A Hadamard-LDPC iterative decoding method based on interference suppression

Abstract

The application provides a Hadamard-LDPC iterative decoding method based on interference suppression and belongs to the technical field of wireless communication. The method comprises the following steps: initializing Hadamard check nodes and decoder variable nodes according to log-likelihood ratio (LLR) values; iteratively using variable node decoders and Hadamard check node decoders; estimating impulse interference power information and calculating impulse interference power variance; weighting and updating channel receiving information LLR values by using the impulse interference power variance; obtaining updated decoder variable node posteriori LLR values; decoding and judging whether the maximum iteration number is met; if the maximum iteration number is not met, iteration is continued; if the maximum iteration number is met, a decision is made according to the sign of the updated decoder variable node posteriori LLR values; and finally, a decision result is output. Through real-time cognition of the interference power of each bit and updating of channel information, the application can suppress the confidence of the interfered bits in the iterative decoding process, reduce the influence of interference on decoding performance, and improve the anti-interference capability and decoding performance of the system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, 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 a low signal-to-noise ratio environment to improve the anti-interference and transmission reliability. BACKGROUND

[0002] With the development of low-altitude economy, unmanned swarm communication network technology is becoming an important direction for future development. Due to the factors of limited payload and hardware resources of the unmanned swarm platform, the transmission power is not large, and in addition, the wireless signal is attenuated in the transmission process, resulting in a low signal-to-noise ratio of the received signal. In addition, the frequency spectrum resource is tight in the swarm network, and there is strong self-interference phenomenon between links. Therefore, compared with the ground communication network, the unmanned swarm network presents the special requirements of low signal-to-noise ratio, low hardware complexity and strong interference. In order to make the unmanned swarm communication network reliable and efficient, it is required to provide a low code rate channel coding scheme suitable for a low signal-to-noise ratio channel environment, which also has low complexity and strong anti-interference.

[0003] The 5G standard LDPC code specifies a code rate range from 1 / 5 to 11 / 12, but does not support low signal-to-noise ratio scenarios below the code rate of 1 / 5. To improve the decoding performance of the LDPC code, the parity check constraint can be replaced by other constraint relationships, such as Hamming code, Hadamard code, etc., to construct a generalized LDPC code. The Hadamard-LDPC code constructs coded bits that satisfy the Hadamard constraint relationship, so that the check nodes can complete efficient information updating at a low signal-to-noise ratio, thereby making up for the deficiency of the LDPC code in decoding performance at a low signal-to-noise ratio.

[0004] The Hadamard-LDPC code has been widely researched in specialized fields such as deep space communication and satellite communication due to its unique low signal-to-noise ratio environment. The patent document “A low complexity decoding method of LDPC-Hadamard code based on prototype graph” (application number 202411236340.4, application publication number CN119402016 A) discloses a low complexity decoding method of LDPC-Hadamard code based on prototype graph; the patent document “An LDPC-Hadamard hybrid encoding and decoding method in a low signal-to-noise ratio environment” (application number 202411003461.4, application publication number CN118868975 A) discloses an LDPC-Hadamard hybrid encoding and decoding method in a low signal-to-noise ratio environment. The above methods effectively improve the transmission reliability of the system in a low signal-to-noise ratio environment by combining the high error correction performance of the LDPC code and the noise resistance characteristics of the Hadamard code.

[0005] But the above decoder considers that the confidence of all received channel information is the same, and the weight of all input channel information is consistent in variable node update, which is obviously not applicable to frequency hopping communication scenarios or interference scenarios. In the frequency hopping communication scenario, due to device and process errors, the antenna gain and radio frequency power of each frequency point will be different, and there is also self-interference problem in the network, which finally leads to dynamic difference of link quality of each frequency point at the receiving end. At this time, setting the confidence of all channel information to the same value will directly affect the decoding performance. Therefore, in the frequency hopping communication scenario with dynamic link quality, it is particularly important to assign different weights to different channel information according to real-time link quality to realize interference suppression iterative decoding based on link quality awareness. SUMMARY

[0006] The present application aims to provide a Hadamard-LDPC iterative decoding method based on interference suppression. In the frequency hopping communication scenario with dynamic link quality, the decoder performs weighted processing on the received channel information based on the real-time awareness of the link quality of each frequency point, and avoids assigning higher confidence to information with poor quality, to solve the technical problem of insufficient anti-interference ability of the decoder in the frequency hopping communication scenario with dynamic link quality in the prior art.

