Channel estimation method and device, communication equipment, chip and chip module

Abstract

The invention relates to a channel estimation method and device, communication equipment, a chip and a chip module. The method comprises the following steps: performing singular value decomposition on a time domain autocorrelation matrix and a frequency domain autocorrelation matrix of a target channel to obtain a time domain unitary matrix and a time domain eigenvalue corresponding to the time domain autocorrelation matrix and a frequency domain unitary matrix and a frequency domain eigenvalue corresponding to the frequency domain autocorrelation matrix; updating the time domain feature value based on the frequency domain feature value to obtain an updated time domain feature value; updating the frequency domain feature value based on the updated time domain feature value to obtain an updated frequency domain feature value; obtaining a time domain filtering coefficient based on the updated time domain characteristic value, and obtaining a frequency domain filtering coefficient based on the updated frequency domain characteristic value; and obtaining a filter coefficient based on the time domain filter coefficient and the frequency domain filter coefficient, and performing channel estimation based on the filter coefficient to obtain a channel estimation result of the target channel. By adopting the method, the channel estimation performance loss can be reduced.

Description

Channel estimation methods, devices, communication equipment, chips and chip modules Technical Field

[0001] This application relates to the field of communication technology, and in particular to a channel estimation method, apparatus, communication equipment, chip, and chip module. Background Technology

[0002] While the traditional 2x1D-CE (2x1D-Channel Estimation, a channel estimation method that uses two-dimensional step-by-step filtering) method can reduce computational overhead compared to the 2D-CE (2D-Channel Estimation, a channel estimation method that uses two-dimensional joint filtering), it suffers from a significant performance loss.

[0003] Therefore, how to achieve a channel estimation method that reduces both computational overhead and performance loss has become a pressing technical problem that needs to be solved. Summary of the Invention

[0004] Therefore, it is necessary to provide a channel estimation method, apparatus, communication equipment, chip, and chip module that reduces both computational overhead and performance loss in channel estimation, in order to address the aforementioned technical problems.

[0005] Firstly, this application provides a channel estimation method, including:

[0006] Singular value decomposition is performed on the time-domain autocorrelation matrix of the target channel to obtain the time-domain unitary matrix and time-domain eigenvalues ​​corresponding to the time-domain autocorrelation matrix. Singular value decomposition is also performed on the frequency-domain autocorrelation matrix of the target channel to obtain the frequency-domain unitary matrix and frequency-domain eigenvalues ​​corresponding to the frequency-domain autocorrelation matrix.

[0007] The time-domain feature values ​​are updated based on the frequency-domain feature values ​​to obtain the updated time-domain feature values; the frequency-domain feature values ​​are then updated based on the updated time-domain feature values ​​to obtain the updated frequency-domain feature values.

[0008] The time-domain filtering coefficients are obtained based on the time-domain unitary matrix, time-domain eigenvalues, and updated time-domain eigenvalues; the frequency-domain filtering coefficients are obtained based on the frequency-domain unitary matrix, frequency-domain eigenvalues, and updated frequency-domain eigenvalues.

[0009] Based on the time-domain and frequency-domain filtering coefficients, the filtering coefficients are obtained, and channel estimation is performed based on the filtering coefficients to obtain the channel estimation result of the target channel.

[0010] In one embodiment, the frequency domain eigenvalues ​​are matrices with the same matrix order as the frequency domain autocorrelation matrix; updating the time domain eigenvalues ​​based on the frequency domain eigenvalues ​​to obtain updated time domain eigenvalues ​​includes: performing squared summation on the frequency domain eigenvalues ​​to obtain a first sum; calculating the ratio of the matrix order of the frequency domain autocorrelation matrix to the first sum to obtain the reciprocal of the frequency domain average; and updating the time domain eigenvalues ​​based on the reciprocal of the frequency domain average and the noise variance to obtain updated time domain eigenvalues.

[0011] In one embodiment, updating the time-domain feature value based on the reciprocal of the frequency-domain average value and the noise variance to obtain the updated time-domain feature value includes: multiplying the reciprocal of the frequency-domain average value and the noise variance to obtain the updated first noise variance; and adding the updated first noise variance to the values ​​of each element on the main diagonal of the time-domain feature value to obtain the updated time-domain feature value.

[0012] In one embodiment, the time-domain eigenvalues ​​are matrices with the same matrix order as the time-domain autocorrelation matrix; updating the frequency-domain eigenvalues ​​based on the updated time-domain eigenvalues ​​to obtain updated frequency-domain eigenvalues ​​includes: performing a squared summation on the updated time-domain eigenvalues ​​to obtain a second sum; obtaining the reciprocal of the time-domain average value based on the second sum and the matrix order of the time-domain autocorrelation matrix; and updating the frequency-domain eigenvalues ​​based on the reciprocal of the time-domain average value, the reciprocal of the frequency-domain average value, and the noise variance to obtain updated frequency-domain eigenvalues.

[0013] In one embodiment, the reciprocal of the time-domain average value is obtained based on the second sum and the matrix order of the time-domain autocorrelation matrix, including: multiplying the updated first noise variance by the matrix order of the time-domain autocorrelation matrix, and then summing the result with the matrix order of the time-domain autocorrelation matrix to obtain the updated matrix order; and calculating the ratio of the updated matrix order to the second sum to obtain the reciprocal of the time-domain average value.

