation is determined. Through the preset deviation grid value, parameters of the deviation grid are optimized through nonlinear constraint, and therefore deviation grid errors are reduced. The equalization achieves better in the aspects of pilot frequency overhead, channel estimation accuracy, calculation complexity and the like.

Description

Cascade channel estimation method, equipment and storage medium
Technical Field
The present invention relates to the field of communications networks, and in particular, to a method, an apparatus, and a storage medium for estimating a cascade channel.
Background
With research and exploration of sixth Generation mobile communication system (6G). The intelligent reflective surface (Reconfigurable intelligence surface, RIS) technology enables active control of the wireless propagation environment, with lower hardware complexity and energy overhead, and high research and application value.
The currently-used technique for realizing channel estimation (Channel Estimation, CE) by using compressed sensing RIS-assisted millimeter waves adopts a grid-based algorithm, wherein the technique regards sparse multipath channel estimation as a sparse recovery problem, firstly, a channel matrix to be estimated is converted into a sparse matrix of discrete angle space, and the path direction and the gain are described based On the angle grid. The offset grid error is ignored for correlation algorithms currently employed in the art, such as the orthogonal matching pursuit (Orthogonal Matching Pursuit, OMP) algorithm. Deviations from the grid error may result in reduced accuracy of the channel estimate.
Therefore, how to reduce Off-grid (OG) errors existing in the conventional orthogonal matching pursuit algorithm is a technical problem to be solved in the industry.
Disclosure of Invention
Aiming at the problems existing in the prior art, the embodiment of the invention provides a cascade channel estimation method, cascade channel estimation equipment and a storage medium.
In a first aspect, an embodiment of the present invention provides a cascade channel estimation method, including:
in the current iteration process, determining a column vector with the maximum correlation with the residual vector in the perception matrix as a first vector;
determining candidate angle combinations based on preset deviation grid values and angle combinations corresponding to the first vector;
determining a candidate column vector with the largest correlation with the residual vector from candidate column vectors corresponding to the candidate angle combination as a second vector;
determining an optimized cascade channel model and target elements of a target channel matrix based on the constructed cascade channel model and the second vector;
updating the residual vector based on the optimized cascade channel model and the target element, and determining the first vector in the next iteration process until the number of different columns of the first vector in the perception matrix, which are determined in all iteration processes, is equal to a preset threshold;
and determining a result of the cascade channel estimation based on the target channel matrix.
Optionally, the determining the candidate angle combination based on the preset deviation grid value and the angle combination corresponding to the first vector includes:
Determining an angle combination corresponding to the first vector as a first angle combination;
determining a candidate first angle with a difference value smaller than a first threshold value from the first angle combination, a candidate second angle with a difference value smaller than a second threshold value from the second angle in the first angle combination, and a candidate third angle with a difference value smaller than a third threshold value from the third angle in the first angle combination, and forming the candidate angle combination;
the preset deviation grid value is composed of the first threshold value, the second threshold value and the third threshold value;
the first angle is a horizontal angle of the transmitting antenna, the second angle is a pitching angle of the transmitting antenna, and the third angle is an angle of the receiving antenna.
Optionally, determining an optimized cascading channel model based on the constructed cascading channel model and the second vector includes:
determining an optimized sensing matrix based on the second vector determined in the preamble iteration process and the second vector determined in the current iteration process;
and determining the optimized cascade channel model based on the constructed cascade channel model and the optimized perception matrix.
Optionally, determining the target element of the target channel matrix based on the constructed cascading channel model and the second vector includes:
determining a target value when a model value corresponding to a noise matrix in the cascade channel model is minimum based on a least square method and the optimized cascade channel model;
determining a target element in the target channel matrix based on the target value and a first index value; the first index value is an index value of the first vector in the sense matrix.
Optionally, the constructed cascade channel model or the optimized cascade channel model is used for representing the relation among the received signal, the perception matrix, the channel estimation matrix and the noise matrix.
Optionally, the formula corresponding to the constructed cascade channel model is: y=ax+n;
wherein,,
Figure BDA0004163073580000031
representing received signals in T time slots A E C T×N Representing a perception matrix of T rows and N columns,
Figure BDA0004163073580000032
any column vector representing said target channel matrix,/->
Figure BDA0004163073580000033
Representing the column vector corresponding to x in the channel noise matrix.
Optionally, the updating the residual vector based on the optimized cascade channel model and the target element includes:
Updating the residual vector based on the optimized cascade channel model, the optimized perception matrix, the received signal and the target element;
the optimized cascade channel model corresponds to the formula: y=a s x+N；
Wherein,,
Figure BDA0004163073580000034
representing the received signal in T time slots, A s ∈C T×S Representing T rows and S columnsPerception matrix->
Figure BDA0004163073580000041
All non-zero elements representing the target channel matrix, < > are>
Figure BDA0004163073580000042
Representing channel noise.
