Data-aided shortwave OFDM sparse channel turbo iterative estimation method

Abstract

The application belongs to the field of OFDM channel estimation, and relates to a data-assisted short-wave OFDM sparse channel Turbo iteration estimation method, which comprises the following steps: S1, receiving a signal transmitted through a channel and processing the signal to obtain a receiving end symbol matrix Y; S2, performing channel estimation and equalization on Y to obtain an estimated channel and an OFDM signal pair, and performing Turbo iteration on the estimated channel and the OFDM signal pair to obtain a symbol variance; S3, selecting a virtual pilot in the signal according to the symbol variance; S4, performing channel estimation and equalization on Y according to the virtual pilot to obtain a current estimated channel and an OFDM signal; S5, performing Turbo iteration on the signal to obtain a current symbol variance; and S6, if a preset maximum iteration number is reached, obtaining a final estimated channel; otherwise, returning to step S3 to select a virtual pilot in the signal according to the symbol variance; and the application selects a virtual pilot in the signal according to the symbol variance, and uses the virtual pilot to perform estimation again, so that the spectral utilization rate is improved and the channel estimation performance is improved.

Description

Technical Field

[0001] This invention belongs to the field of shortwave OFDM channel estimation and relates to a data-assisted shortwave OFDM sparse channel Turbo iterative estimation method. Background Technology

[0002] Currently, the introduction of Orthogonal Frequency Division Multiplexing (OFDM) technology into shortwave communication systems can improve spectrum utilization efficiency and effectively reduce the impact of frequency-selective fading. Shortwave channels exhibit fast time-varying characteristics, and coherent demodulation in shortwave OFDM systems requires channel estimation to obtain channel state information. Therefore, channel estimation is a critical issue in shortwave OFDM systems. Traditional channel estimation methods fail to utilize the sparse characteristics of shortwave frequencies, resulting in low spectrum utilization and poor estimation performance. Summary of the Invention

[0003] To address the aforementioned problems in the prior art, this invention employs a data-assisted shortwave OFDM sparse channel Turbo iterative estimation method, comprising:

[0004] S1. Receive the signal transmitted through the channel, perform preliminary processing on the signal transmitted through the channel, and obtain the receiving end symbol matrix;

[0005] S2. Perform channel estimation and equalization on the symbol matrix at the receiving end to obtain the estimated channel and OFDM signal; perform Turbo iteration on the estimated OFDM signal to obtain the symbol variance;

[0006] S3. Select virtual pilots from the estimated OFDM signal based on the symbol variance;

[0007] S4. Based on the position of the virtual pilot, perform channel estimation and equalization on the receiver symbol matrix to obtain the channel and OFDM signal estimated in the current iteration;

[0008] S5. Perform Turbo iteration on the OFDM signal estimated in the current iteration to obtain the current symbol variance;

[0009] S6. If the preset maximum number of iterations is reached, stop the iteration and obtain the final estimated channel; otherwise, return to step S3 and select a virtual pilot from the currently estimated OFDM signal based on the current symbol variance.

[0010] Beneficial effects:

[0011] 1. In each iteration, this invention selects reliable data symbols based on the CRC check result and the symbol variance of the data symbols generated by the Turbo iteration. These reliable data symbols are used as virtual pilots. Then, the virtual pilots and the initial pilots are used for estimation again, which indirectly increases the number of pilots, thereby improving spectrum utilization and channel estimation performance. 2. This invention utilizes the sparse characteristics of the shortwave channel in the time delay domain and uses compressed sensing for specific channel estimation. To utilize the time correlation of multiple OFDM symbols passing through the channel, the TMSBL algorithm is used for sparse reconstruction, thereby improving channel estimation performance. Attached Figure Description

[0012] Figure 1 A schematic diagram of a data-assisted turbo iterative OFDM system provided in an embodiment of the present invention;

[0013] Figure 2 A flowchart of the data-assisted shortwave OFDM sparse channel Turbo iterative estimation method provided in this embodiment of the invention;

[0014] Figure 3 This is a schematic diagram of a virtual pilot signal provided in an embodiment of the present invention;

[0015] Figure 4 This is a schematic diagram of the CRC encoding structure provided in an embodiment of the present invention;

[0016] Figure 5a This is a schematic diagram illustrating the impact of the number of iterations on the bit error rate in the TDACE algorithm under the HFMQ channel provided in this embodiment of the invention.

