Channel estimation method and device for OTFS modulation

Abstract

The invention discloses a channel estimation method and device for OTFS modulation. The method comprises the following steps: acquiring a transmission symbol matrix X, a maximum Doppler frequency shift, a time delay-Doppler grid dimension value and a sampling time interval of OTFS modulation; based on the emission symbol matrix X, the maximum Doppler frequency shift, the time delay-Doppler grid dimension value and the sampling time interval, a primary function coefficient matrix solving model is constructed; and based on the emission symbol matrix X, solving the primary function coefficient matrix solving model to obtain an OTFS modulated time domain channel response matrix.

Description

Technical Field

[0001] The present invention relates to the fields of OTFS modulation technology, high-reliability communication, and channel estimation, and particularly to a channel estimation method and device for OTFS modulation. Background Art

[0002] Ensuring the reliability and throughput of information transmission in high-speed mobile scenarios is a current popular research direction. For the high-speed mobile channel corresponding to high-speed mobile scenarios, although it exhibits fast time-varying characteristics in the time-frequency domain, it can be equivalently represented by an impulse response as a time-invariant channel in the delay-Doppler domain. Therefore, the magnitudes of delay and Doppler frequency offset can be easily estimated in the delay-Doppler domain. The OTFS modulation technology represents signals in the delay-Doppler domain and can effectively combat the fast time-varying nature of the channel.

[0003] In actual communication scenarios, since the number of reflectors is necessarily finite, the number of multipaths is also finite. The channel has a sparse characteristic in the delay-Doppler domain, which greatly reduces the overhead of pilot symbols for channel estimation and solves the problem of throughput reduction caused by increasing pilot symbols. OTFS signals are interfered with in a doubly selective channel, and the receiver must perform channel estimation to correctly recover the signals. Channel estimation is crucial for the normal operation of the receiver, and its performance directly affects subsequent equalization and signal detection results. Currently, a relatively popular method is the channel estimation algorithm based on embedded pilot assistance, but this method has too high a peak-to-average ratio and is difficult to apply in actual engineering applications. Summary of the Invention

[0004] The present invention mainly solves the problem of how to accurately estimate a fast time-varying channel based on OFTS modulation, and discloses a channel estimation method and device for OTFS modulation.

[0005] In the first aspect of the embodiments of the present invention, a channel estimation method for OTFS modulation is disclosed, including:

[0006] S1, obtaining the transmit symbol matrix X of OTFS modulation, the maximum Doppler frequency shift, the delay-Doppler grid dimension value, and the sampling time interval;

[0007] S2, constructing a solution model for the basis function coefficient matrix based on the transmit symbol matrix X, the maximum Doppler frequency shift, the delay-Doppler grid dimension value, and the sampling time interval;

[0008] S3, solving the solution model for the basis function coefficient matrix based on the transmit symbol matrix X to obtain the time-domain channel response matrix of OTFS modulation.

[0009] Constructing a basis function coefficient matrix solution model based on the transmitted symbol matrix X, the maximum Doppler shift, the delay-Doppler grid dimension value, and the sampling time interval, including:

[0010] S21. Calculating a dimension value Q based on the maximum Doppler shift, the delay-Doppler grid dimension value, and the sampling time interval;

[0011] S22. Constructing a first information matrix B based on the transmitted symbol matrix X p ;

[0012] S23. Constructing a basis function coefficient matrix solution model based on the first information matrix B p . The expression of the basis function coefficient matrix solution model is:

[0013]

[0014] where G is the basis function coefficient matrix to be solved, is a partial time-domain channel matrix.

[0015] Constructing the first information matrix B based on the transmitted symbol matrix X p , including:

[0016] S221. Performing pilot extraction processing on the transmitted symbol matrix X to obtain a pilot matrix X p ;

[0017] S222. Extracting the column vectors of the pilot matrix X p ;

[0018] S223. Performing repeated square loop operation processing on all the extracted column vectors to obtain the first information matrix B p .

