Hadamard-LDPC iterative decoding method based on interference suppression

Abstract

The invention provides a Hadamard-LDPC (Low Density Parity Check) iterative decoding method based on interference suppression, and belongs to the technical field of wireless communication. The method comprises the following steps of: initializing a Hadamard check node and a decoder variable node according to an LLR (Log Likelihood Ratio) value; carrying out iteration by utilizing a variable node decoder and a Hadamard check node decoder; estimating pulse interference power information, and calculating a pulse interference power variance; performing weighted updating on a channel receiving information LLR value by using the pulse interference power variance; obtaining an updated decoder variable node posterior LLR value; and decoding judgment: judging whether the maximum number of iterations is met or not, if not, continuing iteration, if so, judging according to the symbol of the updated decoder variable node posterior LLR value, and finally outputting a judgment result. Through real-time cognition of the interference power of each bit and updating of the channel information, the confidence coefficient of the interfered bit in the iterative decoding process is suppressed, the influence of interference on the decoding performance is reduced, and the anti-interference capability and the decoding performance of the system are improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and particularly relates to a Hadamard-LDPC iterative decoding method based on interference suppression, which can be used for frequency hopping wireless communication data transmission in low signal-to-noise ratio environments to improve anti-interference and transmission reliability. Background Technology

[0002] With the development of the low-altitude economy, unmanned swarm communication network technology is increasingly becoming an important direction for future development. Due to the limited payload and hardware resources of unmanned swarm platforms, their transmission power is usually low. Furthermore, atmospheric attenuation of wireless signals during transmission leads to a low signal-to-noise ratio (SNR) in the received signal. Additionally, the swarm network suffers from limited internal spectrum resources and strong self-interference between links. Therefore, compared to terrestrial communication networks, unmanned swarm networks exhibit unique requirements: low SNR, low hardware complexity, and strong interference. To ensure the reliability and efficiency of unmanned swarm communication networks, a low-code-rate channel coding scheme suitable for low SNR channel environments, while also possessing low complexity and strong anti-interference capabilities, is required.

[0003] The 5G standard LDPC code specifies a code rate range from 1 / 5 to 11 / 12, but it does not support low signal-to-noise ratio (SNR) scenarios with code rates below 1 / 5. To improve the decoding performance of LDPC codes, parity check constraints can be replaced with other constraints, such as Hamming codes and Hadamard codes, to construct generalized LDPC codes. Hadamard-LDPC codes, by constructing coded bits that satisfy the Hadamard constraint, enable check nodes to perform efficient information updates under low SNR conditions, thereby compensating for the insufficient decoding performance of LDPC codes at low SNR.

[0004] Due to its unique suitability for low signal-to-noise ratio (SNR) environments, Hadamard-LDPC codes have been extensively studied in specialized fields such as deep space communication and satellite communication. Patent document "A Low-Complexity Decoding Method for LDPC-Hadamard Codes Based on Prototype Diagrams" (Application No. 202411236340.4, Publication No. CN119402016 A) discloses a low-complexity decoding method for LDPC-Hadamard codes based on prototype diagrams; patent document "A Hybrid Encoding and Decoding Method for LDPC-Hadamard Codes in Low SNR Environments" (Application No. 202411003461.4, Publication No. CN118868975 A) discloses a hybrid encoding and decoding method for LDPC-Hadamard codes in low SNR environments. Both methods combine the efficient error correction performance of LDPC encoding with the noise resistance of Hadamard encoding, effectively improving the transmission reliability of the system in low SNR environments.

[0005] However, the aforementioned decoders all assume that the confidence level of all received channel information is the same, and the weights of all input channel information are consistent when the variable node is updated. This is clearly unsuitable for frequency-hopping communication or interference scenarios. In frequency-hopping communication scenarios, due to device and manufacturing process errors, the antenna gain and RF power of each frequency point will differ. Simultaneously, self-interference exists within the network, ultimately leading to dynamic differences in link quality at each frequency point at the receiver. Setting the confidence level of all channel information to the same value in this case will directly affect decoding performance. Therefore, for frequency-hopping communication scenarios with dynamically changing link quality, it is crucial to assign different weights to different channel information based on real-time link quality to achieve interference suppression iterative decoding based on link quality awareness. Summary of the Invention

[0006] The purpose of this invention is to provide a Hadamard-LDPC iterative decoding method based on interference suppression. For frequency hopping communication scenarios with dynamically changing link quality, the decoder performs weighted processing on the received channel information based on the real-time perceived link quality at each frequency point, avoiding assigning high confidence to information with poor quality. This solves the technical problem of insufficient anti-interference capability of decoders in frequency hopping communication scenarios with dynamically changing link quality in the prior art.