[0017] Figure 5b This is a schematic diagram illustrating the impact of the number of iterations on the bit error rate in the TDACE algorithm under an HFMM channel, as provided in an embodiment of the present invention.

[0018] Figure 5c This is a schematic diagram illustrating the impact of the number of iterations on the bit error rate in the TDACE algorithm under the HFMD channel provided in this embodiment of the invention.

[0019] Figure 6 A schematic diagram illustrating the BER of the TDACE algorithm under different channels provided in embodiments of the present invention;

[0020] Figure 7a This is a schematic diagram comparing the BER of different algorithms under the HFMQ channel provided in an embodiment of the present invention;

[0021] Figure 7b This is a schematic diagram comparing the BER of different algorithms under the HFMM channel provided in an embodiment of the present invention;

[0022] Figure 7cThis is a schematic diagram comparing the BER of different algorithms under the HFMD channel provided in an embodiment of the present invention;

[0023] Figure 8 This is a schematic diagram comparing the running times of different algorithms provided in an embodiment of the present invention;

[0024] Figure 9 This is a schematic diagram of the block pilot structure used in DATAC1 provided in an embodiment of the present invention;

[0025] Figure 10 This is a schematic diagram showing the actual comparison of the step loss rate and frame error rate provided in an embodiment of the present invention.

[0026] Figure 11 This is a schematic diagram illustrating the actual BER comparison provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Figure 1 This is a schematic diagram of a data-assisted turbo iterative OFDM system, which records the entire process of the shortwave OFDM system used in the algorithm.

[0029] This invention employs a data-assisted shortwave OFDM sparse channel Turbo iterative estimation method, which has two distinct operational phases: an initial estimation phase and an iterative estimation phase. The initial estimation phase utilizes initial comb pilots for channel estimation; the iterative estimation phase selects reliable data symbols as virtual pilots and, together with the initial pilots, performs channel estimation, such as... Figure 2 The specific steps include:

[0030] S1. Receive the signal transmitted through the channel, perform preliminary processing on the signal transmitted through the channel, and obtain the receiving symbol matrix Y;

[0031] The preliminary processing of the signal after transmission through the channel includes: removing the CP from the signal after transmission through the channel, performing serial-to-parallel conversion on the signal after removing the CP, and performing FFT on the signal after serial-to-parallel conversion to obtain the receiving symbol matrix Y.

[0032] S2. Perform channel estimation and equalization on the receiver symbol matrix Y to obtain the estimated channel and OFDM signal. The estimated OFDM signal is subjected to Turbo iteration to obtain the symbol variance.

[0033] Channel estimation and equalization of the receiver symbol matrix Y includes: performing TMSBL channel estimation based on the receiver symbol matrix to obtain the estimated channel; and equalizing the receiver symbol matrix based on the estimated channel to obtain the estimated OFDM signal. TMSBL stands for Temporal Multiple Sparse Bayesian Learning.