[0014] In one embodiment, the frequency domain feature value is updated based on the reciprocal of the time-domain average value, the reciprocal of the frequency-domain average value, and the noise variance to obtain the updated frequency domain feature value. This includes: multiplying the reciprocal of the time-domain average value and the noise variance to obtain the updated second noise variance; for each element value on the main diagonal of the frequency domain feature value, multiplying the reciprocal of the frequency-domain average value, the updated second noise variance, and the target element value to obtain the target value; and subtracting the updated second noise variance from the target value and adding it to the target element value to obtain the corresponding element value in the updated frequency domain feature value.

[0015] In one embodiment, time-domain filtering coefficients are obtained based on a time-domain unitary matrix, time-domain eigenvalues, and updated time-domain eigenvalues. Frequency-domain filtering coefficients are obtained based on a frequency-domain unitary matrix, frequency-domain eigenvalues, and updated frequency-domain eigenvalues. This includes: multiplying the inverse matrix of the time-domain unitary matrix, time-domain eigenvalues, and updated time-domain eigenvalues, as well as the conjugate transpose of the time-domain unitary matrix, sequentially to obtain the time-domain filtering coefficients; and multiplying the inverse matrix of the frequency-domain unitary matrix, frequency-domain eigenvalues, and updated frequency-domain eigenvalues, as well as the conjugate transpose of the frequency-domain unitary matrix, to obtain the frequency-domain filtering coefficients.

[0016] Secondly, this application also provides a channel estimation apparatus, comprising:

[0017] The first determining module is used to perform singular value decomposition on the time-domain autocorrelation matrix of the target channel to obtain the time-domain unitary matrix and time-domain eigenvalues ​​corresponding to the time-domain autocorrelation matrix, and to perform singular value decomposition on the frequency-domain autocorrelation matrix of the target channel to obtain the frequency-domain unitary matrix and frequency-domain eigenvalues ​​corresponding to the frequency-domain autocorrelation matrix.

[0018] The update module is used to update the time-domain feature values ​​based on the frequency-domain feature values ​​to obtain the updated time-domain feature values; and to update the frequency-domain feature values ​​based on the updated time-domain feature values ​​to obtain the updated frequency-domain feature values.

[0019] The second determining module is used to obtain time-domain filtering coefficients based on the time-domain unitary matrix, time-domain eigenvalues ​​and updated time-domain eigenvalues, and to obtain frequency-domain filtering coefficients based on the frequency-domain unitary matrix, frequency-domain eigenvalues ​​and updated frequency-domain eigenvalues.

[0020] The estimation module is used to obtain the filtering coefficients based on the time-domain filtering coefficients and the frequency-domain filtering coefficients, and to perform channel estimation based on the filtering coefficients to obtain the channel estimation result of the target channel.

[0021] In one embodiment, the frequency domain eigenvalues ​​are matrices with the same matrix order as the frequency domain autocorrelation matrix; the update module is specifically used to perform squared summation on the frequency domain eigenvalues ​​to obtain a first sum; calculate the ratio of the matrix order of the frequency domain autocorrelation matrix to the first sum to obtain the reciprocal of the frequency domain average; and update the time domain eigenvalues ​​based on the reciprocal of the frequency domain average and the noise variance to obtain the updated time domain eigenvalues.

[0022] In one embodiment, the update module is specifically used to multiply the reciprocal of the frequency domain average value and the noise variance to obtain the updated first noise variance; and to add the updated first noise variance to the values ​​of each element on the main diagonal of the time domain feature value to obtain the updated time domain feature value.

[0023] In one embodiment, the time-domain eigenvalues ​​are matrices with the same matrix order as the time-domain autocorrelation matrix; the update module is specifically used to perform squared summation on the updated time-domain eigenvalues ​​to obtain a second sum; based on the second sum and the matrix order of the time-domain autocorrelation matrix, the reciprocal of the time-domain average value is obtained; based on the reciprocal of the time-domain average value, the reciprocal of the frequency-domain average value, and the noise variance, the frequency-domain eigenvalues ​​are updated to obtain the updated frequency-domain eigenvalues.

[0024] In one embodiment, the update module is specifically used to multiply the updated first noise variance by the matrix order of the time-domain autocorrelation matrix, and then sum the result with the matrix order of the time-domain autocorrelation matrix to obtain the updated matrix order; and to calculate the ratio of the updated matrix order to the second sum to obtain the reciprocal of the time-domain average value.

[0025] In one embodiment, the update module is specifically used to multiply the reciprocal of the time-domain average value and the noise variance to obtain the updated second noise variance; for each element value on the main diagonal of the frequency domain feature value, the reciprocal of the frequency domain average value, the updated second noise variance, and the target element value are multiplied to obtain the target value; the difference between the updated second noise variance and the target value is added to the target element value to obtain the corresponding element value in the updated frequency domain feature value.

[0026] In one embodiment, the second determining module is specifically used to sequentially multiply the time-domain unitary matrix, the time-domain eigenvalues, the inverse matrix of the updated time-domain eigenvalues, and the conjugate transpose of the time-domain unitary matrix to obtain the time-domain filtering coefficients; and to obtain the frequency-domain filtering coefficients by sequentially multiplying the frequency-domain unitary matrix, the frequency-domain eigenvalues, the inverse matrix of the updated frequency-domain eigenvalues, and the conjugate transpose of the frequency-domain unitary matrix.