[0019] Performing the pilot extraction processing on the transmitted symbol matrix X to obtain the pilot matrix X p , including:

[0020] S2211. Calculating a received symbol matrix Z using the transmitted symbol matrix X;

[0021] S2212. Calculating the pilot matrix X using the received symbol matrix Z p ;

[0022] The expression of the received symbol matrix Z is:

[0023]

[0024] Z p [i,n] = Z[mp +i,n], i = 0, …, l max ,

[0025] Where Z[m,n] is the element in the m-th row and n-th column of the received symbol matrix Z, is the channel characteristic matrix, W P is a noise matrix with a matrix dimension of l max ×N, l max represents the ratio of the maximum time delay of the channel to the sampling time interval, M and N are the row dimension and column dimension of the matrix H respectively, vec means arranging all column vectors of the matrix into a vector, m DD = M / 2, m p is the middle position factor, Z p represents the first symbol matrix, Z p represents the first symbol matrix, Z p [i,n] represents the element in the i-th row and n-th column of the first symbol matrix, X[|m-k|,|n-l|] represents the element in the |m-k|-th row and |n-l|-th column of the matrix X.

[0026] The expression of the repeated square cyclic operation processing is:

[0027]

[0028] Where represents the i-th column vector of the pilot matrix X p , N is the column dimension of the pilot matrix, circ(·) represents the square cyclic matrix operation.

[0029] Based on the transmit symbol matrix X, solving the basis function coefficient matrix solving model to obtain the time-domain channel response matrix of OTFS modulation includes:

[0030] S31, constructing a first information matrix B based on the transmit symbol matrix X p ;

[0031] S32, performing eigenvector calculation processing on the channel characteristic matrix and the noise matrix to obtain a first vector and a second vector;

[0032] S33, performing partial channel response estimation processing on the first vector and the second vector to obtain a partial time-domain channel matrix

[0033] S34, solving the basis function coefficient matrix G to be solved based on the partial time-domain channel matrix to obtain the solution value of the basis function coefficient matrix G;

[0034] S35. Calculate the channel response based on the solution value of the basis function coefficient matrix G to obtain the time-domain channel response matrix of OTFS modulation;

[0035] The expression for the channel response calculation is:

[0036] H = G · B,

[0037] where H is the time-domain channel response matrix of OTFS modulation, and B is the basis function matrix.

[0038] The expression for the eigenvector calculation process is:

[0039]

[0040] where, is the channel feature matrix, W p is the noise matrix, and are the first vector and the second vector respectively;

[0041] The expression for the partial channel response estimation process is:

[0042]

[0043] where, is the matrix obtained by rearranging column by column with a dimension of l max × N, is the third vector, is the Fourier transform matrix, and the noise vector is the corresponding row vector, is the N × N-dimensional inverse discrete Fourier transform matrix, and the upper right corner H is the conjugate symmetry operation.

[0044] In the second aspect of the embodiments of the present invention, a channel estimation device for OTFS modulation is disclosed. The device includes:

[0045] A memory storing executable program code;

[0046] A processor coupled to the memory;

[0047] The processor calls the executable program code stored in the memory to execute the channel estimation method for OTFS modulation described above.

[0048] In a third aspect of the embodiments of the present invention, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer instructions, which are used to execute the channel estimation method for OTFS modulation when called by a computer.

[0049] In a fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the channel estimation method for OTFS modulation.

[0050] The beneficial effects of the present invention are as follows:

[0051] An improved embedded pilot channel estimation algorithm for OTFS modulation is proposed in this paper. Compared with the traditional method, when the bit error rate is at the order of 1e-4, the improved algorithm obtains a 10dB peak-to-average power ratio (PAPR) gain at the cost of a 1.5dB performance loss, and can be more effectively applied to practical engineering.