[0034] TMSBL (Temporal Multiple Sparse Bayesian Learning) is an algorithm based on sparse Bayesian learning, specifically designed to handle the multiple measurement vector (MMV) problem. TMSBL channel estimation based on the receiver symbol matrix Y includes:

[0035] S21. Construct the perception matrix Φ and the maximum number of iterations r. max Termination threshold e, noise variance σ 2 Initialize the hyperparameter matrix Γ (0) =I L The iteration number r = 0, and the positive definite time correlation matrix B (0) =I M and hyperparameters Among them, I L I M Let L be the identity matrix of dimensions L and M, where L is the time-domain channel length and M is the number of multi-observation vectors;

[0036] Φ is composed of the first L columns of an N×N dimensional DFT (Discrete Fourier Transform) matrix F and P rows of matrix F corresponding to the original pilot positions; where L is the time-domain channel length, P is the number of original pilots, P < N, L < N, and N is the number of subcarriers; the element in the k-th row and n-th column of the N×N dimensional DFT matrix F is...

[0037] S22, Based on the current hyperparameter matrix Γ (r) Perception matrix Φ and noise variance σ 2 The posterior covariance matrix Σ of the channel coefficients is calculated based on the receiver symbol matrix Y, the current posterior covariance matrix Σ, the sensing matrix Φ, and the noise variance σ. 2 Calculate the posterior mean of the channel coefficients

[0038]

[0039] S23. Using the current posterior mean The posterior covariance matrix Σ and the time correlation matrix B (r) Update hyperparameters Obtain hyperparameters

[0040]

[0041] Where η is a positive value, ensuring the positive definiteness of B. For the estimated value of B, |||| F It is the F-norm.

[0042] S24. If r < r max or Then let r = r + 1 and return to step 2; otherwise, terminate the iteration and obtain the estimated channel.

[0043] S3. Select virtual pilots from the estimated OFDM signal based on the symbol variance;

[0044] S4. Based on the position of the virtual pilot, perform channel estimation and equalization on the receiver symbol matrix to obtain the channel and OFDM signal estimated in the current iteration.

[0045] Channel estimation and equalization of the receiver symbol matrix based on the location of the virtual pilots includes: grouping the receiver symbol matrix Y into pairs according to time order to obtain multiple groups of symbol matrices y. i The selected virtual pilots include the virtual pilots for each symbol matrix. Based on each symbol matrix y i and its corresponding virtual pilot TMSBL channel estimation is performed at the location to obtain the symbol matrix y for each group. i The estimated channel; based on the estimated channel, the corresponding symbol matrix y i Equalization is performed to obtain the OFDM signal estimated in the current iteration; where TMSBL is temporal multiple Bayesian learning.

[0046] According to the symbol matrix y i and its corresponding virtual pilot The TMSBL channel estimation at the location is similar to step S2, except that the sensing matrix Φ changes, and the other procedures are the same.

[0047] The specific steps include:

[0048] S41. Based on the corresponding virtual pilot signal Construct a perception matrix Φ based on the location, and set the maximum number of iterations r. max Termination threshold e and noise variance σ 2 Initialize the hyperparameter matrix Γ (0) =I L Iteration number r = 0, time correlation matrix B (0) =IM and hyperparameters Among them, I L I M Let L be the identity matrix of dimensions L and M, where L is the time-domain channel length and M is the number of multi-observation vectors;

[0049] Φ is formed by the first L columns of the N×N dimensional DFT (Discrete Fourier Transform) matrix F, and the corresponding original pilot positions and virtual pilots in matrix F. The position is composed of row P1, that is in, FL Let L be the first L columns of matrix F. FP Let P be the row in matrix F corresponding to the position of the original pilot. The corresponding virtual pilot in matrix F Position P v Okay, P1 = P + P v P is the number of original pilots. v This represents the number of virtual pilots.