[0027] Thirdly, this application also provides a communication device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any one of the first aspects above.

[0028] Fourthly, this application also provides a chip including a processor and a communication interface, the processor being configured to cause the chip to perform the steps of the method described in any one of the first aspects above.

[0029] Fifthly, this application also provides a chip module, including a communication module, a power module, a storage module, and a chip, wherein:

[0030] The power module is used to provide power to the chip module;

[0031] The storage module is used to store data and instructions;

[0032] The communication module is used for internal communication within the chip module, or for communication between the chip module and external devices.

[0033] The chip is used to perform the steps of the method described in any one of the first aspects above.

[0034] In a sixth aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0035] In a seventh aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0036] The aforementioned channel estimation method, apparatus, communication equipment, chip, and chip module perform singular value decomposition (SVD) on the time-domain autocorrelation matrix of the target channel to obtain the corresponding time-domain unitary matrix and time-domain eigenvalues. They then perform SVD on the frequency-domain autocorrelation matrix of the target channel to obtain the corresponding frequency-domain unitary matrix and frequency-domain eigenvalues. The time-domain eigenvalues ​​are updated based on the frequency-domain eigenvalues ​​to obtain updated time-domain eigenvalues. The frequency-domain eigenvalues ​​are then updated based on the updated time-domain eigenvalues ​​to obtain updated frequency-domain eigenvalues. Time-domain filtering coefficients are obtained based on the time-domain unitary matrix, time-domain eigenvalues, and updated time-domain eigenvalues. Frequency-domain filtering coefficients are also obtained based on the frequency-domain unitary matrix, frequency-domain eigenvalues, and updated frequency-domain eigenvalues. Finally, filtering coefficients are obtained based on the time-domain and frequency-domain filtering coefficients, and channel estimation is performed based on these filtering coefficients to obtain the channel estimation result for the target channel. In this way, 2x1D-CE channel estimation is achieved. However, compared with the traditional 2x1D-CE scheme, which calculates the filter coefficients based on the autocorrelation matrix and noise variance of each dimension without combining the information of the two dimensions, this application updates the filter coefficients of each dimension jointly based on the singular value decomposition results of the two-dimensional correlation matrix and the noise variance, thereby achieving the goal of optimizing the channel estimation performance. Based on this, this application can reduce the computational overhead while reducing performance loss. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 shows the application environment of the channel estimation method in one embodiment;

[0039] Figure 2 is a flowchart illustrating the channel estimation method in one embodiment;

[0040] Figure 3 is a schematic diagram illustrating the principle of determining the time-domain filter coefficients and the frequency-domain filter coefficients in one embodiment;

[0041] Figure 4 is a simulation comparison diagram of 100 channels in one embodiment;

[0042] Figure 5 shows a simulation comparison diagram under 200 channels in one embodiment.

[0043] Figure 6 is a structural block diagram of a channel estimation device in one embodiment;

[0044] Figure 7 is an internal structure diagram of a communication device in one embodiment;

[0045] Figure 8 is an internal structure diagram of the chip module in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0048] The filtering coefficient determination method provided in this application embodiment can be applied to the application environment shown in Figure 1. The terminal 102 communicates with the access network device 104 via a network. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The access network device 104 can be a base station (BTS) in Global System for Mobile communication (GSM) or Code Division Multiple Access (CDMA), a base station (NodeB, NB) in Wideband Code Division Multiple Access (WCDMA), an evolved Node B (eNB or eNodeB) in LTE, a relay station or access point, or a base station in a 5G network, etc., and is not limited here.

[0049] The channel can be decomposed into multiple dimensions such as time domain, frequency domain and spatial domain. The principle of MMSE (Minimum Mean Squared Error) channel estimation is to use the channel correlation and signal-to-noise ratio of each dimension to calculate the filtering coefficients so that the mean square error between the actual value and the estimated value of the channel matrix is ​​minimized, thereby improving the channel estimation performance.

[0050] MMSE channel estimation is expressed as:

[0051]

[0052] in, This is the LS (Least Squares) channel estimation matrix; This is the autocorrelation matrix; For noise variance; It is a diagonal matrix; These are the filter coefficients, also known as the filter coefficient matrix, and have... ; This is the channel estimation result, also known as the MMSE channel estimation matrix.

[0053] For 2D-CE (2D-Channel Estimation, a channel estimation method that uses joint filtering in two dimensions), taking the joint time-domain and frequency-domain channel estimation as an example, the order of the time-domain channel estimation is... The order of the frequency domain Wiener filter is , The time-domain autocorrelation matrix has dimensions of . , The frequency domain autocorrelation matrix has dimensions of . Then the joint time-domain and frequency-domain channel estimation formula is:

[0054]

[0055] in This refers to the Kronecker Product. For matrix vectorization operations, These are the 2D-CE filter coefficients. Dimensions , Dimensions .set up ,but The dimension is It is evident that the calculation of 2D-CE filter coefficients and the filtering process are both highly complex.

