A short wave communication equalization method and system based on decision feedback iteration

Abstract

This invention discloses a shortwave communication equalization method and system based on decision feedback iteration. Specifically, the transmitting end encodes, interleaves, and maps data information to generate multiple unknown data blocks. Known data sequences are interleaved and inserted between the unknown data blocks to form a data segment sequence, which is then transmitted to the receiving end via the channel. The receiving end extracts known data segments and sends them to a channel estimation module to estimate the channel impulse response. These segments, along with the extracted unknown data segments, are simultaneously sent to a first equalizer for segment-by-segment equalization, followed by LLR calculation, deinterleaving, and decoding. The decoding result is then determined to satisfy the iteration termination condition; if so, the iteration ends. Otherwise, the decoding result is encoded, interleaved, and mapped. The mapped result is divided into mapped segments, which are used as the initial values ​​for iteration and input to a second equalizer for segment-by-segment equalization. The LLR calculation, deinterleaving, and decoding process is then repeated until the iteration ends. This invention improves shortwave communication reception performance, has low computational complexity, and exhibits good numerical computation stability.

Description

Technical Field

[0001] This invention relates to the field of shortwave communication technology, and in particular to a shortwave communication equalization method and system based on decision feedback iteration. Background Technology

[0002] Shortwave channels are characterized by multipath and time-varying features, which easily lead to inter-symbol crosstalk and changes in signal envelope over time, severely impacting communication quality. Equalization techniques can reduce the impact of channel multipath and time-varying characteristics on the signal, thereby improving the quality of shortwave communication. With the increasing demand for broadband high-speed data transmission, equalizers are required to better overcome channel influences, and equalization algorithms must also have low computational complexity.

[0003] Appendix D of the US military standard MIL-STD-188-110D defines shortwave broadband waveforms and specifies performance indicators. Existing literature proposes various equalization techniques for broadband high-speed waveforms, such as Turbo equalization and iterative bidirectional Kalman-DFE equalization. The basic principle of Turbo equalization is similar to that of a Turbo decoder, involving repeated interaction of soft information between equalization and decoding, followed by hard decision output after iteration. While performance improves with increasing iteration count, it does not meet the performance requirements, and the algorithm is complex. Iterative bidirectional Kalman-DFE equalization was studied for high-speed waveforms with a 3kHz bandwidth. By selecting the optimal equalization result that eliminates crosstalk between preceding and following symbols and employing iterative techniques, performance is significantly improved, but it still does not meet the performance requirements. Summary of the Invention

[0004] The purpose of this invention is to provide a shortwave communication equalization method and system based on decision feedback iteration, which has stable receiving performance, simple algorithm, low computational load, and good numerical calculation stability.

[0005] The technical solution to achieve the objective of this invention is: a shortwave communication equalization method based on decision feedback iteration, comprising the following steps:

[0006] Step 1: After encoding, interleaving, and mapping the data information, the transmitting end generates multiple unknown data blocks. Known data sequences are then inserted between the unknown data blocks at intervals to form a data segment sequence, which is then sent to the receiving end through the channel.

[0007] Step 2: The receiving end extracts known data segments from the received signal segment by segment and sends them to the channel estimation module to estimate the channel impulse response corresponding to the segment and send it to the first equalizer; it also extracts unknown data segments segment by segment and sends them to the first equalizer for segment-by-segment equalization.

[0008] Step 3: Save the channel estimate, unknown data segment, and equalization result of the first equalizer input segment by segment;

[0009] Step 4: The demapping module performs LLR demapping calculation on the equalization result, and deinterleaves and decodes the calculated LLR value. After decoding, the decoding result is sent to the iteration end determination module.

[0010] Step 5: The iteration end determination module determines whether the decoding meets the iteration end condition according to the set criteria. If it does, the iteration ends and the decoding result is output; otherwise, the decoding result is sent to the encoding module of the iteration receiving unit.