[0050] S42, based on the current hyperparameter matrix Γ (r) Perception matrix Φ and noise variance σ 2 Calculate the posterior covariance matrix Σ of the channel coefficients, based on the current symbol matrix y. i The posterior covariance matrix Σ, the perception matrix Φ, and the noise variance σ 2 Calculate the posterior mean of the channel coefficients

[0051] S43. Using the current posterior mean The posterior covariance matrix Σ and the time correlation matrix B (r) Update hyperparameters Obtain hyperparameters

[0052] S44. If r < r max or Then let r = r + 1 and return to step S42; otherwise, terminate the iteration and obtain the symbol matrix y. i Estimated channel

[0053] S5. Estimating the OFDM signal in the current iteration. Perform Turbo iterations to obtain the current signed variance. Where t is the number of iterations;

[0054] Turbo iteration on the OFDM signal estimated in the current iteration includes: symbol demapping the OFDM signal estimated in the current iteration to obtain the symbol demapping signal and its log-likelihood ratio (LLR(c)). n); the symbol demapped signal and its log-likelihood ratio LLR(c n The signal is de-interleaved to obtain the de-interleaved signal and its likelihood ratio information LLR(d). n ); for the deinterleaved signal and its likelihood ratio information LLR(d n Decode the signal to obtain the decoded signal and its likelihood ratio information LLR′(d). n ); for the decoded signal and its likelihood ratio information LLR′(d n The signals are interleaved to obtain the interleaved signal and its likelihood ratio information LLR′(c n ); for the interleaved signal and its likelihood ratio information LLR′(c n Perform symbol mapping to obtain the estimated symbol variance of the OFDM signal. Among them, c n d represents the bits corresponding to the constellation points of the subcarrier n of the symbol-demapped signal. n The bits corresponding to the constellation points of the subcarrier n of the deinterleaved signal.

[0055] Symbol demapping of the OFDM signal estimated in the current iteration includes:

[0056] The estimated OFDM symbols Mapped to its corresponding Log-Likelihood Ratio (LLR) for use in the maximum a posteriori decoder, due to the estimated posterior probability of OFDM symbols. Approximate P(d) n,j |y), then the log-likelihood ratio LLR(c n ) is defined as:

[0057]

[0058] Where j′=1,2 and j=1,2 represent the indices for the 0 and 1 bit cases, respectively, d n,j This represents the bits corresponding to the constellation points of subcarrier n. express The posterior probability, p(d) n ) represents the probability that the bit corresponding to the constellation point of subcarrier n is 1 or 0. The first term of equation (1) is defined as the external LLR, i.e., L e (d n,j And the second term is defined as the prior LLR, i.e., L f (d n,j It is worth noting that L e (d n,j ) and L f (d n,j Strictly independent.

[0059] Similarly, the likelihood ratio information LLR′(c n This includes the external likelihood ratio L′ e (d n,j ) and prior likelihood ratio L′ f (d n,j ); for the interleaved signal and its likelihood ratio information LLR′(c n Symbol mapping includes:

[0060]

[0061] in, For the OFDM signal estimated in the current iteration, sgn() is the sign function, tanh() is the hyperbolic tangent function, and s k Let L′ be the symbol of the kth constellation point. e (d n,j ) is LLR′(c n The external likelihood ratio of L′ is 0. f (d n,j ) is LLR′(c n The prior likelihood ratio of ), where i represents the imaginary unit, and P() represents the probability.

[0062] S6. If the maximum number of iterations T (T=3) is reached, stop the iteration and obtain the final estimated channel; otherwise, return to step S3 and calculate the current symbol variance. Select a virtual pilot in the OFDM signal estimated in the current iteration.

[0063] Virtual pilot structure such as Figure 3 As shown, selecting virtual pilots in the OFDM signal estimated in the current iteration based on the current symbol variance includes:

[0064] S61. Estimate the OFDM signal in the current iteration in chronological order. The OFDM symbols in the data are grouped in pairs to obtain multiple groups of OFDM symbols. Where i is the index of the group number;

[0065] S62, For each group of OFDM symbols Perform CRC encoding to obtain each group of OFDM symbols. Two sets of cyclic redundancy check codes Where j is the index of the cyclic redundancy check code;