[0056] The 2x1D-CE (2x1D-Channel Estimation, a two-dimensional step-by-step filtering channel estimation method) approach involves performing multi-dimensional channel estimation step-by-step, first estimating one dimension and then moving on to the next. Taking time-domain and frequency-domain 2x1D-CE as an example, assuming frequency-domain channel estimation is performed before time-domain channel estimation, the frequency-domain channel estimation is expressed as:

[0057]

[0058] in The dimension is , Dimensions After frequency domain filtering, time domain channel estimation is performed. The time domain channel estimation is expressed as:

[0059]

[0060] in This is the conjugate-conjugate transpose operation. The time-domain noise is obtained from the frequency-domain filter coefficients and the noise variance. The dimension is As can be seen in 2x1D-CE, channel estimation is performed step by step, with the coefficient matrix for each dimension (e.g., the frequency domain dimension and the time domain dimension in the example above) being... and The size of the filter coefficients is significantly reduced, resulting in a marked improvement in complexity during coefficient calculation and filtering. However, since 2x1D-CE is not the optimal solution for multiple dimensions, it calculates the filter coefficients based on the autocorrelation matrix and noise variance of each dimension, without combining information from both dimensions, resulting in a performance loss compared to 2D-CE.

[0061] Therefore, it is necessary to propose effective technical means to solve the above problems. The technical solution of this application and how it solves the above technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0062] Taking the time and frequency domains as examples, the 2x1D-CE channel estimation can also be expressed as follows:

[0063]

[0064] right After vectorization, we get:

[0065]

[0066] The 2D-CE channel estimation is expressed as: Now Kronecker product consisting of time-domain and frequency-domain filter coefficients This achieves the performance of multi-dimensional joint filtering through multi-dimensional step-by-step filtering. The autocorrelation matrix in the image is converted to SVD (Singular Value Decomposition) form as follows:

[0067]

[0068] in and for The unitary matrix and eigenvalues ​​for SVD decomposition. and for The unitary matrix and eigenvalues ​​for SVD decomposition. If you want to... Kronecker product consisting of time-domain and frequency-domain filter coefficients In terms of form, it is only necessary to consider the eigenvalues. Decompose the diagonal matrix and let ,but

[0069]

[0070] The time-domain filter coefficients and frequency-domain filter coefficients are as follows:

[0071]

[0072]

[0073] Based on this, we can perform SVD decomposition on the correlation matrix of each dimension, and then construct the eigenvalues... The diagonal matrix is ​​decomposed, and finally the time-domain and frequency-domain filter coefficients are updated.

[0074] In an exemplary embodiment, as shown in FIG2, a channel estimation method is provided. The method is illustrated using an example of its application to a communication device (the terminal or access network device in FIG1) or to a chip / chip module with data processing capabilities as shown in FIG1. ​​The method includes steps 201 to 204. Wherein:

[0075] Step 201: Perform singular value decomposition on the time-domain autocorrelation matrix of the target channel to obtain the time-domain unitary matrix and time-domain eigenvalues ​​corresponding to the time-domain autocorrelation matrix. Perform singular value decomposition on the frequency-domain autocorrelation matrix of the target channel to obtain the frequency-domain unitary matrix and frequency-domain eigenvalues ​​corresponding to the frequency-domain autocorrelation matrix.

[0076] The target channel refers to the channel for which channel estimation is to be performed. The time-domain autocorrelation matrix and frequency-domain autocorrelation matrix of the target channel are pre-calculated, and this application does not limit the specific calculation method of the time-domain autocorrelation matrix and frequency-domain autocorrelation matrix.

[0077] Time-domain autocorrelation matrix: Its elements characterize the correlation of the target channel at different time points. It can usually be modeled as a Hermit matrix, and its structure is determined by the Doppler power spectrum of the channel.

[0078] Frequency domain autocorrelation matrix: Its elements characterize the correlation between different frequency points of the channel. Its structure is determined by the power delay spectrum of the channel and is usually a conjugate symmetric non-Topperlitz matrix.

[0079] In one possible implementation, singular value decomposition is performed on the time-domain autocorrelation matrix to obtain:

[0080]

[0081] in, This is the time-domain autocorrelation matrix; For the time domain, the 'W' array, for The conjugate matrix satisfies and Orthogonal; The eigenvalues ​​are time-domain eigenvalues, which are diagonal matrices of the same order as the time-domain autocorrelation matrix.

[0082] Singular value decomposition of the frequency domain autocorrelation matrix yields:

[0083]

[0084] in, This is the frequency domain autocorrelation matrix; For frequency domain unitary arrays, for The conjugate matrix satisfies and Orthogonal; These are frequency domain eigenvalues, which are diagonal matrices of the same order as the frequency domain autocorrelation matrix.

[0085] Step 202: Update the time-domain feature value based on the frequency-domain feature value to obtain the updated time-domain feature value; update the frequency-domain feature value based on the updated time-domain feature value to obtain the updated frequency-domain feature value.

[0086] In one possible implementation, the frequency domain eigenvalues ​​are summed by squares to obtain a first sum; the time domain eigenvalues ​​are then updated based on the first sum to obtain updated time domain eigenvalues. The updated time domain eigenvalues ​​are then summed by squares to obtain a second sum; the frequency domain eigenvalues ​​are then updated based on the second sum to obtain updated frequency domain eigenvalues.