[0007] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0008] A Hadamard-LDPC iterative decoding method based on interference suppression, the method comprising the following steps:

[0009] Step S1: Receive information via channel The log-likelihood ratio (LLR) of the channel received information is obtained, and then all Hadamard check nodes and decoder variable nodes are initialized based on the LLR value.

[0010] Step S2: Iterate using the variable node decoder and the Hadamard check node decoder to update the Hadamard check node information and the decoder variable node information;

[0011] Step S3: During the iteration, the impulse interference power information is estimated using the posterior LLR value of the decoder variable node and the LLR value of the channel received information, and the impulse interference power variance is calculated.

[0012] Step S4: Use the impulse interference power variance to perform a weighted update on the channel received information LLR value; use the weighted updated channel received information LLR value to update the decoder variable node posterior LLR value, and obtain the updated decoder variable node posterior LLR value.

[0013] Step S5: Decoding decision. Determine if the maximum number of iterations is met. If not, return to step S2 to continue iterating. If it is met, make a decision based on the sign of the updated decoder variable node posterior LLR value. A positive sign results in a decision of 1, and a negative sign results in a decision of 0. Finally, output the decision result.

[0014] Further, step S1 includes the following steps:

[0015] Step S11: Receive information via channel The log-likelihood ratio (LLR) of the received information from the channel is calculated as follows:

[0016]

[0017] in, This represents the log-likelihood ratio (LLR) value. For noise variance, The noise one-sided power spectral density;

[0018] Step S12: Decode all decoder variable nodes during the 0th iteration. The a posteriori LLR value is set as the corresponding channel received information LLR value, the first... Each decoder variable node The settings are as follows:

[0019]

[0020] in, This indicates that during the 0th iteration of decoding, the... Each decoder variable node The posterior LLR value; Indicates the first LLR values ​​of received information from each channel;

[0021] Step S13: During the 0th iteration of decoding, start from the... Hadamard verification nodes Passed to the Each decoder variable node The external information LLR value is set to 0, which means the following:

[0022]

[0023] in, This indicates that during the 0th iteration of decoding, starting from the 1st... Hadamard verification nodes Passed to the Each decoder variable node The external information LLR value.

[0024] Furthermore, each iteration of step S2 performs the following operation:

[0025] Step S21: In the first In the nth iteration, the 1st Each decoder variable node According to the The second iteration Hadamard verification nodes Passed to the Each decoder variable node External information LLR value and the Each decoder variable node No. The posterior LLR value updated in the next iteration , obtained the The second iteration Each decoder variable node Passed to the Hadamard verification nodes External information value ,Right now:

[0026]

[0027] Step S22: In the first In the nth iteration, the 1st Hadamard verification nodes Receive all decoder variable nodes connected to it Transmitted extrinsic information value and all Hadamard variable node external information values Thus, the first Hadamard verification nodes Posterior probability ,Right now:

[0028]

[0029] in, This represents the formula for calculating the symbol-by-symbol maximum a posteriori probability of a Hadamard code. Indicates the decoder variable node number, Indicates the relationship with the first Hadamard verification nodes The set of all connected decoder variable nodes; Indicates the Hadamard variable node number. Indicates the relationship with the first Hadamard verification nodes The set of all connected Hadamard variable nodes; This represents the external information value of the Hadamard variable node;

[0030] Step S23: In the first In the iteration, according to the Hadamard verification nodes Posterior probability and the Each decoder variable node Passed to the Hadamard verification nodes External information value , can obtain the first Hadamard verification nodes Pass back to the first Each decoder variable node External information ,Right now:

[0031]

[0032] Step S24: In the In the nth iteration, the 1st Each decoder variable node According to the Hadamard verification nodes External information returned and the Each decoder variable node Passed to the Hadamard verification nodes External information value , obtained the After the nth iteration Each decoder variable node posterior LLR value ,Right now:

[0033] .