In a second aspect, an embodiment of the present invention further provides a cascaded channel estimation apparatus, including:
the first determining module is used for determining a column vector with the largest correlation with the residual vector in the perception matrix as a first vector in the current iteration process;
the second determining module is used for determining candidate angle combinations based on preset deviation grid values and angle combinations corresponding to the first vector;
a third determining module, configured to determine, as a second vector, a candidate column vector having the greatest correlation with the residual vector among candidate column vectors corresponding to the candidate angle combinations;
a fourth determining module, configured to determine, based on the constructed cascade channel model and the second vector, an optimized cascade channel model and a target element of a target channel matrix;
The iteration module is used for updating the residual vector based on the optimized cascade channel model and the target element, and determining the first vector in the next iteration process until the number of different columns of the first vector in the perception matrix, which are determined in all iteration processes, is equal to a preset threshold;
and a fifth determining module, configured to determine a result of the concatenated channel estimation based on the target channel matrix.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a transceiver, and a processor;
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and implementing the cascade channel estimation method as described in the first aspect.
In a fourth aspect, embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the cascade channel estimation method according to the first aspect described above.
In a fifth aspect, embodiments of the present invention further provide a processor-readable storage medium storing a computer program for causing a processor to perform the cascade channel estimation method according to the first aspect as described above.
In a sixth aspect, an embodiment of the present invention further provides a communication device readable storage medium, where a computer program is stored, where the computer program is configured to cause a communication device to perform the cascade channel estimation method according to the first aspect described above.
In a seventh aspect, an embodiment of the present invention further provides a chip product readable storage medium, where a computer program is stored, where the computer program is configured to cause a chip product to perform the cascade channel estimation method according to the first aspect.
In an eighth aspect, embodiments of the present invention further provide a computer program product comprising a computer program which, when executed by a processor, implements the cascade channel estimation method according to the first aspect described above.
According to the cascade channel estimation method, the cascade channel estimation equipment and the storage medium, a first vector with the maximum correlation with a residual vector in a perception matrix is determined, a second vector with the maximum correlation with the residual vector is determined by combining a preset deviation grid value, a constructed cascade channel model is optimized, and target elements of a target channel matrix are determined; and iteratively updating the residual vector, and repeatedly outputting target elements of the target channel matrix until a preset threshold is met, thereby completing the estimation of the cascade channel. Through the preset deviation grid value, parameters of the deviation grid are optimized through nonlinear constraint, and therefore deviation grid errors are reduced. The equalization achieves better in the aspects of accuracy, calculation complexity and the like of channel estimation.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a cascade channel estimation method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a cascaded channel estimation device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to facilitate a clearer understanding of various embodiments of the present invention, some relevant background knowledge is first presented as follows.
At present, the channel estimation is realized by RIS auxiliary millimeter waves, and the adopted orthogonal matching pursuit (Orthogonal Matching Pursuit, OMP) algorithm is an algorithm based on a compressed sensing principle. The method comprises the steps of firstly establishing a corresponding cascade channel model, wherein the cascade channel model is mainly used for representing a channel for transmitting signals to an intelligent reflecting surface and transmitting signals between the intelligent reflecting surface and a target receiver. Different channels are usually represented in a grid form, and the higher the division density of the grid is, the more the calculation complexity is increased, and the higher the accuracy of the corresponding channel estimation result is; the smaller the dividing density of the grid is, the more the calculation complexity is reduced, and the lower the accuracy of the corresponding channel estimation result is. However, no matter whether the grid is divided into larger or smaller, the situation of deviating from the grid exists, and further processing or correction is needed for the situation of deviating from the grid, so that the method for estimating the cascade channel is provided, and the parameter deviating from the grid is optimized through nonlinear constraint, so that the deviation grid error is reduced. The equalization achieves better in the aspects of pilot frequency overhead, channel estimation accuracy, calculation complexity and the like.
Fig. 1 is a schematic flow chart of a cascade channel estimation method according to an embodiment of the present invention; as shown in fig. 1, the method includes:
step 101, in the current iteration process, determining a column vector with the largest correlation with the residual vector in the perception matrix as a first vector;
specifically, the pilot signal sent by the base station is reflected by the RIS through a first channel between the base station and the RIS, and then is transmitted to the terminal through a second channel between the RIS and the terminal. The first channel represents any channel between the base station and the RIS, the second channel represents any channel between the RIS and the terminal, the first channel can be represented by a first channel matrix, and the second channel can be represented by a second channel matrix, so that a cascade channel matrix corresponding to a cascade channel between the base station and the terminal, which corresponds to a pilot signal sent by the base station, is determined by the first channel matrix and the second channel matrix, and a cascade channel model is further constructed, and the cascade channel model is used for representing the relation among a received signal, a perception matrix, a channel matrix and a noise matrix. Each column in the sensing matrix is mapped to a group of angle combinations through a corresponding function, and each angle combination represents angle information corresponding to the receiving antenna, such as azimuth angle, pitch angle, roll angle, angle of the receiving antenna and the like.