[0007] To solve the above technical problems, the specific technical solutions of the present application are as follows:

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

[0009] Step S1: receiving information through a channel Obtain the log-likelihood ratio (LLR) value of the channel received information, and then initialize all Hadamard check nodes and decoder variable nodes according to the log-likelihood ratio (LLR) value;

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

[0011] Step S3: in the iteration, using the decoder variable node posterior LLR value and the channel received information LLR value to estimate the impulse interference power information, and calculating the impulse interference power variance;

[0012] Step S4: using the impulse interference power variance to update the channel received information LLR value; using the weighted updated channel received information LLR value to update the decoder variable node posterior LLR value, to obtain the updated decoder variable node posterior LLR value;

[0013] Step S5: Decoding decision, judging whether the maximum iteration number is met, if not, returning to step S2 to continue iteration, if yes, making decision according to the sign of the updated decoder variable node posterior LLR value, the sign is positive, decision is 1, the sign is negative, decision is 0, finally outputting the decision result.

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

[0015] Step S11: Receiving information through a channel Calculating the log-likelihood ratio (LLR) value of the channel received information, expressed as follows:

[0016]

[0017] Wherein, represents the log-likelihood ratio (LLR) value; is the noise variance, is the noise one-sided power spectral density;

[0018] Step S12: Setting the posterior LLR value of all decoder variable nodes in the 0th iteration decoding to the corresponding channel received information LLR value, the th decoder variable node is set as follows:

[0019]

[0020] Wherein, represents the posterior LLR value of the th decoder variable node in the 0th iteration decoding; represents the LLR value of the th channel received information;

[0021] Step S13: Setting the extrinsic information LLR value transmitted from the th Hadamard check node to the th decoder variable node in the 0th iteration decoding to 0, expressed as follows:

[0022]

[0023] Wherein, represents the extrinsic information LLR value transmitted from the th Hadamard check node to the th decoder variable node in the 0th iteration decoding.

[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 external 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 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 , 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 first 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 is provided in the present application, in the variable node updating process, the interference power weight is considered, and the normalization coefficient is introduced, so that the distribution of the check node external information value is not changed in the interference power weighting and linear approximation process, so that the system anti-interference ability is improved without affecting the decoding performance. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 The Hadamard-LDPC check matrix and code word composition schematic diagram of the present application.

[0050] Figure 2 The Hadamard-LDPC code interference suppression iterative decoding factor graph of the present application.

[0051] Figure 3 The Hadamard-LDPC code interference suppression iterative decoder composition diagram of the present application.

[0052] Figure 4 The variable node decoder schematic diagram of the present application.

[0053] Figure 5 The Hadamard check node decoder schematic diagram of the present application.

[0054] Figure 6 The non-adversarial frequency hopping communication scene and the traditional decoder performance comparison diagram of the present application.

[0055] Figure 7 The adversarial frequency hopping communication scene and the traditional decoder performance comparison diagram of the present application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0057] The present application aims at the problem that the confidence of received channel information is different due to different interference degrees of each frequency point in frequency hopping communication scene, and proposes a Hadamard-LDPC iterative decoding method based on interference suppression, which suppresses the received channel information according to the real-time link quality of each frequency point, and gives lower confidence to the channel information with poor quality, so as to avoid the influence of interference information on the decoder and improve the decoding performance.

[0058] For the convenience of understanding, first, the Hadamard-LDPC code is briefly described as shown in the following table: Figure 1 As shown in the table, the Hadamard-LDPC code is a linear block code which replaces the parity check constraint relationship with the Hadamard constraint relationship on the basis of the LDPC code, and the redundancy information of the Hadamard-LDPC code is divided into two parts of parity check bits and Hadamard constraint bits. The check matrix of the Hadamard-LDPC code in the present application adopts quasi-cyclic and double-diagonal structure, and the check matrix of the Hadamard-LDPC code has rows and columns, and the order of the Hadamard code is , so that the length of the information bits is , the length of the parity check bits is , and the length of the Hadamard constraint bits is , so that the length of the coded bits is . In the iterative decoding process, only the parity check bits and the information bits participate in the iterative update, and the value of the Hadamard constraint bits will not be updated, but only participate in the iteration process. In the present embodiment , , , the length of the information bits is 1024, the length of the parity check bits is 832, the length of the Hadamard constraint bits is 8320, and the length of the coded bits is .

[0059] In frequency hopping communication, the coded information bits are grouped, and each group of bits generates a pulse after modulation and frequency conversion, and each pulse uses a specific frequency hopping frequency. Due to the differences in the gain of the transmitting antenna and the radio frequency channel of different frequency points, and the different interference degrees of different frequency points in the channel, there is a phenomenon that the signal-to-noise ratios of different frequency points are inconsistent at the receiving end. The traditional decoder often considers that the confidence of all the received bits is consistent when designing, so the weight of each channel information is consistent in the iteration process, which causes performance loss. The present application aims at the scene of dynamic change of link quality of each frequency point, evaluates the interference degrees of each frequency point according to the received signal and the output signal of the current decoding judgment, and then updates the input signal according to the predicted interference power information for the next iteration update until the decoding is completed.