[0034] Furthermore, in step S3, the variance of the pulse interference power is calculated as follows:

[0035]

[0036] in, Indicates the first The variance of interference power for each pulse; This indicates the length of the message bits contained in each pulse. This represents the set of all received information using the j-th pulse.

[0037] Further, step S4 includes the following steps: using the first Variance of interference power per pulse and the average interference power of the entire codeword For the Each decoder variable node Channel received information LLR value Perform a weighted update to obtain the weighted channel reception information (LLR) value. Then, the weighted channel receive information LLR value is used. The channel reception information LLR value after the previous iteration weighted value and the current number The next iteration updates the calculated posterior LLR value. The weighted update of the first Each decoder variable node posterior LLR value ; indicates the following:

[0038]

[0039]

[0040] in, This represents the average interference power of the entire codeword.

[0041] Furthermore, the average interference power of the entire codeword is calculated as follows:

[0042]

[0043] in, Indicates the number of pulses.

[0044] Compared with the prior art, the present invention has the following beneficial technical effects:

[0045] 1) The Hadamard-LDPC iterative decoding method based on interference suppression proposed in this invention differs from traditional iterative decoding methods. The interference suppression iterative decoding method introduces real-time awareness of the interference power of each bit and updates the channel information, thereby suppressing the confidence of the interfered bits during the iterative decoding process, reducing the impact of interference on decoding performance, and thus improving the system's anti-interference capability and decoding performance.

[0046] 2) The Hadamard-LDPC iterative decoding method based on interference suppression proposed in this invention simplifies the computation process by replacing nonlinear operations such as exponentiation with linear operations such as addition and comparison, thereby achieving linear decoding and greatly reducing the decoding complexity.

[0047] 3) The Hadamard-LDPC iterative decoding method based on interference suppression proposed in this invention introduces a normalization coefficient while considering the interference power weight during the variable node update process. This avoids changing the distribution of external information values ​​of the check node during the interference power weighting and linear approximation process, thereby improving the system's anti-interference capability without affecting the decoding performance. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of the Hadamard-LDPC parity check matrix and codeword composition of the present invention.

[0050] Figure 2 This is a diagram of the Hadamard-LDPC code interference suppression iterative decoding factor of the present invention.

[0051] Figure 3 This is a diagram of the Hadamard-LDPC code interference suppression iterative decoder of the present invention.

[0052] Figure 4 This is a schematic diagram of the variable node decoder of the present invention.

[0053] Figure 5 This is a schematic diagram of the Hadamard check node decoder of the present invention.

[0054] Figure 6 This is a performance comparison chart between the present invention and a traditional decoder in a non-confrontational frequency hopping communication scenario.

[0055] Figure 7 This is a performance comparison chart between the present invention and a traditional decoder in a frequency-hopping communication scenario. Detailed Implementation

[0056] 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.

[0057] This invention addresses the problem of varying confidence levels of received channel information due to different interference levels at different frequency points in frequency-hopping communication scenarios. It proposes a Hadamard-LDPC iterative decoding method based on interference suppression. The method suppresses the received channel information according to the real-time link quality at each frequency point, assigning lower confidence levels to channel information with poor quality, thus avoiding the impact of interference information on the decoder and improving decoding performance.

[0058] To facilitate understanding, a brief description of Hadamard-LDPC codes will be provided first, such as... Figure 1 As shown, the Hadamard-LDPC code is a linear block code that replaces the parity check constraint with the Hadamard constraint constraint in the LDPC code. The redundancy information of the Hadamard-LDPC code is divided into two parts: parity check bits and Hadamard constraint bits. The present invention provides a Hadamard-LDPC code parity check matrix. The Hadamard-LDPC code parity-check matrix employs a quasi-cyclic, double-diagonal structure. for OK The order of the Hadamard code is... The information bit length is The parity check bit length is The Hadamard constraint bit length is The encoded bit length In the iterative decoding process, only the parity check bits and information bits participate in the iterative update; the Hadamard constraint bit values ​​are not updated, but only participate in the iterative process. In this embodiment... , , The information bit length is 1024, the parity check bit length is 832, the Hadamard constraint bit length is 8320, and the encoded bit length is... .