[0052] This method first uses a delay-Doppler domain embedded pilot scheme to estimate the local channel response, and then converts it to the time domain and restores the complete time-domain channel response through an optimized basis expansion model (BEM) method. This method can effectively solve the problem of high PAPR of the traditional embedded pilot channel estimation algorithm, and thus is more practical in engineering applications. Brief Description of the Drawings

[0053] Figure 1 is the implementation flowchart of the method of the present invention;

[0054] Figure 2 is the CCDF performance comparison diagram of different channel estimation algorithms;

[0055] Figure 3 is the bit error rate comparison diagram of different channel estimation algorithms;

[0056] Figure 4 is the schematic diagram for constructing the double circulant block matrix B. Detailed Embodiments

[0057] To better understand the content of the present invention, an embodiment is given here.

[0058] Figure 1 is the implementation flowchart of the method of the present invention. Figure 2 is the CCDF performance comparison diagram of different channel estimation algorithms; Figure 3 is the bit error rate comparison diagram of different channel estimation algorithms. Figure 4 is the schematic diagram for constructing the double circulant block matrix B.

[0059] In a first aspect of the embodiments of the present invention, a channel estimation method for OTFS modulation is disclosed, including:

[0060] S1. Obtain the transmitted symbol matrix X for OTFS modulation, the maximum Doppler shift, the time-delay Doppler grid dimension value, and the sampling time interval;

[0061] S2. Based on the transmitted symbol matrix X, the maximum Doppler shift, the time-delay Doppler grid dimension value, and the sampling time interval, construct a solution model for the basis function coefficient matrix;

[0062] S3. Based on the transmitted symbol matrix X, solve the solution model for the basis function coefficient matrix to obtain the time-domain channel response matrix for OTFS modulation.

[0063] The construction of the solution model for the basis function coefficient matrix based on the transmitted symbol matrix X, the maximum Doppler shift, the time-delay Doppler grid dimension value, and the sampling time interval includes:

[0064] S21. Calculate the dimension value Q based on the maximum Doppler shift, the time-delay Doppler grid dimension value, and the sampling time interval;

[0065] S22. Based on the transmitted symbol matrix X, construct the first information matrix B p ;

[0066] S23. Based on the first information matrix B p , construct the solution model for the basis function coefficient matrix; the expression of the solution model for the basis function coefficient matrix is:

[0067]

[0068] where G is the basis function coefficient matrix to be solved, is a partial time-domain channel matrix.

[0069] The construction of the first information matrix B based on the transmitted symbol matrix X p , includes:

[0070] Perform pilot extraction processing on the transmitted symbol matrix X to obtain the pilot matrix X p ;

[0071] Extract the column vectors of the pilot matrix X p ;

[0072] Perform repeated square loop operation processing on all the extracted column vectors to obtain the first information matrix B p ;

[0073] The performance of pilot extraction processing on the transmitted symbol matrix X to obtain the pilot matrix X p , includes:

[0074] Calculate the received symbol matrix Z by using the transmitted symbol matrix X;

[0075] Calculate the pilot matrix X by using the received symbol matrix Z p ;

[0076] The expression of the received symbol matrix Z is:

[0077]

[0078] Z p [i, n] = Z[m p + i, n], i = 0, …, l max ,

[0079] where Z[m, n] is the element in the m-th row and n-th column of the received symbol matrix Z, is the channel characteristic matrix, W P is a noise matrix with a matrix dimension of l max × N, l max represents the ratio of the maximum time delay of the channel to the sampling time interval, M and N are the row dimension and column dimension of the matrix H DD respectively, vec means arranging all column vectors of the matrix into a vector, m p = M / 2, m p is the middle position factor, Z p represents the first symbol matrix, Z p [i, n] represents the element in the i-th row and n-th column of the first symbol matrix, X[|m - k|, |n - l|] represents the element in the |m - k|-th row and |n - l|-th column of the matrix X.