[0011] Step 6: The decoding result is encoded, interleaved, and mapped in the iterative receiving unit. The mapping result is divided into mapping segments. The mapping segments are used as the initial values ​​for the iteration. The channel estimate value, unknown data segment, and mapping segment of the first equalizer input are input into the second equalizer segment by segment. Then, proceed to step 4 to perform the next LLR calculation and decoding process until the iteration end condition is met.

[0012] Furthermore, the waveform frame of the data information consists of three segments: a TLC segment, a synchronization segment, and a data segment. The TLC segment is used for transmitting-end level adjustment and receiving-end AGC; the synchronization segment is used for receiving-end synchronization search, frequency offset compensation, and waveform number transmission; and the data segment carries the data information to be transmitted.

[0013] The data segment is composed of alternating known data segments and unknown data segments. The known data segments are used for channel state estimation and tracking. The lengths of the known and unknown data segments vary with the bandwidth and rate, and the modulation scheme of the unknown data varies with the rate.

[0014] Furthermore, after encoding, interleaving, and mapping the data information, the transmitting end in step 1 generates multiple unknown data blocks. Known data sequences are then inserted at intervals between the unknown data blocks to form a data segment sequence, which is transmitted to the receiving end through the channel, as detailed below:

[0015] Step 1.1: The sending end encodes, interleaves, and maps the data information to generate an unknown data sequence. Based on waveform parameters, the data is divided into multiple unknown data blocks of length N. ;

[0016] Step 1.2: Insert the known data sequence at intervals between the unknown data blocks to form a data segment sequence;

[0017] Step 1.3: Frame the TLC segment sequence, synchronization segment sequence, and data segment sequence to form the transmission sequence. via channel Send to the receiving end.

[0018] Furthermore, step 1.3 involves framing the TLC segment sequence, synchronization segment sequence, and data segment sequence to form the transmission sequence. via channel The data is sent to the receiving end as follows:

[0019] For an unknown data block , Through time-varying channels , The channel introduces additive white Gaussian noise. , Received unknown data block , Then, it can be expressed as a system of linear equations:

[0020] , , , (1)

[0021] Will , , Written in vector form:

[0022]

[0023]

[0024]

[0025] Will Written The matrix form is as follows:

[0026]

[0027] Then the system of equations (1) can be written as matrix equations:

[0028] (2)

[0029] Therefore, the problem of balancing unknown data blocks is to find the optimal solution to the matrix equation. In noisy conditions, reduce error The smallest sum of squares that is Therefore, the least squares algorithm is used to obtain the best estimate. Ignore noise vector , Constructed from the channel estimate, the matrix equation of equation (2) simplifies to: , The least squares solution is:

[0030] (3)

[0031] in express The conjugate transpose of . , express The inverse matrix of , where R is the autocorrelation matrix of the channel, which is a Hermitian matrix, let . The least squares solution can be further expressed as: .

[0032] Further, in step 2, the receiving end extracts known data segments from the received signal segment by segment and sends them to the channel estimation module to estimate the channel impulse response corresponding to the segment, and then sends it to the first equalizer; it also extracts unknown data segments segment by segment and sends them to the first equalizer for segment-by-segment equalization, as follows:

[0033] Step 2.1: Use the Jacobi algorithm to solve the matrix equation, and modify the algorithm to solve the matrix equation. Rewritten as follows:

[0034] (4)

[0035] in It is a diagonal matrix; , , , , , ; It is by The lower triangular matrix formed by the elements on the lower left of the main diagonal; Depend on The upper triangular matrix formed by the elements at the top right of the main diagonal is then... The inverse of the expression is directly achieved by finding the reciprocal of the main diagonal element;

[0036] Step 2.2, from To obtain an estimate of X Initial estimates are obtained by hard-decision on an element-by-element basis. ,in These are the values ​​on the corresponding modulation scheme constellation diagram;

[0037] Step 2.3: Iterative solution, using initial values... Substituting into the following formula, we obtain the new symbol estimate. :

[0038] , (5)

[0039] in This represents the maximum number of iterations.