And determining a column vector with the maximum correlation with the residual vector in the perception matrix by using the perception matrix and the residual vector in the constructed cascade channel model as a first vector, wherein the first vector can be used for indicating the position of the first vector in the perception matrix through an index value. The first vector is used for representing phase information of a received signal, and an angle combination used for representing angles of a receiving and transmitting antenna has a one-to-one correspondence.
102, determining candidate angle combinations based on preset deviation grid values and angle combinations corresponding to the first vector;
step 103, determining a candidate column vector with the largest correlation with the residual vector from candidate column vectors corresponding to the candidate angle combination as a second vector;
and determining the candidate angle combination which meets the preset deviation grid value according to the preset deviation grid value.
Also, based on a correspondence between a column vector representing phase information of a received signal and an angle combination representing an angle of a transmitting/receiving antenna, a candidate column vector corresponding to the candidate angle combination is determined, and one vector having the greatest correlation with the residual matrix is determined among the candidate column vectors as a second vector, wherein the preset deviation grid value may be a nonlinear constraint condition determined based on a convex optimization algorithm, such as defining a correlation between the two angle combinations to be within a certain range, defining an angle difference corresponding to the two vectors to be within a certain range, or a combination of the two.
Step 104, determining an optimized cascade channel model and target elements of a target channel matrix based on the constructed cascade channel model and the second vector;
and optimizing the cascade channel model according to the second vector determined in each iteration process and the constructed cascade channel model, determining the optimized cascade channel model, and further determining the target element of the target channel matrix, wherein the target element is a non-zero element.
Step 105, updating the residual vector based on the optimized cascade channel model and the target element, wherein the residual vector is used for determining the first vector in the next iteration process until the number of different columns of the first vector in the perception matrix, which are determined in all the iteration processes, is equal to a preset threshold;
step 106, determining the result of the cascade channel estimation based on the target channel matrix.
And updating the residual vector based on the optimized cascade channel model and the target element. Then, determining a column vector with the maximum correlation with the updated residual vector in the perception matrix again as a first vector; and repeating the process of optimizing the cascade channel model and determining target elements of the target channel matrix, and judging whether the number of different columns of the first vector in the perception matrix determined in all the iterative processes reaches a preset threshold; the preset threshold is used for representing the number of non-zero elements in the target channel matrix. In each iteration process, one first vector corresponds to one target element, and it can be understood that the determined first vector is located in the number of different columns in the sensing matrix, that is, the number of target elements in the target channel matrix is determined, and when it has been determined that the preset threshold number of target elements is met, the process of determining the target channel matrix is completed.
And determining the number of different columns of the first vector in the sensing matrix, namely determining the number of the first vector with the greatest correlation in the different columns, namely, if the first vector in the sensing matrix extracted at the current time is determined to be the same as the last time in the cyclic process, not counting, if the first vector in the sensing matrix extracted at the current time is determined to be different from the last time, accumulating and counting, updating the residual vector based on the optimized cascade channel model and the target value, and obtaining all the non-zero elements in the target channel matrix until the accumulated value reaches the total number of the non-zero elements in the target channel matrix, namely, the target value determined by each iteration of the method, namely, finishing the estimation process of the cascade channel.
According to the cascade channel estimation method provided by the embodiment of the invention, a first vector with the maximum correlation with the residual vector in the sensing matrix is determined, a second vector with the maximum correlation with the residual vector is determined by combining a preset deviation grid value, and a constructed cascade channel model is optimized to determine target elements of a target channel matrix; and iteratively updating the residual vector, and repeatedly outputting target elements of the target channel matrix until a preset threshold is met, thereby completing the estimation of the cascade channel. Through the preset deviation grid value, parameters of the deviation grid are optimized through nonlinear constraint, and therefore deviation grid errors are reduced. The equalization achieves better in the aspects of accuracy, calculation complexity and the like of channel estimation.
Optionally, the preset deviation grid value includes:
any two column vectors used for representing phase information of the received signals respectively correspond to angle combinations, wherein the difference value of the first angle is smaller than a first threshold value, the difference value of the second angle is smaller than a second threshold value, and the difference value of the third angle is smaller than a third threshold value;
the preset deviation grid value is composed of the first threshold value, the second threshold value and the third threshold value;
the first angle is a horizontal angle of the transmitting antenna, the second angle is a pitching angle of the transmitting antenna, and the third angle is an angle of the receiving antenna.
Specifically, there is a one-to-one correspondence between a column vector representing phase information of a received signal and an angle combination representing angles of a transmitting and receiving antenna, which specifically includes a horizontal angle (i.e., a first angle) of the transmitting antenna, a pitch angle (i.e., a second angle) of the transmitting antenna, and an angle (i.e., a third angle) of the receiving antenna. The column vector used for representing the phase information of the received signal can establish a corresponding relation with the angle combination used for representing the angle of the receiving antenna through a corresponding mapping function.