[0060] In the embodiment, the coded information bits are divided into pulses, each pulse contains message bits with length c, and all the pulse information forms a sending signal , , represents the kth sending signal, after propagating through a channel and being disturbed by noise, the kth channel receiving information Yk is obtained , , represents the kth channel receiving information. The relationship between the sending signal, the channel receiving information and the noise is as follows: , wherein σ2 is the noise variance, and S is the noise one-sided power spectral density. In the embodiment, the log-likelihood ratio (LLR) value of the receiving channel information is as follows: , , .

[0061] Referring to Figure 2 , the present application uses a factor graph to express the Hadamard-LDPC iterative decoding process based on interference suppression. The factor graph is a special bipartite graph model, which represents the joint probability distribution relationship between random variables. In the factor graph, all the circle nodes represent variable nodes, and the square nodes represent factor nodes. The factor nodes are connected with the variable nodes related thereto, and represent that the variable nodes satisfy certain specific relationships. The factor graph established by the present application is divided into three regions.

[0062] Region 1 establishes a probability model of the interference power, represents the kth pulse interference power, and the interference power variance of the kth pulse is , , , represents the probability distribution of the interference power of the kth pulse. Region 2 establishes the relationship between the decoder variable nodes (the aforementioned information bits and parity check bit set), the channel receiving information Y and the interference power P, and the channel joint probability distribution

[0063] Region 3 establishes the relationship between the kth channel receiving information , the kth decoder variable node and the interference power variance of the kth pulse , ​​​​​​​​​

[0064]

[0065] wherein, ( is the number of check matrix rows, and in this embodiment, 832 is taken), ( is the number of pulses, and in this embodiment, 48 is taken).

[0066] Since in the iterative decoding process, only the parity check bits and the information bits participate in the iterative update, the Hadamard constraint bit value will not be updated and will only participate in the iterative process, therefore, only the parity check bits and the information bits are processed in the region 2.

[0067] The region 3 is a Tanner graph of the Hadamard-LDPC code, describing the relationship between the decoder variable nodes and the Hadamard check nodes , and is expressed as follows:

[0068]

[0069] wherein, represents the constraint relationship between the th decoder variable node and the th Hadamard check node , ( is the number of check matrix rows, and in this embodiment, 832 is taken), ( is the number of check matrix columns, and in this embodiment, 1856 is taken). The decoder variable node , represents the th decoder variable node.

[0070] Figure 3 is the Hadamard-LDPC iterative decoding framework based on interference suppression of the present application. Different from the conventional iterative decoding method, in the process of iterative interaction between the check nodes and the variable nodes, the present application evaluates the bit interference power information according to the real-time iterative updated variable node information, and then updates the variable node information by using the interference power information, so as to suppress the confidence of the interfered bits in the iterative decoding process and reduce the influence of the interference on the decoding performance.

[0071] For the interference power variance of the th pulse, the confidence updated each time and 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 second iteration, the first Hadamard check node receives all the decoder variable nodes connected to it and passes the extrinsic information values and all the Hadamard variable node extrinsic information values , thus obtaining the first Hadamard check node posterior probability , i.e.

[0100]

[0101] wherein, represents the Hadamard code symbol-by-symbol maximum posterior probability calculation formula; represents the decoder variable node sequence number, represents the set of all the decoder variable nodes connected to the first Hadamard check node ; represents the Hadamard variable node sequence number, represents the set of all the Hadamard variable nodes connected to the first Hadamard check node (the aforementioned Hadamard constraint bits); represents the Hadamard variable node extrinsic information value.

[0102] Since the Hadamard variable nodes do not participate in the iteration, the Hadamard variable node extrinsic information value is equal to the LLR value of the channel received information, i.e.

[0103]

[0104] wherein, ( is the number of check matrix rows, and in the embodiment, 832 is taken), ( is the order of the Hadamard code, and in the embodiment, 4 is taken). Referring to Figure 5 , in the embodiment, each Hadamard check node is connected to 6 decoder variable nodes and 10 Hadamard variable nodes.

[0105] Step S23: In the second iteration, the first Hadamard check node posterior probability and the first decoder variable node extrinsic information value 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, the interference power variance of the first pulse and the average interference power of the whole code word are used to update the channel receiving information LLR value of the first decoder variable node , and the updated channel receiving information LLR value is obtained. Then, the updated channel receiving information LLR value, the last iteration updated channel receiving information LLR value and the current iteration updated a posteriori LLR value of the first decoder variable node are used to update the a posteriori LLR value of the first decoder variable node , and the updated a posteriori LLR value of the first decoder variable node is obtained.

[0114]

[0115]

[0116] The decoder weights the receiving channel information according to the real-time recognized link quality of each frequency point, and obtains the updated a posteriori probability LLR value of the variable node. The purpose of the LDPC decoder is to calculate the a posteriori probability LLR value of each decoder variable node through iteration, and then make a decision according to the sign of the a posteriori probability LLR value. If the sign is positive, the decision is 1; if the sign is negative, the decision is 0.