[0040] right Perform element-by-element judgment to obtain a new judgment value. Used as the initial value for the next iteration;

[0041] Step 2.4: Compare the decision values ​​of two adjacent iterations. The iteration continues until the percentage of distinct decision value elements is less than the threshold value Th, or the number of iterations reaches the set maximum number M, at which point the iteration terminates; otherwise, it returns to step 2.3 to start a new round of iteration.

[0042] Step 2.5: Set the final estimated value. The output of the first equalizer is sent to the subsequent demapping module.

[0043] Furthermore, in step 5, the iteration end determination module determines whether the decoding result meets the iteration end condition based on the set criteria, as follows:

[0044] The iteration termination criterion is as follows: the iteration does not end upon receiving the first decoding result. Starting from the second decoding result, the iteration termination criterion is determined by comparing the percentage difference between the results of two adjacent decodings with a threshold and by combining this with the set maximum number of iterations. When the iteration reaches the maximum number of iterations or the percentage difference between the results of two adjacent decodings is less than the set threshold, the iteration process exits, the decoding result is output, and the reception of the transmitted data is completed.

[0045] Furthermore, the initial value of the second equalizer iteration in step 6 The symbols derived from the decoded information are re-encoded, interleaved, and mapped. The maximum number of iterations is M=1. The output value of the second equalizer is... .

[0046] A shortwave communication equalization system based on decision feedback iteration is disclosed. This system implements the aforementioned shortwave communication equalization method based on decision feedback iteration. The system includes a transmitting module, a receiving module, a storage module, a demapping module, an iteration result determination module, and an iteration receiving unit, wherein:

[0047] The transmitting module encodes, interleaves, and maps the data information to generate multiple unknown data blocks. Known data sequences are then inserted between the unknown data blocks at intervals to form a data segment sequence, which is then transmitted to the receiving end through the channel.

[0048] The receiving module extracts known data segments from the received signal segment by segment and sends them to the channel estimation module to estimate the channel impulse response corresponding to the segment and send it to the first equalizer; it also extracts unknown data segments segment by segment and sends them to the first equalizer for segment-by-segment equalization.

[0049] The storage module saves the channel estimate, unknown data segments, and equalization results of the first equalizer input segment by segment.

[0050] The demapping module performs LLR demapping calculation on the equalization result, and deinterleaves and decodes the calculated LLR value. After decoding, the decoding result is output to the iteration end determination module.

[0051] The iteration end determination module determines whether the decoding meets the iteration end condition according to the set criteria. If it does, the iteration ends and the decoding result is output; otherwise, the decoding result is sent to the iteration receiving unit.

[0052] In the iterative receiving unit, the decoding result is encoded, interleaved, and mapped. The mapping result is divided into mapping segments, which are used as the initial values ​​for iteration. The channel estimate value, unknown data segment, and mapping segment of the first equalizer input are input to the second equalizer segment by segment. The demapping module performs the next calculation of LLR and decoding process until the iteration ends.

[0053] A mobile terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the aforementioned shortwave communication equalization method based on decision feedback iteration.

[0054] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the aforementioned shortwave communication equalization method based on decision feedback iteration.

[0055] Compared with the prior art, the present invention has the following significant advantages: (1) By eliminating crosstalk between the preceding and following symbols at the same time and combining iterative technology, shortwave communication equalization is achieved, which solves the problem of shortwave broadband waveform being affected by multipath and channel fading, and improves the shortwave communication receiving performance; (2) No matrix inversion is required, the amount of computation is low, and the numerical calculation stability is good. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating a shortwave communication equalization method based on decision feedback iteration according to the present invention.

[0057] Figure 2 This is a schematic diagram of the structure of the data information waveform frame in this invention.

[0058] Figure 3This is a graph showing the bit error rate under the AWGN channel in an embodiment of the present invention.