Determining angle combinations corresponding to any two column vectors respectively, wherein the angle combination A and the angle combination B comprise a horizontal angle A of a transmitting antenna, a pitching angle A of the transmitting antenna and an angle A of a receiving antenna; the angle combination B comprises a horizontal angle B of the transmitting antenna, a pitching angle B of the transmitting antenna and an angle B of the receiving antenna; comparing whether the sizes of the angle combination A and the angle combination B meet a preset deviation grid value or not, wherein the sizes of the angle combination A and the angle combination B meet the preset deviation grid value, namely respectively determining that the absolute value of the difference value between the horizontal angle A of the transmitting antenna and the horizontal angle B of the transmitting antenna is smaller than a first threshold value, the absolute value of the difference value between the pitching angle A of the transmitting antenna and the pitching angle B of the transmitting antenna is smaller than a second threshold value, and the absolute value of the difference value between the angle A of the receiving antenna and the angle B of the receiving antenna is smaller than a third threshold value; the first threshold, the second threshold and the third threshold may be the same or different, and are specifically determined according to the performance requirement of the concatenated channel estimation.
Optionally, the determining the candidate angle combination based on the preset deviation grid value and the angle combination corresponding to the first vector includes:
determining an angle combination corresponding to the first vector as a first angle combination;
determining a candidate first angle with a difference value smaller than a first threshold value from the first angle combination, a candidate second angle with a difference value smaller than a second threshold value from the second angle in the first angle combination, and a candidate third angle with a difference value smaller than a third threshold value from the third angle in the first angle combination, and forming the candidate angle combination;
the preset deviation grid value is composed of the first threshold value, the second threshold value and the third threshold value;
the first angle is a horizontal angle of the transmitting antenna, the second angle is a pitching angle of the transmitting antenna, and the third angle is an angle of the receiving antenna.
Specifically, the perceptual matrix a= { F is determined i The first vector of i=1, …, N } having the greatest correlation with residual vector u, denoted F j J∈ { i }; the angle combination corresponding to the first vector is denoted as G j ＝{φ j ,θ j ,γ j And }, wherein phi j ，θ j And gamma j The horizontal angle of the transmitting antenna, the elevation angle of the transmitting antenna, and the receiving antenna angle are respectively represented.
Determining a candidate angle combination having a difference from a first angle of the first vector less than the first threshold, a difference from a second angle of the first vector less than the second threshold, and a difference from a third angle of the first vector less than the third threshold; that is, determine the sum G j Candidate angle combinations meeting preset deviation grid values
Figure BDA0004163073580000111
I.e.
Figure BDA0004163073580000112
Wherein delta 1 Represents a first threshold, delta 2 Represents a second threshold, delta 3 Representing a third threshold. Delta 1 ，δ 2 And delta 3 Is phi in the angle combination j ，θ j And gamma j I.e. a preset offset grid value, the three thresholds comprised in the preset offset grid value being determined according to the performance requirements of the concatenated channel estimation. />
Figure BDA0004163073580000113
The horizontal angle of the transmitting antenna, the elevation angle of the transmitting antenna, and the receiving antenna angle are respectively represented.
According to the candidate angle combination and the one-to-one correspondence relationship between the phase information of the received signal and the angle combination, the phase information of the target received signal corresponding to the candidate angle combination is determined, that is, a candidate column vector for representing the phase information of the target received signal is determined. There may be multiple candidate column vectors obtained here, so that further processing of the candidate column vectors is required. And further determining a second vector, namely, determining a candidate column vector with the largest correlation with the residual vector in the candidate column vectors corresponding to the candidate angle combination.
Determining the correlation between the candidate column vector and the residual vector u, and screening the one with the largest correlation asSecond vector
Figure BDA0004163073580000114
The formula can be expressed as: />
Figure BDA0004163073580000115
Optionally, determining an optimized cascading channel model based on the constructed cascading channel model and the second vector includes:
determining an optimized sensing matrix based on the second vector determined in the preamble iteration process and the second vector determined in the current iteration process;
and determining the optimized cascade channel model based on the constructed cascade channel model and the optimized perception matrix.
Specifically, after determining the second vector, an optimized perceptual matrix is determined based on the second vector, which may be denoted as a s The initial value of the optimized sensing matrix is null, the second vector determined by each iteration is put into the optimized sensing matrix,
Figure BDA0004163073580000121
a constructed concatenated channel model is used to represent the relationship between the received signal, the perceptual matrix, the channel estimation matrix, and the noise matrix, and can be formulated as: y=ax+n;
wherein,,
Figure BDA0004163073580000122
representing the received signal in T time slots, A s ∈C T×s A sense matrix representing T rows and S columns, < > >
Figure BDA0004163073580000123
All non-zero elements representing the target channel matrix, < > are>
Figure BDA0004163073580000124
Representing channel noise.