[0087] Step 203: Based on the time-domain unitary matrix, time-domain eigenvalues, and updated time-domain eigenvalues, obtain the time-domain filtering coefficients; based on the frequency-domain unitary matrix, frequency-domain eigenvalues, and updated frequency-domain eigenvalues, obtain the frequency-domain filtering coefficients.

[0088] In one possible implementation, the time-domain filter coefficients are obtained by sequentially multiplying the time-domain unitary matrix, the time-domain eigenvalues, the inverse matrix of the updated time-domain eigenvalues, and the conjugate transpose of the time-domain unitary matrix; the frequency-domain filter coefficients are obtained by sequentially multiplying the frequency-domain unitary matrix, the frequency-domain eigenvalues, the inverse matrix of the updated frequency-domain eigenvalues, and the conjugate transpose of the frequency-domain unitary matrix.

[0089] Expressed mathematically as follows:

[0090]

[0091]

[0092] in, These are the time-domain filter coefficients; These are the frequency domain filtering coefficients.

[0093] Step 204: Based on the time-domain filtering coefficients and the frequency-domain filtering coefficients, obtain the filtering coefficients, and perform channel estimation based on the filtering coefficients to obtain the channel estimation result of the target channel.

[0094] In one possible implementation, the Kronecker product of the time-domain filter coefficients and the frequency-domain filter coefficients is used as the filter coefficients. Expressed mathematically as follows:

[0095]

[0096] The channel estimation result can be expressed as:

[0097]

[0098] in, The LS channel estimation matrix is... This is a matrix vectorization operation.

[0099] The aforementioned channel estimation method performs singular value decomposition (SVD) on the time-domain autocorrelation matrix of the target channel to obtain the corresponding time-domain unitary matrix and time-domain eigenvalues. It then performs SVD on the frequency-domain autocorrelation matrix of the target channel to obtain the corresponding frequency-domain unitary matrix and frequency-domain eigenvalues. The time-domain eigenvalues ​​are updated based on the frequency-domain eigenvalues ​​to obtain updated time-domain eigenvalues. The frequency-domain eigenvalues ​​are then updated based on the updated time-domain eigenvalues ​​to obtain updated frequency-domain eigenvalues. Time-domain filtering coefficients are obtained based on the time-domain unitary matrix, time-domain eigenvalues, and updated time-domain eigenvalues. Frequency-domain filtering coefficients are also obtained based on the frequency-domain unitary matrix, frequency-domain eigenvalues, and updated frequency-domain eigenvalues. Finally, filtering coefficients are obtained based on the time-domain and frequency-domain filtering coefficients, and channel estimation is performed based on these filtering coefficients to obtain the channel estimation result for the target channel. In this way, 2x1D-CE channel estimation is achieved. However, compared with the traditional 2x1D-CE scheme, which calculates the filter coefficients based on the autocorrelation matrix and noise variance of each dimension without combining the information of the two dimensions, this application updates the filter coefficients of each dimension jointly based on the singular value decomposition results of the two-dimensional correlation matrix and the noise variance, thereby achieving the goal of optimizing the channel estimation performance. Based on this, this application can reduce the computational overhead while reducing performance loss.

[0100] In one embodiment, the frequency domain eigenvalues ​​are matrices with the same matrix order as the frequency domain autocorrelation matrix; updating the time domain eigenvalues ​​based on the frequency domain eigenvalues ​​to obtain updated time domain eigenvalues ​​includes: performing squared summation on the frequency domain eigenvalues ​​to obtain a first sum; calculating the ratio of the matrix order of the frequency domain autocorrelation matrix to the first sum to obtain the reciprocal of the frequency domain average; and updating the time domain eigenvalues ​​based on the reciprocal of the frequency domain average and the noise variance to obtain updated time domain eigenvalues.

[0101] The process of performing a sum-of-squares operation on the frequency domain eigenvalues, which involves calculating the sum of squares of the elements on the main diagonal of the frequency domain eigenvalues, can be expressed mathematically as follows:

[0102]

[0103] in, Let be the matrix order of the frequency domain autocorrelation matrix; It is the square of the element value in the j-th row and j-th column of the frequency domain eigenvalues; This is the first sum.

[0104] reciprocal of the frequency domain average for:

[0105]

[0106] In one possible implementation, the time-domain feature value is updated based on the reciprocal of the frequency domain average value and the noise variance to obtain the updated time-domain feature value. This includes: multiplying the reciprocal of the frequency domain average value and the noise variance to obtain the updated first noise variance; and adding the updated first noise variance to the values ​​of each element on the main diagonal of the time-domain feature value to obtain the updated time-domain feature value.

[0107] The updated time-domain eigenvalues ​​are expressed mathematically as follows:

[0108]

[0109] in, Let be the value of the element in the i-th row and i-th column of the time-domain feature values; For noise variance, This is the updated first noise variance; This refers to the element value in the i-th row and i-th column of the updated time-domain feature values.