[0080] The expression of the repeated square cyclic operation process is:

[0081]

[0082] where, represents the i-th column vector of the pilot matrix X p , N is the column dimension of the pilot matrix, circ(·) represents the square cyclic matrix operation;

[0083] Based on the transmitted symbol matrix X, solve the model of the basis function coefficient matrix to obtain the time-domain channel response matrix of OTFS modulation, including:

[0084] S31, construct the first information matrix B based on the transmitted symbol matrix X p ;

[0085] S32. Perform eigenvector calculation processing on the channel feature matrix and the noise matrix to obtain a first vector and a second vector;

[0086] S33. Perform partial channel response estimation processing on the first vector and the second vector to obtain a partial time-domain channel matrix

[0087] S34. Based on the partial time-domain channel matrix Solve the basis function coefficient matrix G to be solved to obtain the solution value of the basis function coefficient matrix G;

[0088] S35. Perform channel response calculation on the solution value of the basis function coefficient matrix G to obtain the time-domain channel response matrix of OTFS modulation;

[0089] The expression of the channel response calculation is:

[0090] H = G·B,

[0091] where H is the time-domain channel response matrix of OTFS modulation, and B is the basis function matrix;

[0092] The expression b q (n) of the q-th row and the n-th column of the basis function matrix B is:

[0093]

[0094] where M and N are the dimension sizes of the delay-Doppler grid, and are also the row dimension and the column dimension of the matrix H DD , P is a preset constant factor, f max and T s are the maximum Doppler frequency shift and the sampling time interval respectively, represents rounding up;

[0095] The calculation expression of the solution value of the basis function coefficient matrix G is:

[0096]

[0097] The calculation of the dimension value Q based on the maximum Doppler frequency shift, the delay-Doppler grid dimension value, and the sampling time interval includes: f max and T s are the maximum Doppler frequency shift and the sampling time interval respectively.

[0098] The expression of the eigenvector calculation processing is:

[0099]

[0100] where, is the channel feature matrix, and W p is the noise matrix, and are the first vector and the second vector respectively;

[0101] The expression of the partial channel response estimation process is:

[0102]

[0103] where is the matrix obtained by rearranging column by column with dimension l max ×N, is the third vector, is the Fourier transform matrix, and the noise vector is the corresponding row vector, is the N×N-dimensional inverse discrete Fourier transform matrix, and the upper right H is the conjugate symmetric operation.

[0104] The channel feature matrix can be calculated by the following formula: represents the vector corresponding to Z p

[0105] To improve the estimation accuracy of the channel feature matrix , the accuracy improvement process can be performed on the calculated at multiple times;

[0106] The expression of the accuracy improvement process is:

[0107]

[0108] where h ij is the element in the i-th row and j-th column of the channel feature matrix after the accuracy improvement process, ω1 and ω2 are the first weighting factor and the second weighting factor respectively, and h k,ij is the element in the i-th row and j-th column of the channel feature matrix calculated at the k-th time, is the mean value of the elements in the i-th row and j-th column of the channel feature matrix at all times, K is the total number of calculation times, and μ is a preset division factor, which can be obtained by calculating the mean value under the elements of all rows and columns.

[0109] The rows of matrix B p are the rows of matrix B, and the columns of matrix B p are the M / 2 + M×k columns of matrix B, where k = 0,..., N - 1.​

[0110] In this embodiment, the performance of the channel estimation algorithm of the present invention and the performance of the traditional embedded pilot algorithm are simulated under the TU channel condition, and the CCDF and BER are used as evaluation indicators. The specific simulation parameters are shown in Table 1. For the convenience of marking and understanding, the algorithms proposed in this section are denoted as Proposed-BEM, Proposed-Linear, Proposed-Spline, and Proposed-Cubic respectively.