Based on constructed cascade channel model and optimized perception matrix A s The determined optimized concatenated channel model may be expressed as y=a s x+N; wherein y represents the received signal, A represents the sensing matrix, A s Representing the optimized perceptual matrix, N representing the channel noise vector.
Figure BDA0004163073580000125
Representing received signals in T time slots A E C T×N Representing a perception matrix of T rows and N columns,
Figure BDA0004163073580000126
any column vector representing said target channel matrix,/->
Figure BDA0004163073580000127
Representing column vectors corresponding to x in the channel noise matrix, typically obeying a mean of 0, and a variance of σ 2 Is a gaussian distribution of (c). The optimized cascade channel model is used for representing the relation among the received signals, the perception matrix, the channel estimation matrix and the noise matrix.
Optionally, determining the target element of the target channel matrix based on the constructed cascading channel model and the second vector includes:
determining a target value when a model value corresponding to a noise matrix in the cascade channel model is minimum based on a least square method and the optimized cascade channel model;
determining a target element in the target channel matrix based on the target value and a first index value; the first index value is an index value of the first vector in the sense matrix.
Specifically, the target value when the mode value corresponding to the channel noise is minimized is determined based on the least square method and the optimized cascade channel model, and may be expressed as:
Figure BDA0004163073580000128
the target value x is set s And serving as a non-zero element corresponding to the first index value in the target channel matrix. Target value x s I.e. the S-loose approximation of the original signal x. The number of non-zero elements in the original signal x is S. S target values x are determined s All non-zero elements in the original signal x are determined, and the cascade channel estimation is completed.
According to the cascade channel estimation method provided by the embodiment of the invention, a first vector with the maximum correlation with the residual vector in the sensing matrix is determined, a second vector with the maximum correlation with the residual vector is determined by combining a preset deviation grid value, and a constructed cascade channel model is optimized to determine target elements of a target channel matrix; and iteratively updating the residual vector, and repeatedly outputting target elements of the target channel matrix until a preset threshold is met, thereby completing the estimation of the cascade channel. Through the preset deviation grid value, parameters of the deviation grid are optimized through nonlinear constraint, and therefore deviation grid errors are reduced. The equalization achieves better in the aspects of accuracy, calculation complexity and the like of channel estimation.
In order to more clearly illustrate the cascade channel estimation method provided by the present invention, a specific example will be described below.
In the following, a multi-input single-output (Multiple Input Single Output, MISO) cascade channel model will be mainly described as an example, and each output may be performed according to the method corresponding to the multi-input single-output (MISO) cascade channel model for a multi-input multi-output (Multiple Input Multiple Output, MIMO) cascade channel.
The intelligent reflective surface RIS-aided millimeter wave cascade channel model can be expressed as:
y＝Ax+N
wherein,,
Figure BDA0004163073580000131
is a received signal in T time slots A epsilon C T×N Is a sense matrix +.>
Figure BDA0004163073580000132
For the open channel vector, S non-zero elements (S is called the rarefaction) are included +.>
Figure BDA0004163073580000133
Is channel noise.
The perceptual matrix a can be expressed as: a= [ F ] 1 ,F 2 ,…,F N ]Wherein each column vector F i Representing phase information of the received signal, the column vector
Figure BDA0004163073580000134
An atom called A, and column vector F i Mapping to an angle combination G by a function i ＝{φ i ,θ i ,γ i And }, wherein phi j ，θ j And gamma j The horizontal angle of the transmitting antenna, the pitching angle of the transmitting antenna and the receiving antenna angle are respectively represented, namely, each column vector and an angle combination have a one-to-one correspondence. The specific mapping form of mapping the column vectors to the angle combinations is determined according to whether the wireless communication system is a multiple-input single-output MISO system or a multiple-input multiple-output MIMO system and the number of antennas respectively corresponding to a transmitting end and a receiving end.
Assuming that the received signal y, the perceptual matrix a and the degree of openness S are known, the sparse channel vector x needs to be approximately solved. The improved orthogonal matching pursuit algorithm provided by the invention, namely a discrete-continuous optimized orthogonal matching pursuit (OMP with Discrete-Continuous Optimization, DC-OMP) algorithm, is used for determining a sparse channel vector x, and specifically comprises the following steps:
input: receiving a signal y, a perception matrix A and a degree of openness S (the number of non-zero elements in the perception matrix);
and (3) outputting: s-ary approximation x of original signal x s ；
Initializing: residual error u 0 =y, index set
Figure BDA0004163073580000141
Reconstruction of the atom set->
Figure BDA0004163073580000142
Step 1: find the residual u and the column vector F of the perceptual matrix j Subscript v corresponding to the maximum value of the inner product s V, i.e s ＝argmax j＝1,…,N |<u s-1 ,F j >V of I, A s Is listed as
Figure BDA00041630735800001410
Step 2: updating index set T s ＝T s-1 ∪v s ；
Step 3: acquiring columns constituting a perceptual matrix
Figure BDA0004163073580000143
Corresponding angle combination->
Figure BDA0004163073580000144
Figure BDA0004163073580000145
Step 4: the nonlinear constraint optimization is carried out by taking the inner product as an objective function, and the nonlinear constraint optimization specifically comprises the following steps:
Figure BDA0004163073580000146
Figure BDA0004163073580000147
and column vector
Figure BDA0004163073580000148
The corresponding angle combination is->
Figure BDA0004163073580000149
Wherein delta 1 ，δ 2 And delta 3 To pair(s)
Figure BDA0004163073580000151
And->
Figure BDA0004163073580000152
Is usually set to an appropriate value according to actual needs.