[0059] In frequency hopping communication, the encoded information bits are grouped, and each group of bits is modulated and frequency-converted to generate a pulse. Each pulse uses a specific frequency hopping frequency. Due to differences in the transmit antenna gain and RF channel at different frequencies, and the varying degrees of interference at different frequencies, there is an inconsistency in the signal-to-noise ratio at the receiving end. Traditional decoders often assume that the confidence level of the channel information of all received bits is consistent. Therefore, each channel information weight is consistent during the iteration process, resulting in performance loss. This invention addresses the scenario of dynamic changes in link quality at each frequency. It assesses the degree of interference at each frequency based on the received signal and the output signal of the current decoding decision, and then updates the input signal with weights based on the predicted interference power information for the next iteration until the decoding ends.

[0060] In the embodiment, the encoded Each bit is divided into Each pulse contains a message bit length c equal to the data bit length of each group. All pulse information constitutes the transmitted signal. , Indicates the first The transmitted signal, after propagating through the channel, is affected by noise. After interference, the channel reception information is obtained. , Indicates the first Information is received through each channel. The relationship between the transmitted signal, the received information, and the noise is as follows: The log-likelihood ratio (LLR) of the received channel information is then... ,in For noise variance, This represents the one-sided power spectral density of the noise. In this embodiment, , , .

[0061] See Figure 2 This invention utilizes factor graphs to represent the Hadamard-LDPC iterative decoding process based on interference suppression. A factor graph is a special type of bipartite graph model that characterizes the joint probability distribution relationship between random variables. In the factor graph, all circle nodes represent variable nodes, and box nodes represent factor nodes. Factor nodes are connected to their associated variable nodes, indicating that these variable nodes satisfy certain specific relationships. The factor graph established in this invention is divided into three regions.

[0062] A probabilistic model of interference power was established for region 1. Indicates the first The power of the pulse interference, the first pulse interference power, The variance of the interference power of each pulse is ,but Indicates the first The probability distribution of interference power for each pulse.

[0063] Decoder variable nodes were established in region 2. (The aforementioned set of information bits and parity bits), the relationship between channel received information Y and interference power P, and the joint probability distribution of the channel. The first Receive information through each channel , No. Each decoder variable node and the Variance of interference power per pulse The relationship between them is expressed as follows:

[0064]

[0065] in, ( To verify the number of columns in the matrix, this embodiment uses 1856. ( (This refers to the number of pulses; in this embodiment, it is set to 48).

[0066] Since only parity bits and information bits participate in the iterative decoding process, the Hadamard constraint bit values ​​are not updated but only participate in the iterative process. Therefore, in region 2, only the parity bits and information bits are updated. The parity check bits and information bits are processed together.

[0067] Region 3 is the Tanner diagram of the Hadamard-LDPC code, describing the decoder variable nodes. With Hadamard verification node The relationship between them is represented as follows:

[0068]

[0069] in, Indicates the first Each decoder variable node With the Hadamard verification nodes The constraint relationship, ( To verify the number of rows in the matrix, this embodiment uses 832. ( To verify the number of columns in the matrix, this embodiment uses 1856. Decoder variable node. , Indicates the first Each decoder variable node.

[0070] Figure 3 This invention presents a Hadamard-LDPC iterative decoding framework based on interference suppression. Unlike traditional iterative decoding methods, this invention, during the iterative interaction between the verification node and the variable node, evaluates the interference power information of each bit based on the real-time updated variable node information. Then, it uses this interference power information to weight and update the variable node information, thereby suppressing the confidence level of the interfered bits during iterative decoding and reducing the impact of interference on decoding performance.

[0071] For the Variance of interference power per pulse The confidence level updated in each iteration With the The joint channel probability distribution of all received information sets for each pulse is proportional to the channel probability distribution, i.e.:

[0072]

[0073] in, Indicates the first The set of all received information from the nth pulse, i.e., the set of all received information from the nth pulse. pulse interference power All connected factor nodes f i A set; Indicates the first Factor nodes f i Passed to the Each pulse interference power variable node The information is the aforementioned joint probability distribution of the channels. and The product of For decoder variable nodes The probability of finding the first [value / value]. The purpose of this invention is to find the [value / value] with the highest confidence level. Variance of interference power per pulse According to the first Variance of interference power per pulse Update the channel input information and participate in the next iteration until the decoding is complete.