[0059] Figure 4 This is a graph showing the bit error rate under the Poor channel in an embodiment of the present invention. Detailed Implementation

[0060] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0061] To address the issue of unstable reception performance of shortwave broadband waveforms due to multipath and channel fading, and the complexity and poor performance of most shortwave broadband high-speed waveform equalization techniques, this invention proposes a decision feedback iterative equalization technique for single-carrier continuous frequency band data waveforms in Appendix D of MIL-STD-188-110D. By simultaneously eliminating crosstalk between preceding and following symbols and combining iterative techniques, this technique achieves performance superior to the specifications stipulated in Appendix D.

[0062] MIL-STD-188-110D Appendix D defines a single-carrier continuous-band data waveform group, with bandwidth increasing in 3kHz increments from 3kHz to 24kHz. The primary purpose of this waveform design is to provide high-speed, long-distance communication over shortwave bands under extreme conditions. The maximum communication rate reaches 120kbps. Modulation methods include Walsh, PSK, and QAM; four interleaving lengths are defined for each rate level; error correction codes use (2,1,7) or (2,1,9) convolutional codes. Figure 2 As shown, the waveform frame of the data information consists of three segments: TLC segment, synchronization segment, and data segment. The TLC segment is used for level adjustment at the transmitting end and AGC at the receiving end; the synchronization segment is used for synchronization search, frequency offset compensation, and waveform number transmission at the receiving end; and the data segment carries the data information to be transmitted.

[0063] The data segment is composed of alternating known data segments and unknown data segments. The known data segments are used for channel state estimation and tracking. The lengths of the known and unknown data segments vary with the bandwidth and rate, and the modulation scheme of the unknown data varies with the rate.

[0064] Combination Figure 1 This invention discloses a shortwave communication equalization method based on decision feedback iteration, comprising the following steps:

[0065] Step 1: After encoding, interleaving, and mapping the data information, the transmitting end generates multiple unknown data blocks. Known data sequences are then inserted between the unknown data blocks at intervals to form a data segment sequence, which is then sent to the receiving end through the channel.

[0066] Step 2: The receiver extracts known data segments from the received signal and sends them to the channel estimation module to estimate the channel impulse response corresponding to the segment and sends it to equalizer 1; it also extracts unknown data segments and sends them to equalizer 1 for segment-by-segment equalization.

[0067] Step 3: Save the channel estimate, unknown data segment, and equalization result of equalizer 1 segment by segment;

[0068] Step 4: The LLR calculation module performs demapping calculation on the equalization result, and deinterleaves and decodes the calculated LLR value. After decoding, the decoding result is sent to the iteration end determination module.

[0069] Step 5: The iteration end determination module determines whether the decoding result meets the iteration end condition according to the set criteria. If it meets the condition, the iteration ends and the decoding result is output; otherwise, the decoding result is sent to the encoding module of the iteration receiving unit.

[0070] Step 6: The decoding result is encoded, interleaved, and mapped in the iterative receiving unit. The mapping result is divided into mapping segments, which are used as the initial values ​​for the iteration. The channel estimate value and unknown data segments and mapping segments stored in equalizer 1 are input into equalizer 2 segment by segment. Then, proceed to step 4 to perform the next LLR calculation and decoding process until the iteration end condition is met.

[0071] As a specific example, in step 1, the transmitting end encodes, interleaves, and maps the data information to generate multiple unknown data blocks. Known data sequences are then inserted at intervals between the unknown data blocks to form a data segment sequence, which is then transmitted to the receiving end through the channel. Specifically, as follows:

[0072] Step 1.1: The sending end encodes, interleaves, and maps the data information to generate an unknown data sequence. Based on waveform parameters, the data is divided into multiple unknown data blocks of length N. ;

[0073] Step 1.2: Insert the known data sequence at intervals between the unknown data blocks to form a data segment sequence;

[0074] Step 1.3: Frame the TLC segment sequence, synchronization segment sequence, and data segment sequence to form the transmission sequence. via channel Send to the receiving end.