Namely solving through a common nonlinear constraint optimization solving method (such as an interior point method) and obtaining an optimized angle combination
Figure BDA0004163073580000153
Step 5: recording the optimized reconstructed atom set
Figure BDA0004163073580000154
Step 6: obtaining x by least square method s ＝arg min x ||y-A s x|| 2 ；
Step 7: determining column vectors of selected perceptual matrices
Figure BDA0004163073580000155
The number of the different rows is denoted as s; judging whether s is satisfied<S, if yes, updating residual error u s ＝y-A s x s Returning to the step 1; if not, the cycle is stopped.
The algorithm thus far calculates an S-ary approximation x of the original signal x s The solution objective is achieved.
Fig. 2 is a schematic structural diagram of a cascaded channel estimation apparatus according to an embodiment of the present invention, as shown in fig. 2, where the apparatus includes a first determining module 201, a second determining module 202, a third determining module 203, a fourth determining module 204, an iteration module 205, and a fifth determining module 206, where:
a first determining module 201, configured to determine, as a first vector, a column vector having a maximum correlation with a residual vector in a perceptual matrix in a current iterative process;
a second determining module 202, configured to determine a candidate angle combination based on a preset deviation grid value and an angle combination corresponding to the first vector;
a third determining module 203, configured to determine, as a second vector, a candidate column vector having the greatest correlation with the residual vector, among candidate column vectors corresponding to the candidate angle combinations;
A fourth determining module 204, configured to determine, based on the constructed cascade channel model and the second vector, an optimized cascade channel model and a target element of a target channel matrix;
the iteration module 205 is configured to update the residual vector based on the optimized cascade channel model and the target element, and determine the first vector in a next iteration process until the number of different columns of the first vector determined in all iteration processes in the sensing matrix is equal to a preset threshold;
a fifth determining module 206, configured to determine a result of the concatenated channel estimation based on the target channel matrix.
Optionally, the second determining module 202 is specifically configured to, in determining the candidate angle combination based on the preset deviation grid value and the angle combination corresponding to the first vector:
determining an angle combination corresponding to the first vector as a first angle combination;
determining a candidate first angle with a difference value smaller than a first threshold value from the first angle combination, a candidate second angle with a difference value smaller than a second threshold value from the second angle in the first angle combination, and a candidate third angle with a difference value smaller than a third threshold value from the third angle in the first angle combination, and forming the candidate angle combination;
The preset deviation grid value is composed of the first threshold value, the second threshold value and the third threshold value;
the first angle is a horizontal angle of the transmitting antenna, the second angle is a pitching angle of the transmitting antenna, and the third angle is an angle of the receiving antenna.
Optionally, the fourth determining module 204 is specifically configured to, in determining the optimized cascade channel model based on the constructed cascade channel model and the second vector:
determining an optimized sensing matrix based on the second vector determined in the preamble iteration process and the second vector determined in the current iteration process;
and determining the optimized cascade channel model based on the constructed cascade channel model and the optimized perception matrix.
Optionally, the fourth determining module 204 is specifically configured to, in determining the target element of the target channel matrix based on the constructed concatenated channel model and the second vector:
determining a target value when a model value corresponding to a noise matrix in the cascade channel model is minimum based on a least square method and the optimized cascade channel model;
determining a target element in the target channel matrix based on the target value and a first index value; the first index value is an index value of the first vector in the sense matrix.
Specifically, the apparatus for cascade channel estimation provided by the embodiment of the present invention can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the embodiment are not described in detail herein.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention; as shown in fig. 3, the electronic device includes a memory 320, a transceiver 310, and a processor 300; wherein the processor 300 and the memory 320 may also be physically separate.
A memory 320 for storing a computer program; a transceiver 310 for transceiving data under the control of the processor 300.
In particular, the transceiver 310 is used to receive and transmit data under the control of the processor 300.
Wherein in fig. 3, a bus architecture may comprise any number of interconnected buses and bridges, and in particular, one or more processors represented by processor 300 and various circuits of memory represented by memory 320, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., all as are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. Transceiver 310 may be a number of elements, including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc.