[0110] In one embodiment, the time-domain eigenvalues ​​are matrices with the same matrix order as the time-domain autocorrelation matrix; updating the frequency-domain eigenvalues ​​based on the updated time-domain eigenvalues ​​to obtain updated frequency-domain eigenvalues ​​includes: performing a squared summation on the updated time-domain eigenvalues ​​to obtain a second sum; obtaining the reciprocal of the time-domain average value based on the second sum and the matrix order of the time-domain autocorrelation matrix; and updating the frequency-domain eigenvalues ​​based on the reciprocal of the time-domain average value, the reciprocal of the frequency-domain average value, and the noise variance to obtain updated frequency-domain eigenvalues.

[0111] The updated time-domain eigenvalues ​​are subjected to a sum-of-squares process, which involves calculating the sum of squares of the elements on the main diagonal of the updated time-domain eigenvalues. This can be expressed mathematically as follows:

[0112]

[0113] in, Let be the matrix order of the time-domain autocorrelation matrix; The value is the square of the element in the i-th row and i-th column of the updated time-domain feature values; This is the second sum.

[0114] In one possible implementation, the reciprocal of the time-domain average is obtained based on the second sum and the matrix order of the time-domain autocorrelation matrix, including: multiplying the updated first noise variance by the matrix order of the time-domain autocorrelation matrix, and then summing the result with the matrix order of the time-domain autocorrelation matrix to obtain the updated matrix order; and calculating the ratio of the updated matrix order to the second sum to obtain the reciprocal of the time-domain average.

[0115] reciprocal of the time-domain average for:

[0116]

[0117] In one possible implementation, the frequency domain feature values ​​are updated based on the reciprocal of the time-domain average, the reciprocal of the frequency-domain average, and the noise variance to obtain updated frequency domain feature values. This includes: multiplying the reciprocal of the time-domain average and the noise variance to obtain an updated second noise variance; for each element value on the main diagonal of the frequency domain feature values, multiplying the reciprocal of the frequency-domain average, the updated second noise variance, and the target element value to obtain a target value; and subtracting the updated second noise variance from the target value and adding it to the target element value to obtain the corresponding element value in the updated frequency domain feature values.

[0118] The updated frequency domain eigenvalues ​​are expressed mathematically as follows:

[0119]

[0120] in, is the value of the element in the j-th row and j-th column of the frequency domain eigenvalues; For noise variance, This is the updated second noise variance; It is the reciprocal of the frequency domain average. The target value; This refers to the element value in the j-th row and j-th column of the updated frequency domain eigenvalues.

[0121] In summary, Figure 3 provides a schematic diagram illustrating the principle of determining time-domain and frequency-domain filter coefficients. The specific process has been described in detail above and will not be repeated here.

[0122] Simulations were performed under PDSCH (Physical Downlink Shared Channel) type 1 DMRS (Demodulation Reference Signal). The simulation results for different channels are shown in Figures 4 and 5. Figure 4 compares the performance of the proposed scheme with the original scheme under the TDLC300 Doppler100 channel. Figure 5 compares the performance of the proposed scheme with the original scheme under the ETU Doppler200 channel. In both Figures 4 and 5, the vertical axis represents the minimum mean square error; the horizontal axis represents different positions within the channel.

[0123] Figures 4 and 5 respectively provide performance comparisons between our proposed scheme and existing schemes for real-number filtering and complex-number filtering. Under real-number filtering, our scheme (red line) shows a significant performance gain compared to the traditional 2x1D-CE scheme (purple line), and its performance is close to that of the 2D-CE scheme (yellow line). Under complex-number filtering, our scheme (green line) shows a significant performance gain compared to the traditional 2x1D-CE scheme (blue line). Real-number filtering uses coefficients corresponding to a pre-stored rectangular spectrum, while complex-number filtering uses coefficients corresponding to the actual estimated time-delay power spectrum.

[0124] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0125] Based on the same inventive concept, this application also provides a channel estimation apparatus for implementing the channel estimation method described above. This apparatus can be applied to or integrated into a chip or chip module, for example. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more channel estimation apparatus embodiments provided below can be found in the limitations of the channel estimation method described above, and will not be repeated here.

[0126] In an exemplary embodiment, as shown in FIG6, a channel estimation device 600 is provided, comprising: a first determining module 601, an updating module 602, a second determining module 603, and an estimating module 604, wherein:

[0127] The first determining module 601 is used to perform singular value decomposition on the time-domain autocorrelation matrix of the target channel to obtain the time-domain unitary matrix and time-domain eigenvalues ​​corresponding to the time-domain autocorrelation matrix, and to perform singular value decomposition on the frequency-domain autocorrelation matrix of the target channel to obtain the frequency-domain unitary matrix and frequency-domain eigenvalues ​​corresponding to the frequency-domain autocorrelation matrix.

[0128] The update module 602 is used to update the time domain feature value based on the frequency domain feature value to obtain the updated time domain feature value; and to update the frequency domain feature value based on the updated time domain feature value to obtain the updated frequency domain feature value.

[0129] The second determining module 603 is used to obtain time-domain filtering coefficients based on the time-domain unitary matrix, time-domain eigenvalues ​​and updated time-domain eigenvalues, and to obtain frequency-domain filtering coefficients based on the frequency-domain unitary matrix, frequency-domain eigenvalues ​​and updated frequency-domain eigenvalues.

[0130] The estimation module 604 is used to obtain the filtering coefficients based on the time-domain filtering coefficients and the frequency-domain filtering coefficients, and to perform channel estimation based on the filtering coefficients to obtain the channel estimation result of the target channel.