[0075] As a specific example, step 1.3 involves framing the TLC segment sequence, synchronization segment sequence, and data segment sequence to form a transmission sequence. via channel The data is sent to the receiving end as follows:

[0076] For an unknown data block , Through time-varying channels , The channel introduces additive white Gaussian noise. , Received unknown data block , The relationship between them can be represented by a system of linear equations:

[0077] , , , (1)

[0078] Will , , Written in vector form:

[0079]

[0080]

[0081]

[0082] Will Written The matrix form is as follows:

[0083]

[0084] Then the system of equations (1) can be written as matrix equations:

[0085] (2)

[0086] Therefore, the problem of balancing unknown data blocks is to find the optimal solution to the matrix equation. In noisy conditions, reduce error The smallest sum of squares that is Therefore, the least squares algorithm is used to obtain the best estimate. Ignore noise vector , Constructed from the channel estimate, the matrix equation of equation (2) simplifies to: , The least squares solution is:

[0087] (3)

[0088] in express The conjugate transpose of . , express The inverse matrix of , where R is the autocorrelation matrix of the channel, which is a Hermitian matrix, let . The least squares solution can then be further expressed as The solution involves matrix inversion, which requires a large amount of computation. Therefore, an iterative algorithm with low computational complexity and stable numerical computation is selected.

[0089] As a specific example, in step 2, the receiving end extracts known data segments from the received signal segment by segment and sends them to the channel estimation module to estimate the channel impulse response corresponding to the segment, and then sends it to equalizer 1; it also extracts unknown data segments segment by segment and sends them to equalizer 1 for segment-by-segment equalization, as follows:

[0090] Step 2.1: Use the Jacobi algorithm to solve the matrix equation, and modify the algorithm to solve the matrix equation. Rewritten as follows:

[0091] (4)

[0092] in It is a diagonal matrix; , , , , , ; It is by The lower triangular matrix formed by the elements on the lower left of the main diagonal; Depend on The upper triangular matrix formed by the elements at the top right of the main diagonal is then... The inverse of the expression is directly achieved by finding the reciprocal of the main diagonal element;

[0093] Step 2.2, from To obtain an estimate of X Initial estimates are obtained by hard-decision on an element-by-element basis. ,in These are the values ​​on the corresponding modulation scheme constellation diagram;

[0094] Step 2.3: Iterative solution, using initial values... Substituting into the following formula, we obtain the new symbol estimate. :

[0095] , (5)

[0096] in This represents the maximum number of iterations.

[0097] right Perform element-by-element judgment to obtain a new judgment value. Used as the initial value for the next iteration;

[0098] Step 2.4: Compare the estimates from two adjacent iterations. The process continues until the percentage of different estimated elements is obtained, until the percentage is less than a certain threshold Th, or the number of iterations reaches the set maximum number M, at which point the iteration terminates; otherwise, the process returns to step 2.3 to start a new round of iteration.

[0099] Step 2.5: Set the final estimated value. As the output of equalizer 1, it is sent to the subsequent demapping module.

[0100] As a specific example, the iteration end determination module in step 5 determines whether the decoding meets the iteration end condition based on the set criteria, as follows:

[0101] The iteration termination criterion is as follows: the iteration does not end upon receiving the first decoding result. Starting from the second decoding result, the iteration termination criterion is determined by comparing the percentage difference between the results of two adjacent decodings with a threshold and by combining this with the set maximum number of iterations. When the iteration reaches the maximum number of iterations or the percentage difference between the results of two adjacent decodings is less than the set threshold, the iteration process exits, the decoding result is output, and the reception of the transmitted data is completed.

[0102] As a specific example, the initial value of the equalizer 2 iteration described in step 6 The symbols derived from the decoded information are re-encoded, interleaved, and mapped. The maximum number of iterations is M=1. The output value of equalizer 2 is... .