The processor 300 is responsible for managing the bus architecture and general processing, and the memory 320 may store data used by the processor 300 in performing operations.
The processor 300 may be a central processing unit (Central Processing Unit, CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA), or a complex programmable logic device (Complex Programmable Logic Device, CPLD), or may employ a multi-core architecture.
Processor 300 is operable to perform any of the methods provided by embodiments of the present invention in accordance with the obtained executable instructions by invoking a computer program stored in memory 320, for example:
in the current iteration process, determining a column vector with the maximum correlation with the residual vector in the perception matrix as a first vector;
determining candidate angle combinations based on preset deviation grid values and angle combinations corresponding to the first vector;
determining a candidate column vector with the largest correlation with the residual vector from candidate column vectors corresponding to the candidate angle combination as a second vector;
determining an optimized cascade channel model and target elements of a target channel matrix based on the constructed cascade channel model and the second vector;
Updating the residual vector based on the optimized cascade channel model and the target element, and determining the first vector in the next iteration process until the number of different columns of the first vector in the perception matrix, which are determined in all iteration processes, is equal to a preset threshold; the preset threshold value is used for representing the number of non-zero elements in the target channel matrix;
and determining a result of the cascade channel estimation based on the target channel matrix.
Optionally, the preset deviation grid value includes:
any two column vectors used for representing phase information of the received signals respectively correspond to angle combinations, wherein the difference value of the first angle is smaller than a first threshold value, the difference value of the second angle is smaller than a second threshold value, and the difference value of the third angle is smaller than a third threshold value;
the preset deviation grid value is composed of the first threshold value, the second threshold value and the third threshold value;
the first angle is a horizontal angle of the transmitting antenna, the second angle is a pitching angle of the transmitting antenna, and the third angle is an angle of the receiving antenna.
Optionally, the determining the candidate angle combination based on the preset deviation grid value and the angle combination corresponding to the first vector includes:
Determining an angle combination corresponding to the first vector as a first angle combination;
determining a candidate first angle with a difference value smaller than a first threshold value from the first angle combination, a candidate second angle with a difference value smaller than a second threshold value from the second angle in the first angle combination, and a candidate third angle with a difference value smaller than a third threshold value from the third angle in the first angle combination, and forming the candidate angle combination;
the preset deviation grid value is composed of the first threshold value, the second threshold value and the third threshold value;
the first angle is a horizontal angle of the transmitting antenna, the second angle is a pitching angle of the transmitting antenna, and the third angle is an angle of the receiving antenna.
Optionally, determining an optimized cascading channel model based on the constructed cascading channel model and the second vector includes:
determining an optimized sensing matrix based on the second vector determined in the preamble iteration process and the second vector determined in the current iteration process;
and determining the optimized cascade channel model based on the constructed cascade channel model and the optimized perception matrix.
Optionally, determining the target element of the target channel matrix based on the constructed cascading channel model and the second vector includes:
determining a target value when a model value corresponding to a noise matrix in the cascade channel model is minimum based on a least square method and the optimized cascade channel model;
determining a target element in the target channel matrix based on the target value and a first index value; the first index value is an index value of the first vector in the sense matrix.
Optionally, the constructed cascade channel model or the optimized cascade channel model is used for representing the relation among the received signal, the perception matrix, the channel estimation matrix and the noise matrix.
Optionally, the formula corresponding to the constructed cascade channel model is: y=ax+n;
wherein,,
Figure BDA0004163073580000201
representing received signals in T time slots A E C T×N Representing a perception matrix of T rows and N columns,
Figure BDA0004163073580000202
any column vector representing said target channel matrix,/->
Figure BDA0004163073580000203
Representing the column vector corresponding to x in the channel noise matrix. />
Optionally, the updating the residual vector based on the optimized cascade channel model and the target element includes:
Updating the residual vector based on the optimized cascade channel model, the optimized perception matrix, the received signal and the target element;
the optimized cascade channel model corresponds to the formula: y=a s x+N；
Wherein,,
Figure BDA0004163073580000204
representing the received signal in T time slots, A s ∈C T×S A sense matrix representing T rows and S columns, < >>
Figure BDA0004163073580000205
All non-zero elements representing the target channel matrix, < > are>
Figure BDA0004163073580000206
Representing channel noise.
It should be noted that, the electronic device provided in the embodiment of the present invention can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and the parts and beneficial effects that are the same as those of the method embodiment in the embodiment are not described in detail herein.
In another aspect, embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the cascade channel estimation method provided by the above embodiments.
In another aspect, an embodiment of the present invention further provides a processor readable storage medium, where a computer program is stored, where the computer program is configured to cause the processor to execute the cascade channel estimation method provided in the foregoing embodiments.