[0111] Table 1 Channel Estimation Simulation Parameters

[0112]

[0113] Based on the need to set a very high pilot signal-to-noise ratio for the embedded pilot, the pilot power is set 40 dB higher than the noise power in this simulation. From Figure 2 It can be clearly seen that using the traditional embedded pilot method, the maximum peak-to-average ratio is as high as 20 dB, about 10 dB higher than the original signal, which will inhibit the use efficiency of the power amplifier. For the improved method proposed in this paper, the CCDF of the transmitted signal increases by about 0.71 dB at the probability of 10 -6 order of magnitude compared with the original signal. The increase in the peak-to-average ratio is due to the use of zero-sequence-filled guard symbols in the embedded pilot, which reduces the average power of the signal.

[0114] In the simulation parameters of this section, the maximum multipath delay l max = 10, then the total number of pilot symbols and guard symbols used is (2l max +1)×N. Assuming the energy of each modulation symbol is σ 2 , according to the above analysis section, the pilot energy is Then theoretically, after using the improved channel estimation method, the average power of the signal should be reduced by 10×log10(M / (M - 2l max -1)) = 0.77 dB, that is, the peak-to-average ratio increases by 0.77 dB. According to the actual simulation results, the peak-to-average ratio of the improved algorithm increases by 0.71 dB, which is almost the same as the theoretical result. Generally speaking, at the probability of 10 -6 order of magnitude, using the improved algorithm can effectively reduce the peak-to-average ratio by about 10 dB compared with the traditional embedded pilot channel estimation method.

[0115] Figure 3 shows the comparison of the bit error rates between the improved channel estimation algorithm and the traditional algorithm. It can be seen from the figure that the bit error rate is at 10 -4In terms of magnitude, compared with the ideal channel estimation, using the traditional embedded pilot-based channel estimation algorithm, the performance loss is about 2.5 dB. While using the optimized BEM algorithm, the performance has a loss of 1.5 dB compared with the traditional embedded pilot channel algorithm. The bit error rates of the unoptimized BEM algorithm and the three interpolation methods are almost the same, and the performance has a loss of about 2 dB compared with the traditional embedded pilot channel estimation algorithm. Considering the CCDF, although the proposed algorithm has a performance loss compared with the traditional embedded pilot algorithm, it effectively solves the problem of high peak-to-average ratio and is convenient for hardware implementation.

[0116] For B = circ[circ(x1),…,circ(x N )], circ(·) represents a square circulant matrix, and matrix B is a double circulant block matrix. x i is the i-th column vector of matrix X. The formation of matrix B, that is, the operation process of circ(·), is as Figure 4 shown.

[0117] In the second aspect of the embodiments of the present invention, a channel estimation device for OTFS modulation is disclosed. The device includes:

[0118] A memory storing executable program code;

[0119] A processor coupled to the memory;

[0120] The processor calls the executable program code stored in the memory to execute the channel estimation method for OTFS modulation described above.

[0121] In the third aspect of the embodiments of the present invention, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer instructions, which are used to execute the channel estimation method for OTFS modulation when called by a computer.

[0122] In the fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the channel estimation method for OTFS modulation described above.

[0123] The above are only the embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A channel estimation method for OTFS modulation, characterized in that: include: S1, obtain the transmission symbol matrix X, maximum Doppler frequency shift, delay-Doppler grid dimension value, and sampling time interval of OTFS modulation; S2, constructing a basis function coefficient matrix solution model based on the transmit symbol matrix X, the maximum Doppler frequency shift, the delay-Doppler grid dimension value and the sampling time interval; S3, based on the transmission symbol matrix X, solving the basis function coefficient matrix solution model to obtain the time domain channel response matrix of OTFS modulation.