[0103] This invention also provides a shortwave communication equalization system based on decision feedback iteration. This system is used to implement the aforementioned shortwave communication equalization method based on decision feedback iteration. The system includes a transmitting module, a receiving module, a storage module, a demapping module, an iteration result determination module, and an iteration receiving unit, wherein:

[0104] The transmitting module encodes, interleaves, and maps the data information to generate multiple unknown data blocks. Known data sequences are then inserted between the unknown data blocks at intervals to form a data segment sequence, which is then transmitted to the receiving end through the channel.

[0105] The receiving module extracts known data segments from the received signal segment by segment and sends them to the channel estimation module to estimate the channel impulse response corresponding to the segment and send it to the first equalizer; it also extracts unknown data segments segment by segment and sends them to the first equalizer for segment-by-segment equalization.

[0106] The storage module saves the channel estimate, unknown data segments, and equalization results of the first equalizer input segment by segment.

[0107] The demapping module performs LLR demapping calculation on the equalization result, and deinterleaves and decodes the calculated LLR value. After decoding, the decoding result is sent to the iteration end determination module.

[0108] The iteration end determination module determines whether the decoding result meets the iteration end condition according to the set criteria. If it does, the iteration ends and the decoding result is output; otherwise, the decoding result is sent to the iteration receiving unit.

[0109] In the iterative receiving unit, the decoding result is encoded, interleaved, and mapped. The mapping result is divided into mapping segments, which are used as the initial values ​​for iteration. The channel estimate value, unknown data segment, and mapping segment of the first equalizer input are input to the second equalizer segment by segment. The demapping module performs the next calculation of LLR and decoding process until the iteration ends.

[0110] The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned shortwave communication equalization method based on decision feedback iteration.

[0111] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps in the aforementioned shortwave communication equalization method based on decision feedback iteration.

[0112] The present invention will be further described in detail below with reference to specific embodiments.

[0113] Example

[0114] This embodiment uses the bandwidth BW=24kHz, waveform number wid=7, long interleaving, (2,1,7) convolutional code from Appendix D to simulate and test the performance of iterative reception. The modulation scheme of this waveform is 8PSK, and the LLR is calculated using an exact algorithm for demapping. The simulation channels selected are the AWGN channel and the mid-latitude perturbation channel of ITU-R.F1487, abbreviated as Poor channel. The channel parameters are: multipath delay 2ms, Doppler spread 1Hz. The performance indicators of this waveform are: with a signal-to-noise ratio of 13dB under the AWGN channel and a signal-to-noise ratio of 19dB under the Poor channel, the bit error rate is no greater than .

[0115] Figure 3 Simulation results under an AWGN channel are presented, showing that the bit error rate at 11dB is less than It outperforms Appendix D by 2dB, and iteration did not improve performance.

[0116] Figure 4 Simulation results for a poor channel are presented. Without iteration, the bit error rate (BER) is around 1E-4 at a signal-to-noise ratio (SNR) of 19dB. After one iteration, the BER is lower than [previous value] at an SNR of 16dB. The performance is 3dB better than Appendix D, and the performance is 1.5dB better after 2 iterations than after 1 iteration. Beyond 2 iterations, the performance improvement is negligible. Therefore, setting the maximum number of iterations to 2 is sufficient.

[0117] This embodiment utilizes decision feedback iterative equalization technology to iteratively receive waveforms defined in Appendix D of MIL-STD-188-110D, effectively eliminating inter-symbol interference caused by fading and multipath propagation during shortwave signal transmission. This method requires no matrix inversion, has low computational complexity, and exhibits good numerical stability. Simulation results outperform the performance indicators specified in Appendix D, demonstrating that this technology has excellent reception performance in equalized reception of shortwave waveforms.