The processor-readable storage medium may be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic storage (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), semiconductor storage (e.g., ROM, EPROM, EEPROM, nonvolatile storage (NAND FLASH), solid State Disk (SSD)), and the like.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for concatenated channel estimation, comprising:
in the current iteration process, determining a column vector with the maximum correlation with the residual vector in the perception matrix as a first vector;
determining candidate angle combinations based on preset deviation grid values and angle combinations corresponding to the first vector;
determining a candidate column vector with the largest correlation with the residual vector from candidate column vectors corresponding to the candidate angle combination as a second vector;
determining an optimized cascade channel model and target elements of a target channel matrix based on the constructed cascade channel model and the second vector;
Updating the residual vector based on the optimized cascade channel model and the target element, and determining the first vector in the next iteration process until the number of different columns of the first vector in the perception matrix, which are determined in all iteration processes, is equal to a preset threshold; the preset threshold value is used for representing the number of non-zero elements in the target channel matrix;
and determining a result of the cascade channel estimation based on the target channel matrix.
2. The method of cascaded channel estimation according to claim 1, wherein said determining candidate angle combinations based on the preset offset grid value and the angle combination corresponding to the first vector comprises:
determining an angle combination corresponding to the first vector as a first angle combination;
determining a candidate first angle with a difference value smaller than a first threshold value from the first angle combination, a candidate second angle with a difference value smaller than a second threshold value from the second angle in the first angle combination, and a candidate third angle with a difference value smaller than a third threshold value from the third angle in the first angle combination, and forming the candidate angle combination;
the preset deviation grid value is composed of the first threshold value, the second threshold value and the third threshold value;
The first angle is a horizontal angle of the transmitting antenna, the second angle is a pitching angle of the transmitting antenna, and the third angle is an angle of the receiving antenna.
3. The method of cascading channel estimation according to claim 2, wherein determining an optimized cascading channel model based on the constructed cascading channel model and the second vector comprises:
determining an optimized sensing matrix based on the second vector determined in the preamble iteration process and the second vector determined in the current iteration process;
and determining the optimized cascade channel model based on the constructed cascade channel model and the optimized perception matrix.
4. The method of concatenated channel estimation of claim 3, wherein determining the target element of the target channel matrix based on the constructed concatenated channel model and the second vector comprises:
determining a target value when a model value corresponding to a noise matrix in the cascade channel model is minimum based on a least square method and the optimized cascade channel model;
determining a target element in the target channel matrix based on the target value and a first index value; the first index value is an index value of the first vector in the sense matrix.
5. The method according to any one of claims 1 to 4, wherein the constructed concatenated channel model or the optimized concatenated channel model is used to represent a relationship among a received signal, a perceptual matrix, a channel estimation matrix and a noise matrix.
6. The method for estimating a concatenated channel of claim 5, wherein the constructed concatenated channel model corresponds to the formula: y=ax+n;
wherein,,
Figure FDA0004163073570000021
representing received signals in T time slots A E C T×N A sense matrix representing T rows and N columns, < >>
Figure FDA0004163073570000022
Any column vector representing said target channel matrix,/->
Figure FDA0004163073570000023
Representing the column vector corresponding to x in the channel noise matrix.
7. The method of cascade channel estimation according to claim 5, wherein said updating the residual vector based on the optimized cascade channel model and the target element comprises:
updating the residual vector based on the optimized cascade channel model, the optimized perception matrix, the received signal and the target element;
the optimized cascade channel model corresponds to the formula: y=a s x+N；
Wherein,,
Figure FDA0004163073570000031
representing the received signal in T time slots, A s ∈C T×S Representing a perception matrix of T rows and S columns,
Figure FDA0004163073570000032
all non-zero elements representing the target channel matrix, < > are>
Figure FDA0004163073570000033
Representing channel noise.
8. A concatenated channel estimation device comprising:
the first determining module is used for determining a column vector with the largest correlation with the residual vector in the perception matrix as a first vector in the current iteration process;
the second determining module is used for determining candidate angle combinations based on preset deviation grid values and angle combinations corresponding to the first vector;
a third determining module, configured to determine, as a second vector, a candidate column vector having the greatest correlation with the residual vector among candidate column vectors corresponding to the candidate angle combinations;
a fourth determining module, configured to determine, based on the constructed cascade channel model and the second vector, an optimized cascade channel model and a target element of a target channel matrix;
the iteration module is used for updating the residual vector based on the optimized cascade channel model and the target element, and determining the first vector in the next iteration process until the number of different columns of the first vector in the perception matrix, which are determined in all iteration processes, is equal to a preset threshold;
And a fifth determining module, configured to determine a result of the concatenated channel estimation based on the target channel matrix.
9. An electronic device comprising a memory, a transceiver, and a processor;
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the cascade channel estimation method of any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for causing a computer to execute the cascade channel estimation method according to any one of claims 1 to 7.
CN202310355086.9A 2023-04-04 2023-04-04 Cascade channel estimation method, equipment and storage medium Pending CN116418634A (en)

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