[0066] Figure 4 The diagram shows the CRC encoding structure. Each OFDM symbol group undergoes two CRC encodings to obtain the Cyclic Redundancy Check (CRC) code. and Each CRC group is composed of bits from the corresponding data symbol. When using the TMSBL algorithm, two OFDM symbols are processed at a time, i.e., one group of OFDM symbols. To enable channel estimation using virtual pilots in the MMV model, the selection of virtual pilots must include bits corresponding to all data symbols on the same subcarrier. Therefore, each CRC group selects all data symbols on several subcarriers. To ensure performance during compressed reconstruction, all pilot positions should be distributed as randomly as possible. Therefore, the two CRC groups do not select several consecutive subcarriers, but rather select bits from the OFDM symbols. Select the data symbols on the intersecting subcarriers as a set of CRCs. i,1 The remaining data symbols are used as another set of CRC. i,2 .

[0067] S63, By analyzing each group of OFDM symbols Two sets of cyclic redundancy check codes Perform CRC check to obtain each group of OFDM symbols virtual pilot

[0068] By analyzing OFDM symbols Two sets of cyclic redundancy check codes Perform verification to obtain OFDM symbols The virtual pilots include: two sets of cyclic redundancy check codes. Perform CRC checks separately. If at least one set of cyclic redundancy check codes is found... If the verification passes, the OFDM symbol will be... Cyclic Redundancy Check (CRC) code that has passed verification The corresponding data symbols are used as OFDM symbols. virtual pilot Otherwise, use the sign variance of the soft information data generated by Turbo iterative feedback. To determine the reliability of data symbols, The closer a symbol is to 0, the closer it is to the true value, and therefore the more reliable it is. Specifically, when selecting OFDM symbols... Signed variance The smallest P v Data symbols on each subcarrier are used as OFDM symbols. virtual pilot Among them, P v P represents the number of virtual pilots. v =15.

[0069] Select OFDM symbol Signed variance The smallest P vData symbols on each subcarrier are used as OFDM symbols. Virtual pilots include: OFDM symbols The symbol variances of the two data symbols on each subcarrier are added together, and the P symbol with the smallest symbol variance is selected. v Group, the smallest P v The data symbols corresponding to the group act as virtual pilots.

[0070] In one embodiment, the parameters used in the simulation are shown in Table 1.

[0071] Table 1 System Simulation Parameters

[0072]

[0073]

[0074] The shortwave channel used in the simulation refers to the mid-latitude shortwave channel recommended by ITU-RF.1487, and the parameters are shown in Table 2. This channel is divided into three conditions: quiet condition (HFMQ), moderate condition (HFMM), and interference condition (HFMD).

[0075] Table 2. Mid-latitude shortwave channel parameters

[0076]

[0077]

[0078] The simulation metric uses the bit error rate (BER), which is the ratio of the number of decoded erroneous bits to the total number of transmitted bits. First, the performance variation of the proposed method under different iteration counts is investigated. Figures 5(a), 5(b), and 5(c) show the changes in channel estimation performance indirectly represented by the BER under different iteration counts for HFMQ, HFMM, and HFMD channels, respectively. It can be seen that regardless of the channel, the second iteration provides the greatest relative performance improvement. After three iterations, the performance improvement in subsequent iterations is relatively small. To save simulation time, three iterations are used for subsequent iterations.

[0079] Figure 6 This paper compares the performance of the proposed algorithm under three channel conditions: HFMQ, HFMM, and HFMD. Figure 6 It can be seen that the performance is best under the HFMQ channel condition, followed by the HFMM channel, and the performance is worst under the HFMD channel. This is consistent with the channel parameter results shown in Table 2. Since the HFMQ channel has smaller delay and frequency spread, it is the best channel condition, and TDACE has the best performance.