[0074] Simplifying the above formula, we can obtain:

[0075]

[0076] in, This indicates the length of the message bits contained in each pulse.

[0077] Simplify by taking the logarithm of the above expression.

[0078]

[0079] Taking the first derivative, we get

[0080]

[0081] So, based on the current input, the first... Receive information through each channel With the iteration of the first Each decoder variable node , can obtain the first Variance of interference power per pulse for:

[0082]

[0083] in, Indicates the first The variance of interference power of each pulse Indicates the first Each channel receives information. This indicates the 1st iteration. Each decoder variable node This indicates the length of the message bits contained in each pulse. This represents the set of all received information using the j-th pulse.

[0084] See Figure 3 The Hadamard-LDPC iterative decoding method based on interference suppression proposed in this invention introduces real-time evaluation of impulse interference power information on the basis of traditional iterative decoding. The weights of the channel received information are calculated using the variance of the impulse interference power, thereby reducing the confidence of the interfered information and improving decoding performance. The specific steps are as follows:

[0085] Step S1: Receive information via the channel The log-likelihood ratio (LLR) of the channel received information is obtained, and then all Hadamard check nodes and decoder variable nodes are initialized based on the LLR value.

[0086] Step S11: Receive information via channel The log-likelihood ratio (LLR) of the received information from the channel is calculated as follows:

[0087]

[0088] in, This represents the log-likelihood ratio (LLR) value. For noise variance, This represents the one-sided power spectral density of the noise.

[0089] Step S12: Decode all decoder variable nodes during the 0th iteration. The a posteriori LLR value is set as the corresponding channel received information LLR value, as shown below.

[0090]

[0091] in, This indicates that during the 0th iteration of decoding, the... Each decoder variable node The posterior LLR value; Indicates the first The LLR value of the received information for each channel.

[0092] Step S13: During the 0th iteration of decoding, start from the... Hadamard verification nodes Passed to the Each decoder variable node The external information LLR value is set to 0, which means the following:

[0093]

[0094] in, This indicates that during the 0th iteration of decoding, starting from the 1st... Hadamard verification nodes Passed to the Each decoder variable node External information LLR value, ( To verify the number of rows in the matrix, this embodiment uses 832. ( To verify the number of columns in the matrix, this embodiment uses 1856.

[0095] Step S2: Iterate using the variable node decoder and the Hadamard check node decoder to update the Hadamard check node information and the decoder variable node information.

[0096] Each iteration performs the following operations:

[0097] Step S21: See Figure 4 In the In the next iteration ( Starting from 1), the Each decoder variable node According to the The second iteration Hadamard verification nodes Passed to the Each decoder variable node External information LLR value and the Each decoder variable node No. The posterior LLR value updated in the next iteration , obtained the The second iteration Each decoder variable node Passed to the Hadamard verification nodes External information value ,Right now:

[0098]

[0099] Step S22: See Figure 5 In the In the nth iteration, the 1st Hadamard verification nodes Receive all decoder variable nodes connected to it Transmitted extrinsic information value and all Hadamard variable node external information values Thus, the first Hadamard verification nodes Posterior probability ,Right now:

[0100]

[0101] in, This represents the formula for calculating the symbol-by-symbol maximum a posteriori probability of a Hadamard code. Indicates the decoder variable node number, Indicates the relationship with the first Hadamard verification nodes The set of all connected decoder variable nodes; Indicates the Hadamard variable node number. Indicates the relationship with the first Hadamard verification nodes The set of all connected Hadamard variable nodes (the aforementioned Hadamard constraint bits); This represents the external information value of the Hadamard variable node.

[0102] Since the Hadamard variable node does not participate in the iteration, the external information value of the Hadamard variable node is equal to the LLR value of the channel received information, that is:

[0103]

[0104] in, ( To verify the number of rows in the matrix, this embodiment uses 832. ( (This is the order of the Hadamard code; in this embodiment, it is set to 4). See also... Figure 5 In this embodiment, each Hadamard check node is connected to 6 decoder variable nodes and 10 Hadamard variable nodes.

