A polar code blind recognition decoding method and device for a non-cooperative communication scenario

Abstract

This invention discloses a method and apparatus for blind polar code identification and decoding in non-cooperative communication scenarios, relating to the field of channel coding parameter identification technology in non-cooperative communication. The method first acquires multiple sets of polar code received observation sequences and constructs a channel LLR matrix; initializes a candidate path set and corresponding state variables; calculates the decision LLR for each surviving path bit-by-bit; constructs a dual hypothesis of frozen bits and information bits for each path and expands candidate paths, updating the path metric; after sorting, retains a preset number of superior paths; finally, outputs the information set identification result based on the optimal path. This invention deeply integrates SCL decoding and information set identification, fully utilizing the soft information of the received signal, significantly improving the accuracy, stability, and robustness of polar code information set identification under low signal-to-noise ratio and complex channel conditions, and is suitable for non-cooperative communication scenarios.

Description

Technical Field

[0001] This invention relates to the field of channel coding parameter identification technology in non-cooperative communication, specifically to a method and apparatus for blind identification and decoding of polar codes in non-cooperative communication scenarios. It is applicable to blind identification and decoding of information sets with known polar code lengths in non-cooperative communication scenarios such as spectrum monitoring, electronic reconnaissance, unauthorized access, and intelligent reception. Background Technology

[0002] With the large-scale commercialization of 5G mobile communication systems and the in-depth research and development of 6G technology, modern communication systems not only place higher demands on transmission rates, spectral efficiency, and connection scale, but also impose stringent standards on signal perception, blind parameter acquisition, and intelligent processing capabilities in complex electromagnetic environments. In non-cooperative communication scenarios such as spectrum monitoring, electronic reconnaissance, unauthorized access detection, and intelligent blind reception, the receiving end cannot know the channel coding parameters of the transmitting end in advance. It must blindly identify the coding scheme and core parameters through the intercepted received signal. This is the fundamental prerequisite for subsequent signal demodulation, decoding recovery, and communication behavior analysis.

[0003] Polar codes, as the first channel coding scheme theoretically proven to asymptotically achieve Shannon channel capacity in symmetric binary discrete memoryless channels, possess core advantages such as regular coding structure, low encoding / decoding complexity, and a complete theoretical foundation. They have been adopted by 3GPP standards as the standard coding scheme for 5G control channels and are also a candidate coding technology for 6G mobile communication systems. Their application in civilian and dedicated communication fields continues to expand, and the corresponding blind identification technology for polar code parameters has become a research hotspot in non-cooperative communication. Among these, blind identification of the information set is the core step in polar code parameter identification, and its accuracy directly determines the subsequent decoding performance and the overall reliability of parameter recovery.

[0004] Currently, mainstream blind identification schemes for polar code information sets are mainly divided into two categories. One category is based on hard decision results and matrix structure analysis to achieve parameter identification. Although the implementation logic is simple and the engineering adaptability is strong, it cannot fully utilize the soft information and reliability information of the received signal. In low signal-to-noise ratio and strong noise environments, effective information loss is likely to occur, and the identification accuracy and stability will drop significantly. The other category introduces soft decision information to optimize the identification effect, but still relies on fixed threshold decision, code length traversal and dual space verification. It does not deeply integrate information set identification with polar code decoding algorithms, and the identification process is complex and redundant. In non-cooperative scenarios with small received sample size and complex channel conditions, the identification robustness and execution efficiency still have obvious shortcomings, and it cannot meet the engineering application requirements in complex electromagnetic environments. Summary of the Invention

[0005] Therefore, the present invention provides a polar code blind identification and decoding method and apparatus for non-cooperative communication scenarios to solve or partially solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A first aspect provides a polar code blind identification and decoding method for non-cooperative communication scenarios, comprising:

[0007] S1. Obtain multiple sets of polar code received observation sequences to be identified, and construct a channel log-likelihood ratio (LLR) matrix based on the received observation sequences; S2. Initialize the candidate path set based on the channel log-likelihood ratio (LLR) matrix, as well as the cumulative path metric, path label, recursive LLR state, and partial sum information corresponding to each candidate path, to form the initial identification state; S3. According to the order of the source bit positions, for all currently alive candidate paths, calculate the decision LLR corresponding to the current bit position for each path based on the recursive LLR state and partial sum information of each path. S4. For each live path, based on the decision LLR corresponding to the current bit position, construct the freeze bit hypothesis and the information bit hypothesis respectively, expand to generate two candidate paths, and calculate the path metric increment of the corresponding hypothesis respectively, and update the cumulative path metric of each candidate path. S5. Sort all expanded candidate paths according to cumulative path metric, retain a preset number of paths as surviving paths, and feed back the status information of the surviving paths to step S3 to proceed to the identification process of the next bit position. S6. After processing all bit positions, select the candidate path with the best cumulative metric among the final surviving paths, and output the blind identification result of the polar code information set based on the path label of the best candidate path.