[0080] To verify the effectiveness of the method (TDACE) of this invention, a comparison was made with traditional algorithms. This invention was compared with Least Square (LS), Stagewise Weak Orthogonal Matching Pursuit (SWOMP), and Orthogonal Matching Pursuit (OMP) algorithms. SWOMP and OMP are greedy reconstruction algorithms in compressed sensing. All algorithm simulations used the same encoding method and number of pilots. Figures 7(a), 7(b), and 7(c) show the performance comparison of different algorithms under HFMQ, HFMM, and HFMD channels, respectively. It can be concluded that regardless of the algorithm, the best performance is achieved under the HFMQ channel, while the worst performance is achieved under the HFMD channel, due to the best HFMQ channel conditions and the worst HFMD channel conditions. The LS algorithm, due to its limited number of pilots, cannot effectively cope with the fast time-varying characteristics of shortwave frequencies, resulting in poor performance. Compared to the OMP algorithm, the SWOMP algorithm is slightly inferior in performance, but the OMP algorithm requires prior information about the channel sparsity, while the SWOMP algorithm does not. The method proposed in this invention is optimal, outperforming the OMP algorithm by 2dB. Furthermore, since the method proposed in this invention uses the TMSBL algorithm, it also does not require prior knowledge of the channel sparsity.

[0081] To compare the computational complexity of the method proposed in this invention with other algorithms, the single-run time of each algorithm under different signal-to-noise ratios was statistically analyzed. The software used was MATLAB R2020b, and the computer CPU was an Intel 12th generation Core i7-12700 processor with a clock speed of 2.10 GHz. Figure 8 It is known that OMP and SWOMP, as greedy reconstruction algorithms, have relatively low computational complexity, only slightly higher than the LS algorithm. The SWOMP algorithm, due to its need to solve for sparse positions, has higher computational complexity than the OMP algorithm. The method proposed in this invention, due to its need for iteration and soft information solving, has higher computational complexity than other algorithms, with a single run time approximately four times that of the LS algorithm. Furthermore, the running time of the method proposed in this invention varies with the signal-to-noise ratio (SNR), as changes in SNR affect CRC transmission, leading to different numbers of virtual pilots selected and the calculation of variance for soft data symbols.

[0082] In one embodiment, a real radio station was used for communication and analysis. The communication link was selected with Xinjiang Luntai (84°E, 41°N) transmitting and Finland (24°E, 68°N) receiving, with a communication distance of approximately 5000 kilometers. The test period was from 14:00 to 21:00 on December 30, 2024, a total of 8 hours, with measurements taken every hour. In shortwave communication, the available frequencies vary depending on the path and time, therefore frequency prediction is required. The Voice of America Coverage Analysis Program (VOACAP) software was used for frequency prediction to select a usable frequency band. The frequencies tested every hour are shown in Table 3.

[0083] Table 3 Test Frequency Table

[0084]

[0085] In the actual test, the method was compared with the commonly used DATAC1 mode in engineering. In this mode, the channel estimation algorithm is LS. Unlike the comb-shaped pilot method proposed in this invention, the DATAC1 mode uses block-shaped pilots, meaning the pilots are placed on all subcarriers of an OFDM symbol. The pilot time interval is 5, meaning that one out of every five OFDM symbols has pilots placed on it, and the remaining four OFDM symbols have data symbols placed on it. See details... Figure 9 As shown in Table 1, during hourly individual tests, 10 frames of data were sent. Each frame in TDACE contains 136 OFDM symbols, and each frame in DATAC1 contains 135 OFDM symbols. The TDACE algorithm parameters are shown in Table 1, and other identical measured parameters are shown in Table 4.

[0086] Table 4 shows the measured parameters of the system.

[0087]

[0088] The main measured indicators are out-of-synchronization rate, frame error rate, and bit error rate. The out-of-synchronization rate is the ratio of the number of frames that are not synchronized to the total number of frames sent, and the frame error rate is the ratio of the number of frames that are decoded incorrectly to the total number of frames sent. Figure 10 The graph shows the test results for the out-of-synchronization rate and frame error rate. As can be seen, due to the use of the same synchronization algorithm, the out-of-synchronization rate of the method proposed in this invention is basically the same as that of DATAC1. The frame error rate of the method proposed in this invention is lower than that of DATAC1 at 14:00, 18:00, and 21:00. At other time points, the frame error rates of both are the same, indicating that the performance of the method proposed in this invention is superior to that of DATAC1. The higher out-of-synchronization rate and frame error rate at 14:00 is because this is sunrise time, when ionospheric ion activity is more intense and the channel changes rapidly.