[0105] Step S23: In the first In the iteration, according to the Hadamard verification nodes Posterior probability and the Each decoder variable node Passed to the Hadamard verification nodes External information value , can obtain the first Hadamard verification nodes Pass back to the first Each decoder variable node External information ,Right now:

[0106]

[0107] Step S24: In the In the nth iteration, the 1st Each decoder variable node According to the Hadamard verification nodes External information returned and the Each decoder variable node Passed to the Hadamard verification nodes External information value , obtained the After the nth iteration Each decoder variable node posterior LLR value ,Right now:

[0108]

[0109] Step S3: Impulse interference power information estimation. In the l-th iteration, the aforementioned impulse interference power variance is used. The calculation formula, in the logarithmic domain, uses the channel received information LLR value to represent the channel received information (i.e., use (represented by) the decoder variable node posterior LLR value (i.e., use (represented), to obtain the first Variance of interference power per pulse ,Right now:

[0110]

[0111] in, This indicates the length of the message bits contained in each pulse. This represents the set of all received information using the j-th pulse. The average interference power of the entire codeword. Calculated as follows:

[0112]

[0113] Step S4: Channel information update, utilizing the first Variance of interference power per pulse and the average interference power of the entire codeword For the Each decoder variable node Channel received information LLR value Perform a weighted update to obtain the weighted channel reception information (LLR) value. Then, the weighted channel receive information LLR value is used. The channel reception information LLR value after the previous iteration weighted value and the current number The calculation of the iteration update Each decoder variable node posterior LLR value The weighted update of the first Each decoder variable node posterior LLR value ,

[0114]

[0115]

[0116] The decoder performs weighted processing on the received channel information based on the real-time perceived link quality of each frequency point, and obtains the weighted updated variable node posterior probability (LLR) value. The purpose of the LDPC decoder is to iteratively calculate the LLR value of each decoder variable node, and then make a decision based on the sign of this LLR value. A positive sign results in a decision of 1, and a negative sign results in a decision of 0.

[0117] Step S5: Decoding decision. Determine if the maximum number of iterations is met. If not, return to step S2 to continue iterating. If it is met, make a decision based on the sign of the updated decoder variable node posterior LLR value. A positive sign results in a decision of 1, and a negative sign results in a decision of 0. Finally, output the decision result.

[0118] See Figure 6This invention simulates scenarios where the received signal link quality dynamically changes due to inconsistent antenna gain and RF channel gain at different frequency points in frequency-hopping communication. The attached figures compare the bit error rate (BER) performance curves of the traditional non-interference suppression decoding method and the interference suppression decoding method of this invention under different signal-to-noise ratio (SNR) conditions. The horizontal axis represents the SNR (Eb / N0), and the vertical axis represents the BER. The simulation assumes a receiving gain variation scale of 5 dB at different frequency points. The comparison between the traditional method and the interference suppression method shows that this invention achieves a performance improvement of approximately 0.5 dB compared to the traditional decoding algorithm.

[0119] See Figure 7 This invention addresses malicious interference scenarios, setting the noise power to -99.5 dBm, the received interference power to -84.5 dBm, and the interference to 70% of the frequency points. Benefiting from the suppression of severely interfered bit channel information, the attached figure compares the bit error rate (BER) performance curves of the traditional non-interference suppression decoding method and the interference suppression decoding method of this invention under different signal-to-noise ratio (SNR) conditions. The horizontal axis represents the SNR (Eb / N0), and the vertical axis represents the BER. Compared to the traditional decoding algorithm, the interference suppression decoding method improves performance by approximately 2.5 dB.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A Hadamard-LDPC iterative decoding method based on interference suppression, characterized in that, The method includes the following steps: Step S1: Receive information via the channel The log-likelihood ratio (LLR) of the channel received information is obtained, and then all Hadamard check nodes and decoder variable nodes are initialized based on the LLR value. Step S2: Iterate using the variable node decoder and the Hadamard check node decoder to update the Hadamard check node information and the decoder variable node information; Step S3: During the iteration, the impulse interference power information is estimated using the posterior LLR value of the decoder variable node and the LLR value of the channel received information, and the impulse interference power variance is calculated. Step S4: Use the impulse interference power variance to perform a weighted update on the channel received information LLR value; use the weighted updated channel received information LLR value to update the decoder variable node posterior LLR value, and obtain the updated decoder variable node posterior LLR value. Step S5: Decoding decision. Determine if the maximum number of iterations is met. If not, return to step S2 to continue iterating. If it is met, make a decision based on the sign of the updated decoder variable node posterior LLR value. A positive sign results in a decision of 1, and a negative sign results in a decision of 0. Finally, output the decision result.