[0008] As a preferred solution for a polar code blind identification and decoding method for non-cooperative communication scenarios, step S1 specifically includes: Obtain M sets of polar code received observation sequences after transmission through the channel. The length of each set of observation sequences is the polar code length N. For each received symbol in each set of observation sequences, the corresponding channel LLR is calculated based on the received value and noise variance, and an M×N dimension input soft information matrix is ​​constructed, where M is the number of sets of received observation sequences and N is the polar code length.

[0009] As a preferred solution for a polar code blind identification and decoding method for non-cooperative communication scenarios, step S2 specifically includes: Activate one initial candidate path and initialize the cumulative path metric of that initial candidate path to 0; Configure corresponding path labels, recursive LLR status, and partial sum information for the initial candidate paths; The path label is used to record the result of each bit position being determined as a frozen bit or an information bit. The recursive LLR state is used to save the intermediate LLR information of each layer node during the decoding recursion process. The partial information is used to support the temporary bit feedback in the decoding recursion operation.

[0010] As a preferred solution for a polar code blind identification and decoding method for non-cooperative communication scenarios, step S3 specifically includes: For each candidate survival path at the current bit position, based on the recursive LLR state and partial sum information of the path, a recursive update mechanism of polar code serial elimination list (SCL) decoding is adopted, and the decision LLR corresponding to the current bit position is calculated layer by layer through f and g operations. For each group of received observation sequences, the recursive calculation is performed to obtain M decision LLR results corresponding to the current bit position of the current path.

[0011] As a preferred scheme for a polar code blind identification and decoding method for non-cooperative communication scenarios, the calculation formulas for the f operation and the g operation are as follows:

[0012]

[0013] In the formula, : The precise update function for the left node of the polar code serial cancellation decoding; : Right node update function for polar code serial cancellation decoding; Hyperbolic tangent function; : Inverse hyperbolic tangent function; , The log-likelihood ratio (LLR) of the two inputs in a recursive operation; : The bit value that has been decided.

[0014] As a preferred solution for a polar code blind identification and decoding method for non-cooperative communication scenarios, step S4 specifically includes: For each live path at the current bit position, expand the frozen bit assumption branch and the information bit assumption branch respectively: Frozen bit assumption branch: Assuming the current bit position is frozen, the temporary decision value of this position in all received observation sequences is uniformly set to 0. Based on the M decision LLRs corresponding to the current bit position, the average path metric increment corresponding to the frozen bit assumption is calculated. Information bit assumption branch: Assuming the current bit position is an information bit, the temporary decision value of the corresponding received observation sequence is determined according to the symbol of each decision LLR, and a reference path metric is introduced as the path metric increment corresponding to the information bit assumption; The path metric increments corresponding to the two branches are added to the cumulative path metric of the original surviving path to generate two candidate paths corresponding to the freeze bit hypothesis and the information bit hypothesis, respectively.

[0015] As a preferred solution for a polar code blind identification and decoding method for non-cooperative communication scenarios, the formula for calculating the average path metric increment corresponding to the frozen bit assumption is as follows:

[0016] The formula for calculating the average path metric increment corresponding to the information bit assumption is as follows:

[0017] In the formula, : The average path metric increment corresponding to the frozen position assumption; The information bit assumption corresponds to the path metric increment; Increment symbol; : The total number of received observation sequences; Summation operator, lower limit of summation Upper limit ; : No. The group received the observation sequence in the first The decision log-likelihood ratio (LLR) for each bit position; To receive sequence number variables, This is a bit position index variable.