[0131] In one embodiment, the frequency domain eigenvalues ​​are matrices with the same matrix order as the frequency domain autocorrelation matrix; the update module 602 is specifically used to perform squared summation on the frequency domain eigenvalues ​​to obtain a first sum; calculate the ratio of the matrix order of the frequency domain autocorrelation matrix to the first sum to obtain the reciprocal of the frequency domain average value; update the time domain eigenvalues ​​based on the reciprocal of the frequency domain average value and the noise variance to obtain the updated time domain eigenvalues.

[0132] In one embodiment, the update module 602 is specifically used to multiply the reciprocal of the frequency domain average value and the noise variance to obtain the updated first noise variance; and to add the updated first noise variance to the values ​​of each element on the main diagonal of the time domain feature value to obtain the updated time domain feature value.

[0133] In one embodiment, the time-domain eigenvalues ​​are matrices with the same matrix order as the time-domain autocorrelation matrix; the update module 602 is specifically used to perform squared summation on the updated time-domain eigenvalues ​​to obtain a second sum; based on the second sum and the matrix order of the time-domain autocorrelation matrix, the reciprocal of the time-domain average value is obtained; based on the reciprocal of the time-domain average value, the reciprocal of the frequency-domain average value, and the noise variance, the frequency-domain eigenvalues ​​are updated to obtain the updated frequency-domain eigenvalues.

[0134] In one embodiment, the update module 602 is specifically used to multiply the updated first noise variance by the matrix order of the time-domain autocorrelation matrix, and then sum the result with the matrix order of the time-domain autocorrelation matrix to obtain the updated matrix order; and to calculate the ratio of the updated matrix order to the second sum to obtain the reciprocal of the time-domain average value.

[0135] In one embodiment, the update module 602 is specifically used to multiply the reciprocal of the time-domain average value and the noise variance to obtain the updated second noise variance; for each element value on the main diagonal of the frequency domain feature value, multiply the reciprocal of the frequency domain average value, the updated second noise variance, and the target element value to obtain the target value; subtract the updated second noise variance from the target value and add it to the target element value to obtain the corresponding element value in the updated frequency domain feature value.

[0136] In one embodiment, the second determining module 603 is specifically used to multiply the time-domain unitary matrix, the time-domain eigenvalues, the inverse matrix of the updated time-domain eigenvalues, and the conjugate transpose of the time-domain unitary matrix in sequence to obtain the time-domain filtering coefficients; and to obtain the frequency-domain filtering coefficients by multiplying the frequency-domain unitary matrix, the frequency-domain eigenvalues, the inverse matrix of the updated frequency-domain eigenvalues, and the conjugate transpose of the frequency-domain unitary matrix.

[0137] Regarding the modules / units included in the various devices and products described in the above embodiments, they can be software modules / units, hardware modules / units, or a combination of both. For example, for various devices and products applied to or integrated into a chip, all of their modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits; for various devices and products applied to or integrated into a chip module, all of their modules / units can be implemented using hardware methods such as circuits, and different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The components can be implemented using software programs that run on the processor integrated within the chip module. The remaining (if any) modules / units can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into the terminal, each of its components / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or in different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on the processor integrated within the terminal, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits.

[0138] In an exemplary embodiment, a communication device is provided, which can be a terminal, and its internal structure diagram is shown in Figure 7. The communication device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the communication device provides computing and control capabilities. The memory of the communication device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the communication device is used for exchanging information between the processor and external devices. The communication interface of the communication device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a channel estimation method. The display unit of the communication device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the communication device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the communication device, or external keyboards, touchpads, or mice, etc.

[0139] Those skilled in the art will understand that the structure shown in Figure 7 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0140] In one exemplary embodiment, a communication device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any one of the above method embodiments.

[0141] Based on the same inventive concept, this application also provides a chip, including a processor and a communication interface; the communication interface is used to receive or send data; the processor is configured to cause the chip to perform the steps of any one of the methods described in the above method embodiments.

[0142] It is understood that the chip involved in the embodiments of this application may be a field-programmable gate array (FPGA), may be an application-specific integrated circuit (ASIC), may be a system on chip (SoC), may be a central processor unit (CPU), may be a network processor (NP), may be a digital signal processor (DSP), may be a microcontroller unit (MCU), may be a programmable logic device (PLD), or other integrated chips, etc.

[0143] Based on the same inventive concept, this application also provides a chip module, as shown in Figure 8. The chip module includes a communication module, a power module, a storage module, and a chip. Wherein:

[0144] The power module is used to provide power to the chip module; the storage module is used to store data and instructions; the communication module is used for internal communication within the chip module, or for communication between the chip module and external devices; this chip corresponds to the chip in the above chip embodiment.

[0145] The implementation method of this chip module can be found in the relevant content of the above chip embodiment, and will not be repeated here.

[0146] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above method embodiments.

[0147] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in any one of the above method embodiments.