2. The Hadamard-LDPC iterative decoding method based on interference suppression according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Receive information via channel The log-likelihood ratio (LLR) of the received information from the channel is calculated as follows: in, This represents the log-likelihood ratio (LLR) value. For noise variance, The noise one-sided power spectral density; Step S12: Decode all decoder variable nodes during the 0th iteration. The a posteriori LLR value is set as the corresponding channel received information LLR value, the first... Each decoder variable node The settings are as follows: in, This indicates that during the 0th iteration of decoding, the... Each decoder variable node The posterior LLR value; Indicates the first LLR values ​​of received information from each channel; Step S13: During the 0th iteration of decoding, start from the... Hadamard verification nodes Passed to the Each decoder variable node The external information LLR value is set to 0, which means the following: in, This indicates that during the 0th iteration of decoding, starting from the 1st... Hadamard verification nodes Passed to the Each decoder variable node The external information LLR value.

3. The Hadamard-LDPC iterative decoding method based on interference suppression according to claim 2, characterized in that, Each iteration of step S2 performs the following operations: Step S21: In the first In the nth iteration, the 1st Each decoder variable node According to the The second iteration Hadamard verification nodes Passed to the Each decoder variable node External information LLR value and the Each decoder variable node No. The posterior LLR value updated in the next iteration , obtained the The second iteration Each decoder variable node Passed to the Hadamard verification nodes External information value ,Right now: Step S22: In the first In the nth iteration, the 1st Hadamard verification nodes Receive all decoder variable nodes connected to it. Transmitted extrinsic information value and all Hadamard variable node external information values Thus, the first Hadamard verification nodes Posterior probability ,Right now: in, This represents the formula for calculating the symbol-by-symbol maximum a posteriori probability of a Hadamard code. Indicates the decoder variable node number, Indicates the relationship with the first Hadamard verification nodes The set of all connected decoder variable nodes; Indicates the Hadamard variable node number. Indicates the relationship with the first Hadamard verification nodes The set of all connected Hadamard variable nodes; This represents the external information value of the Hadamard variable node; Step S23: In the first In the nth iteration, according to the th Hadamard verification nodes Posterior probability and the Each decoder variable node Passed to the Hadamard verification nodes External information value , obtained the Hadamard verification nodes Pass back to the first Each decoder variable node External information ,Right now: Step S24: In the In the nth iteration, the 1st Each decoder variable node According to the Hadamard verification nodes External information returned and the Each decoder variable node Passed to the Hadamard verification nodes External information value , obtained the After the nth iteration Each decoder variable node posterior LLR value ,Right now: 。 4. The Hadamard-LDPC iterative decoding method based on interference suppression according to claim 3, characterized in that, In step S3, the variance of the pulse interference power is calculated as follows: in, Indicates the first The variance of interference power for each pulse; This indicates the length of the message bits contained in each pulse. This represents the set of all received information using the j-th pulse.

5. The Hadamard-LDPC iterative decoding method based on interference suppression according to claim 4, characterized in that, Step S4 includes the following steps: using the first Variance of interference power per pulse and the average interference power of the entire codeword For the Each decoder variable node Channel received information LLR value Perform a weighted update to obtain the weighted channel reception information (LLR) value. Then, the weighted channel receive information LLR value is used. The channel reception information LLR value after the previous iteration weighted value and the current number The next iteration updates the calculated posterior LLR value. The weighted update of the first Each decoder variable node posterior LLR value ; indicates the following: in, This represents the average interference power of the entire codeword.

6. The Hadamard-LDPC iterative decoding method based on interference suppression according to claim 5, characterized in that, The average interference power of the entire codeword is calculated as follows: in, Indicates the number of pulses.

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