[0118] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A shortwave communication equalization method based on decision feedback iteration, characterized in that, Includes the following steps: Step 1: After encoding, interleaving, and mapping the data information, the transmitting end generates multiple unknown data blocks. Known data sequences are then inserted between the unknown data blocks at intervals to form a data segment sequence, which is then sent to the receiving end through the channel. Step 2: The receiving end extracts known data segments from the received signal segment by segment and sends them to the channel estimation module to estimate the channel impulse response corresponding to the known data segments and send it to the first equalizer; it also extracts unknown data segments segment by segment and sends them to the first equalizer for segment-by-segment equalization. Step 3: Save the channel estimate, unknown data segment, and equalization result of the first equalizer input segment by segment; Step 4: The LLR calculation module demaps the equalization result, deinterleaves and decodes the calculated LLR value, and sends the decoding result to the iteration end determination module after decoding is completed. Step 5: The iteration end determination module determines whether the decoding result meets the iteration end condition according to the set criteria. If it does, the iteration ends and the decoding result is output. Otherwise, the decoding result is sent to the encoding module of the iterative receiving unit; Step 6: The decoding result is encoded, interleaved, and mapped in the iterative receiving unit. The mapping result is divided into mapping segments. The mapping segments are used as the initial values ​​for the iteration. The channel estimate value, unknown data segment, and mapping segment of the first equalizer input are input into the second equalizer segment by segment. Then, proceed to step 4 to perform the next LLR calculation, deinterleaving, and decoding process until the iteration end condition is met.

2. The shortwave communication equalization method based on decision feedback iteration according to claim 1, characterized in that, The waveform frame of the data information consists of three segments: TLC segment, synchronization segment, and data segment. The TLC segment is used for transmitting-end level adjustment and receiving-end AGC; the synchronization segment is used for receiving-end synchronization search, frequency offset compensation, and waveform number transmission; and the data segment carries the data information to be transmitted. The data segment is composed of alternating known data segments and unknown data segments. The known data segments are used for channel state estimation and tracking. The lengths of the known and unknown data segments vary with bandwidth and data rate, and the modulation scheme of the unknown data varies with data rate.

3. The shortwave communication equalization method based on decision feedback iteration according to claim 1, characterized in that, In step 1, the transmitting end encodes, interleaves, and maps the data information to generate multiple unknown data blocks. Known data sequences are then inserted at intervals between the unknown data blocks to form a data segment sequence, which is then transmitted to the receiving end through the channel. The specific steps are as follows: Step 1.1: The sending end encodes, interleaves, and maps the data information to generate an unknown data sequence. Based on waveform parameters, the data is divided into multiple unknown data blocks of length N. ; Step 1.2: Insert the known data sequence at intervals between the unknown data blocks to form a data segment sequence; Step 1.3: Frame the TLC segment sequence, synchronization segment sequence, and data segment sequence to form the transmission sequence. via channel Send to the receiving end.

4. The shortwave communication equalization method based on decision feedback iteration according to claim 3, characterized in that, Step 1.3 involves framing the TLC segment sequence, synchronization segment sequence, and data segment sequence to form the transmission sequence. via channel The data is sent to the receiving end as follows: For an unknown data block , Through time-varying channels , The channel introduces additive white Gaussian noise. , Received unknown data block , Then, it can be expressed as a system of linear equations: ， ， ， （1） Will , , Written in vector form: ； ； ； Will Written The matrix form is: ； Then the system of equations (1) can be written as matrix equations: （2） Therefore, the problem of balancing unknown data blocks is to find the optimal solution to the matrix equation. In noisy conditions, reduce error The smallest sum of squares that is Therefore, the least squares algorithm is used to obtain the best estimate. Ignore noise vector , Constructed from the channel estimate, the matrix equation of equation (2) simplifies to: , The least squares solution is: （3） in express The conjugate transpose of . , express The inverse matrix of , where R is the autocorrelation matrix of the channel, which is a Hermitian matrix, let . The least squares solution can be further expressed as: .