[0089] Figure 11 The graph shows the actual BER (Bit Error Rate) test results. It can be seen that the best performance was achieved at 19:00, with both DATAC1 and the proposed method showing a BER of 0. The proposed method employed three iterations, and it can be seen that the performance after three iterations is superior to DATAC1, demonstrating the effectiveness of the proposed method.

[0090] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data-assisted shortwave OFDM sparse channel Turbo iterative estimation method, characterized in that, include: S1. Receive the signal transmitted through the channel, perform preliminary processing on the signal transmitted through the channel, and obtain the receiving end symbol matrix; S2. Perform channel estimation and equalization on the symbol matrix at the receiving end to obtain the estimated channel and OFDM signal; perform Turbo iteration on the estimated OFDM signal to obtain the symbol variance; S3. Select virtual pilots from the estimated OFDM signal based on the symbol variance; S4. Based on the position of the virtual pilot, perform channel estimation and equalization on the receiver symbol matrix to obtain the channel and OFDM signal estimated in the current iteration; S5. Perform Turbo iteration on the OFDM signal estimated in the current iteration to obtain the current symbol variance; S6. If the preset maximum number of iterations is reached, stop the iteration and obtain the final estimated channel. Otherwise, return to step S3 to select a virtual pilot in the currently estimated OFDM signal based on the current symbol variance; The selection of virtual pilots in the OFDM signal estimated in the current iteration includes: S61. Estimate the OFDM signal in the current iteration in chronological order. The OFDM symbols in the data are grouped in pairs to obtain multiple groups of OFDM symbols. Where t is the number of iterations and i is the index of the group; S62, For each group of OFDM symbols Perform CRC encoding to obtain each group of OFDM symbols. Two sets of cyclic redundancy check codes Where j is the index of the cyclic redundancy check code; S63, By analyzing each group of OFDM symbols Two sets of cyclic redundancy check codes Verification is performed to obtain each group of OFDM symbols. virtual pilot By analyzing OFDM symbols Two sets of cyclic redundancy check codes Perform verification to obtain OFDM symbols The virtual pilots include: two sets of cyclic redundancy check codes. Perform verification separately; if at least one set of cyclic redundancy check codes exists... If the verification passes, the OFDM symbol will be... Cyclic Redundancy Check (CRC) code that has passed verification The corresponding data symbols are used as OFDM symbols. virtual pilot Otherwise, select OFDM symbol. P with the smallest sign variance v Data symbols on each subcarrier are used as OFDM symbols. virtual pilot Among them, P v The preset number of virtual pilots.

2. The data-assisted shortwave OFDM sparse channel Turbo iterative estimation method according to claim 1, characterized in that, The preliminary processing of the signal after transmission through the channel includes: removing the cyclic prefix (CP) from the signal after transmission through the channel, performing serial-to-parallel conversion on the signal after removing the CP, and performing a fast Fourier transform on the converted signal to obtain the symbol matrix at the receiving end.

3. The data-assisted shortwave OFDM sparse channel Turbo iterative estimation method according to claim 1, characterized in that, For each group of OFDM symbols Performing CRC encoding includes: in OFDM symbols Select the data symbols on the intersecting subcarriers as a set of CRCs. i,1 The remaining data symbols are used as another set of CRC. i,2 .