[0148] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0149] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0150] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A channel estimation method, characterized in that, The method includes: performing singular value decomposition on the time-domain autocorrelation matrix of the target channel to obtain the time-domain unitary matrix and time-domain eigenvalues ​​corresponding to the time-domain autocorrelation matrix; performing singular value decomposition on the frequency-domain autocorrelation matrix of the target channel to obtain the frequency-domain unitary matrix and frequency-domain eigenvalues ​​corresponding to the frequency-domain autocorrelation matrix; updating the time-domain eigenvalues ​​based on the frequency-domain eigenvalues ​​to obtain updated time-domain eigenvalues; updating the frequency-domain eigenvalues ​​based on the updated time-domain eigenvalues ​​to obtain updated frequency-domain eigenvalues; obtaining time-domain filtering coefficients based on the time-domain unitary matrix, the time-domain eigenvalues, and the updated time-domain eigenvalues; obtaining frequency-domain filtering coefficients based on the frequency-domain unitary matrix, the frequency-domain eigenvalues, and the updated frequency-domain eigenvalues; obtaining filtering coefficients based on the time-domain filtering coefficients and the frequency-domain filtering coefficients; and performing channel estimation based on the filtering coefficients to obtain the channel estimation result of the target channel.

2. The method according to claim 1, characterized in that, The frequency domain feature value is a matrix with the same matrix order as the frequency domain autocorrelation matrix; updating the time domain feature value based on the frequency domain feature value to obtain the updated time domain feature value includes: performing a squared summation on the frequency domain feature value to obtain a first sum; calculating the ratio of the matrix order of the frequency domain autocorrelation matrix to the first sum to obtain the reciprocal of the frequency domain average value; updating the time domain feature value based on the reciprocal of the frequency domain average value and the noise variance to obtain the updated time domain feature value.

3. The method according to claim 2, characterized in that, The step of updating the time-domain feature value based on the reciprocal of the frequency domain average value and the noise variance to obtain the updated time-domain feature value includes: multiplying the reciprocal of the frequency domain average value and the noise variance to obtain the updated first noise variance; and adding the updated first noise variance to the values ​​of each element on the main diagonal of the time-domain feature value to obtain the updated time-domain feature value.

4. The method according to claim 3, characterized in that, The time-domain feature value is a matrix with the same matrix order as the time-domain autocorrelation matrix; updating the frequency-domain feature value based on the updated time-domain feature value to obtain the updated frequency-domain feature value includes: performing a squared summation on the updated time-domain feature value to obtain a second sum; obtaining the reciprocal of the time-domain average value based on the second sum and the matrix order of the time-domain autocorrelation matrix; updating the frequency-domain feature value based on the reciprocal of the time-domain average value, the reciprocal of the frequency-domain average value, and the noise variance to obtain the updated frequency-domain feature value.

5. The method according to claim 4, characterized in that, The step of obtaining the reciprocal of the time-domain average based on the second sum and the matrix order of the time-domain autocorrelation matrix includes: multiplying the updated first noise variance by the matrix order of the time-domain autocorrelation matrix, and then summing the result with the matrix order of the time-domain autocorrelation matrix to obtain the updated matrix order; and calculating the ratio of the updated matrix order to the second sum to obtain the reciprocal of the time-domain average.

6. The method according to claim 4, characterized in that, The step of updating the frequency domain feature value based on the reciprocal of the time domain average, the reciprocal of the frequency domain average, and the noise variance to obtain the updated frequency domain feature value includes: multiplying the reciprocal of the time domain average and the noise variance to obtain the updated second noise variance; multiplying the reciprocal of the frequency domain average, the updated second noise variance, and the target element value for each element value on the main diagonal of the frequency domain feature value to obtain the target value; and subtracting the updated second noise variance from the target value and adding it to the target element value to obtain the corresponding element value in the updated frequency domain feature value.

7. A channel estimation device, characterized in that, The apparatus includes: a first determining module, configured to perform singular value decomposition on the time-domain autocorrelation matrix of the target channel to obtain a time-domain unitary matrix and time-domain eigenvalues ​​corresponding to the time-domain autocorrelation matrix, and perform singular value decomposition on the frequency-domain autocorrelation matrix of the target channel to obtain a frequency-domain unitary matrix and frequency-domain eigenvalues ​​corresponding to the frequency-domain autocorrelation matrix; an updating module, configured to update the time-domain eigenvalues ​​based on the frequency-domain eigenvalues ​​to obtain updated time-domain eigenvalues; and update the frequency-domain eigenvalues ​​based on the updated time-domain eigenvalues ​​to obtain updated frequency-domain eigenvalues; a second determining module, configured to obtain time-domain filtering coefficients based on the time-domain unitary matrix, the time-domain eigenvalues, and the updated time-domain eigenvalues, and obtain frequency-domain filtering coefficients based on the frequency-domain unitary matrix, the frequency-domain eigenvalues, and the updated frequency-domain eigenvalues; and an estimating module, configured to obtain filtering coefficients based on the time-domain filtering coefficients and the frequency-domain filtering coefficients, and perform channel estimation based on the filtering coefficients to obtain a channel estimation result for the target channel.

8. A communication device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A chip, characterized in that, The device includes a processor and a communication interface, wherein the processor is configured to cause the chip to perform the steps of the method described in any one of claims 1 to 6.

10. A chip module, characterized in that, The device includes a communication module, a power module, a storage module, and a chip, wherein: the power module provides power to the chip module; the storage module stores data and instructions; the communication module performs internal communication within the chip module or communication between the chip module and external devices; and the chip performs the steps of the method described in any one of claims 1 to 6.

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