2. The channel estimation method for OTFS modulation according to claim 1, characterized in that: The method of constructing a basis function coefficient matrix solution model based on the transmit symbol matrix X, the maximum Doppler frequency shift, the delay-Doppler grid dimension value, and the sampling time interval includes: S21, calculating a dimension value Q based on the maximum Doppler frequency shift, the delay-Doppler grid dimension value and the sampling time interval; S22, constructing a first information matrix B based on the transmitted symbol matrix X p ; S23, based on the first information matrix B p , a basis function coefficient matrix solution model is constructed; the expression of the basis function coefficient matrix solution model is: Among them, G is the basis function coefficient matrix to be solved, is the partial time domain channel matrix.

3. The channel estimation method for OTFS modulation according to claim 2, characterized in that: The first information matrix B is constructed based on the transmitted symbol matrix X. p ,include: S221, performing pilot extraction processing on the transmission symbol matrix X to obtain a pilot matrix X p ; S222, extract and obtain the pilot matrix X p Column vector of ; S223, repeating square loop operations on all extracted column vectors to obtain a first information matrix B p .

4. The channel estimation method for OTFS modulation according to claim 3, characterized in that: The pilot extraction process is performed on the transmission symbol matrix X to obtain the pilot matrix X p ,include: S2211, using the transmission symbol matrix X, calculate and obtain a reception symbol matrix Z; S2212, using the received symbol matrix Z, calculate and obtain the pilot matrix X p ; The expression of the received symbol matrix Z is: Z p [i,n]=Z[m p +i,n],i=0,…,l max , Where Z[m,n] is the element of the mth row and nth column of the received symbol matrix Z. is the channel feature matrix, W P The matrix dimension size is l max ×N noise matrix, l max represents the ratio of the maximum delay of the channel to the sampling time interval, M and N are matrices H DD The row and column dimensions of the matrix, vec means arranging all column vectors of the matrix into one vector, m p =M / 2,m p is the intermediate position factor, Z p represents the first symbol matrix, Z p [i,n] represents the element in the i-th row and the n-th column of the first symbol matrix, and X[|mk|,|nl|] represents the element in the |mk|-th row and the |nl|-th column of the matrix X.

5. The channel estimation method for OTFS modulation according to claim 4, characterized in that: The expression for the repeated square loop operation process is: in, Denotes the pilot matrix X p is the i-th column vector of , N is the column dimension of the pilot matrix, and circ(·) represents a square circulant matrix operation.

6. The channel estimation method for OTFS modulation according to claim 5, characterized in that: The step of solving the basis function coefficient matrix solution model based on the transmit symbol matrix X to obtain the time domain channel response matrix of OTFS modulation includes: S31, based on the transmission symbol matrix X, construct a first information matrix B p ; S32, performing characteristic vector calculation processing on the channel characteristic matrix and the noise matrix to obtain a first vector and a second vector; S33, performing partial channel response estimation processing on the first vector and the second vector to obtain a partial time domain channel matrix S34, based on partial time domain channel matrix Solve the basis function coefficient matrix G to be solved, and obtain the solution value of the basis function coefficient matrix G; S35, performing channel response calculation on the solved value of the basis function coefficient matrix G to obtain a time domain channel response matrix of OTFS modulation; The expression for calculating the channel response is: H=G·B, Wherein, H is the time domain channel response matrix of OTFS modulation, and B is the basis function matrix.

7. The channel estimation method for OTFS modulation according to claim 6, characterized in that: The expression of the feature vector calculation process is: in, is the channel feature matrix, W p is the noise matrix, and are the first vector and the second vector respectively; The expression of the partial channel response estimation process is: in, For the general The dimension of the rearranged columns is l max ×N matrix, is the third vector, is the Fourier transform matrix, the noise vector for The corresponding row vector, is the N×N dimensional inverse discrete Fourier transform matrix, and the upper right corner H is a conjugate symmetric operation.

8. A channel estimation device for OTFS modulation, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the channel estimation method for OTFS modulation according to any one of claims 1 to 7.

9. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the channel estimation method for OTFS modulation according to any one of claims 1 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the channel estimation method for OTFS modulation according to any one of claims 1 to 7.

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