5. The shortwave communication equalization method based on decision feedback iteration according to claim 4, characterized in that, In step 2, the receiving end extracts known data segments from the received signal segment by segment and sends them to the channel estimation module to estimate the channel impulse response corresponding to the segment, which is then sent to the first equalizer. Unknown data segments are also extracted segment by segment and sent to the first equalizer for segment-by-segment equalization, as detailed below: Step 2.1: Use the Jacobi algorithm to solve the matrix equation, and modify the algorithm to solve the matrix equation. Rewritten as follows: （4） in It is a diagonal matrix; , , , , , ; It is by The lower triangular matrix formed by the elements on the lower left of the main diagonal; Depend on The upper triangular matrix formed by the elements at the top right of the main diagonal is then... The inverse of the expression is directly achieved by finding the reciprocal of the main diagonal element; Step 2.2, from To obtain an estimate of X Initial estimates are obtained by hard-decision on an element-by-element basis. ,in These are the values ​​on the corresponding modulation scheme constellation diagram; Step 2.3: Iterative solution, using initial values... Substituting into the following formula, we obtain the new symbol estimate. : ， （5） in This represents the maximum number of iterations. right Perform element-by-element judgment to obtain a new judgment value. Used as the initial value for the next iteration; Step 2.4: Compare the decision values ​​of two adjacent iterations. The iteration continues until the percentage of distinct decision value elements is less than the threshold Th, or the number of iterations reaches the set maximum number M, at which point the iteration terminates. Otherwise, return to step 2.3 to begin a new round of iterations; Step 2.5: Set the final estimated value. The output of the first equalizer is sent to the subsequent demapping module.

6. The shortwave communication equalization method based on decision feedback iteration according to claim 1, characterized in that, In step 5, the iteration end determination module determines whether the decoding result meets the iteration end condition based on the set criteria, as follows: The iteration termination criterion is as follows: the iteration does not end upon receiving the first decoding result. Starting from the second decoding result, the iteration termination criterion is determined by comparing the percentage difference between the results of two adjacent decodings with a threshold and by combining this with the set maximum number of iterations. When the iteration reaches the maximum number of iterations or the percentage difference between the results of two adjacent decodings is less than the set threshold, the iteration process exits, the decoding result is output, and the reception of the transmitted data is completed.

7. The shortwave communication equalization method based on decision feedback iteration according to claim 1, characterized in that, The initial value of the second equalizer iteration in step 6 The symbols derived from the decoded information are re-encoded, interleaved, and mapped. The maximum number of iterations is M=1. The output value of the second equalizer is... .

8. A shortwave communication equalization system based on decision feedback iteration, characterized in that, This system is used to implement the shortwave communication equalization method based on decision feedback iteration as described in any one of claims 1 to 7. The system includes a transmitting module, a receiving module, a storage module, a demapping module, an iteration end determination module, and an iteration receiving unit, wherein: The transmitting module encodes, interleaves, and maps the data information to generate multiple unknown data blocks. Known data sequences are then inserted between the unknown data blocks at intervals to form a data segment sequence, which is then transmitted to the receiving end through the channel. The receiving module extracts known data segments from the received signal segment by segment and sends them to the channel estimation module to estimate the channel impulse response corresponding to the known data segments and send it to the first equalizer; it also extracts unknown data segments segment by segment and sends them to the first equalizer for segment-by-segment equalization. The storage module saves the channel estimate, unknown data segments, and equalization results of the first equalizer input segment by segment. The demapping module performs LLR demapping calculation on the equalization result, and deinterleaves and decodes the calculated LLR value. After decoding, the decoded value is output to the iteration end determination module. The iteration end determination module determines whether the decoding result meets the iteration end condition according to the set criteria. If it does, the iteration ends and the decoding result is output; otherwise, the decoding result is sent to the encoding module of the iteration receiving unit. In the iterative receiving unit, the decoding result is encoded, interleaved, and mapped. The mapping result is divided into mapping segments, which are used as the initial values ​​for iteration. The channel estimate value, unknown data segment, and mapping segment of the first equalizer input are input to the second equalizer segment by segment. The demapping module performs the next calculation of LLR and decoding process until the iteration ends.

9. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the shortwave communication equalization method based on decision feedback iteration as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the shortwave communication equalization method based on decision feedback iteration as described in any one of claims 1 to 7.

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