[0117] Step S5: Decoding decision, whether the maximum iteration number is met is determined. If not, the iteration is continued by returning to step S2; if yes, a decision is made according to the sign of the updated a posteriori LLR value of the decoder variable node. If the sign is positive, the decision is 1; if the sign is negative, the decision is 0. Finally, the decision result is output.

[0118] Referring to Figure 6 ​​​​The present application is aimed at simulating the scene that the link quality of the received signal frequency changes dynamically due to the inconsistent antenna gain of different frequencies and the inconsistent radio frequency channel gain, and the figure shows the bit error rate performance curve comparison between the traditional non-interference suppression decoding method and the interference suppression decoding method of the present application under different signal-to-noise ratio conditions, wherein the horizontal axis is the signal-to-noise ratio Eb / N0, and the vertical axis is the bit error rate BER. The simulation assumes that the scale of the received gain of different frequencies changes by 5dB. The comparison between the traditional method and the interference suppression method is tested, and it can be seen that the present application has a performance improvement of about 0.5dB compared with the traditional decoding algorithm.

[0119] Referring to Figure 7 The present application is aimed at the malicious interference scene, and the noise power is-99.5dBm, the received interference power is-84.5dBm, and the interference is 70% of the frequency. Due to the suppression of the disturbed serious bit channel information, the figure shows the bit error rate performance curve comparison between the traditional non-interference suppression decoding method and the interference suppression decoding method of the present application under different signal-to-noise ratio conditions, wherein the horizontal axis is the signal-to-noise ratio Eb / N0, and the vertical axis is the bit error rate BER. Compared with the traditional decoding algorithm, the interference suppression decoding method has a performance improvement of about 2.5dB.

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

Claims

1. A Hadamard-LDPC iterative decoding method based on interference suppression, characterized in that, The method comprises the following steps: Step S1: receiving information through a channel Log-likelihood ratio (LLR) values of the channel received information are obtained, and all Hadamard check nodes and decoder variable nodes are initialized according to the log-likelihood ratio (LLR) values. Step S2: updating the Hadamard check node information and the decoder variable node information by iteration using a variable node decoder and a Hadamard check node decoder; Step S3: in the iteration, estimating the impulse interference power information using the decoder variable node posterior LLR value and the channel receiving information LLR value, and calculating the impulse interference power variance; Step S4: updating the channel receiving information LLR value by weighting using the impulse interference power variance; updating the decoder variable node posterior LLR value using the weighted updated channel receiving information LLR value to obtain the updated decoder variable node posterior LLR value; Step S5: decoding and judging whether the maximum iteration number is met, if not, returning to step S2 for iteration, and if yes, judging according to the sign of the updated decoder variable node posterior LLR value, the sign being positive for a judgment of 1 and the sign being negative for a judgment of 0, and finally outputting the judgment result; Step S1 comprises the following steps: Step S11: Receiving information through a channel The log likelihood ratio (LLR) value of the channel received information is calculated as follows: wherein, denotes a log-likelihood ratio, LLR, value; is a noise variance, is a 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: wherein, represents the a posteriori LLR value of the 0th iteration decoding for the 0th decoder variable node ; represents the LLR value of the 0th channel received information; 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: wherein, represents the extrinsic information LLR value passed to the 0th decoder variable node from the 0th Hadamard check node at the 0th iteration decoding ​​ 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 iteration, the first Hadamard check node receives all the extrinsic variable node values passed to it from the connected decoders and all the Hadamard variable node extrinsic values , and thus obtains the first Hadamard check node extrinsic probability , i.e.: wherein, represents a Hadamard code symbol-wise maximum a posteriori probability computation formula; represents a decoder variable node sequence number, represents a set of all decoder variable nodes connected to the th Hadamard check node represents a Hadamard variable node sequence number, represents a set of all Hadamard variable nodes connected to the th Hadamard check node represents a Hadamard variable node extrinsic information value;​​ 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: 。 2. The interference- suppression-based Hadamard-LDPC iterative decoding method according to claim 1, characterized in that, In step S3, the impulse interference power variance is calculated as follows: wherein, denotes the interference power variance of the jth pulse; denotes the interference power variance of the jth pulse; denotes the length of the message bits contained in each pulse, denotes the set of all received information using the jth pulse.

3. The interference- suppression based Hadamard-LDPC iterative decoding method according to claim 2, 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 first 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: wherein denotes the average interference power of the entire code word.

4. The interference- suppression-based Hadamard-LDPC iterative decoding method according to claim 3, characterized in that, The average interference power of the entire code word is calculated as follows: wherein represents the number of pulses.

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