4. The data-assisted shortwave OFDM sparse channel Turbo iterative estimation method according to claim 1, characterized in that, Channel estimation and equalization of the receiver symbol matrix based on the location of the virtual pilots includes: grouping the receiver symbol matrix pairwise according to time order to obtain multiple groups of symbol matrices y. i According to each group of symbol matrices y i and its corresponding virtual pilot TMSBL channel estimation is performed at the location to obtain the symbol matrix y for each group. i The estimated channel; based on the estimated channel, the corresponding symbol matrix y i Equalization is performed to obtain the OFDM signal estimated in the current iteration; where TMSBL is temporal multi-sparse Bayesian learning.

5. The data-assisted shortwave OFDM sparse channel Turbo iterative estimation method according to claim 4, characterized in that, According to the symbol matrix y i and its corresponding virtual pilot Location-based TMSBL channel estimation includes: S41. According to the symbol matrix y i Corresponding virtual pilot Construct a perception matrix Φ based on the location, and set the maximum number of iterations r. max Termination threshold e and noise variance σ 2 Initialize the hyperparameter matrix Γ (0) =I L Iteration number r = 0, time correlation matrix B (0) =I M and hyperparameters Among them, I L I M Let L be the identity matrix of dimensions L and M, where L is the time-domain channel length and M is the number of multi-observation vectors; S42, based on the current hyperparameter matrix Γ (r) Perception matrix Φ and noise variance σ 2 Calculate the posterior covariance matrix Σ of the channel coefficients, based on the current symbol matrix y. i The posterior covariance matrix Σ, the perception matrix Φ, and the noise variance σ 2 Calculate the posterior mean of the channel coefficients S43. Using the current posterior mean The posterior covariance matrix Σ and the time correlation matrix B (r) Update hyperparameters Obtain hyperparameters S44, if r <r max or Then let r = r + 1 and return to step S42; otherwise, terminate the iteration and obtain the symbol matrix y. i Estimated channel 6. The data-assisted shortwave OFDM sparse channel Turbo iterative estimation method according to claim 1, characterized in that, Turbo iteration on the OFDM signal estimated in the current iteration includes: symbol demapping the OFDM signal estimated in the current iteration to obtain the symbol demapping signal and its log-likelihood ratio information LLR(c n ); the signal after symbol demapping and its log-likelihood ratio information LLR(c n The signal is de-interleaved to obtain the de-interleaved signal and its likelihood ratio information LLR(d). n ); for the deinterleaved signal and its likelihood ratio information LLR(d n Decode the signal to obtain the decoded signal and its likelihood ratio information (LLR). ′ (d n ); for the decoded signal and its likelihood ratio information LLR′(d n The signals are interleaved to obtain the interleaved signal and its likelihood ratio information LLR′(c n ); for the interleaved signal and its likelihood ratio information LLR′(c n Perform symbol mapping to obtain the estimated symbol variance of the OFDM signal. Among them, c n d represents the bits corresponding to the constellation points of the subcarrier n of the symbol-demapped signal. n The number of iterations is the number of bits corresponding to the constellation points of the subcarrier n of the deinterleaved signal.

7. The data-assisted shortwave OFDM sparse channel Turbo iterative estimation method according to claim 6, characterized in that, The interleaved signal and its likelihood ratio information LLR′(c n Symbol mapping includes: Based on the likelihood ratio information LLR′(c n Calculate the symbol mean of the estimated OFDM signal. According to the sign mean Calculate the sign variance in, For the OFDM signal estimated in the current iteration, s k The symbol for the k-th constellation point.

8. The data-assisted shortwave OFDM sparse channel Turbo iterative estimation method according to claim 7, characterized in that, Likelihood ratio information LLR′(c n This includes the external likelihood ratio L′ e (d n,j ) and prior likelihood ratio L′ f (d n,j ); Calculate the sign mean include: Where j = 1, 2 represents the index of bits 0 and 1, respectively, and d n,j Let be the bit corresponding to the constellation point of subcarrier n, tanh() be the hyperbolic tangent function, i be the imaginary unit, and P() be the probability.

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