[0018] As a preferred solution for a polar code blind identification and decoding method for non-cooperative communication scenarios, step S5 specifically includes: Sort all candidate paths obtained by expanding the current bit position in ascending order of cumulative path metric; Before keeping The candidate path with the smallest cumulative path metric is selected as the surviving path and proceeds to the identification process for the next bit position. This is the preset list size; Synchronously update the path labels, recursive LLR status, and partial sum information corresponding to each surviving path.

[0019] As a preferred solution for a polar code blind identification and decoding method for non-cooperative communication scenarios, step S6 specifically includes: The candidate path with the smallest cumulative path metric among the final surviving paths is selected as the optimal path. Based on the path label sequence of the optimal path, the bit positions marked as information bits are included in the identified information set, and the bit positions marked as frozen bits are included in the frozen set. The final blind identification result of the polar code information set is then output.

[0020] In a second aspect, the present invention provides a polar code blind identification and decoding apparatus for non-cooperative communication scenarios, employing the polar code blind identification and decoding method for non-cooperative communication scenarios as described in the first aspect or any possible implementation thereof, comprising: The receiving data processing module is used to acquire multiple sets of polar code receiving observation sequences to be identified, and to construct a channel log-likelihood ratio (LLR) matrix based on the receiving observation sequences. The initialization module is used to initialize the candidate path set based on the channel log-likelihood ratio (LLR) matrix, as well as the cumulative path metric, path label, recursive LLR state and partial sum information corresponding to each candidate path, to form an initial identification state. The recursive decision calculation module is used to calculate the decision LLR corresponding to the current bit position for each of the current live candidate paths in the order of the source bit positions, based on the recursive LLR state and partial sum information of each path. The dual-hypothesis path expansion module is used to construct a frozen bit hypothesis and an information bit hypothesis for each live path based on the decision LLR corresponding to the current bit position, expand and generate two candidate paths, calculate the path metric increment of the corresponding hypothesis, and update the cumulative path metric of each candidate path. The path filtering module is used to sort all the expanded candidate paths according to the cumulative path metric, retain a preset number of paths as surviving paths, and feed back the status information of the surviving paths to the recursive decision calculation module to enter the identification processing of the next bit position. The identification output module is used to select the candidate path with the best cumulative metric among the final surviving paths after processing all bit positions, and output the blind identification result of the polar code information set based on the path label of the best candidate path.

[0021] Thirdly, the present invention provides an electronic device, including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein when the processor executes the program or instructions, it implements the first aspect or any possible implementation thereof, a polar code blind identification and decoding method for non-cooperative communication scenarios.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing a program or instructions, wherein when the program or instructions are executed by a processor, the program or instructions implement the steps of the first aspect or any possible implementation thereof, the polar code blind identification and decoding method for non-cooperative communication scenarios.

[0023] The present invention has the following advantages: First, this invention deeply integrates the recursive processing mechanism of polar code SCL decoding with the blind identification process of information set, and completes the discrimination of information set while decoding bit by bit. It makes full use of the soft information and reliability information in the received signal. Compared with hard decision methods, it significantly reduces information loss in low signal-to-noise ratio environments and significantly improves the recognition accuracy.

[0024] Second, the present invention constructs a dual-branch path extension mechanism that simultaneously constructs a frozen bit hypothesis and an information bit hypothesis at each bit position, and performs path metric calculation by combining the statistical characteristics of multiple sets of received sequences. This avoids the dependence of existing technologies on fixed threshold decisions, simplifies the identification process, and at the same time eliminates the natural bias of information bit or frozen bit hypothesis by introducing a reference metric, thereby improving the reliability of the identification results.

[0025] Third, the present invention employs a multi-path search and optimal retention mechanism for SCL decoding, which retains a preset number of better paths after each bit is processed, effectively suppressing the cumulative spread of local misjudgments, avoiding the loss of correct paths when early decisions are unreliable, and significantly improving the stability and robustness of identification in low signal-to-noise ratio, small number of received samples, and complex channel environments.

[0026] Fourth, the technical solution of the present invention is adapted to the application requirements of non-cooperative communication scenarios. It does not require prior knowledge of the information set parameters of the sending end, and can complete the high-precision blind identification of the polar code information set only through the intercepted received signal, providing reliable support for decoding recovery and communication behavior analysis, and has strong engineering practicality. Attached Figure Description

[0027] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0028] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0029] Figure 1 This is a schematic diagram of the polar code blind identification and decoding method for non-cooperative communication scenarios provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the technical route of the polar code blind identification and decoding method for non-cooperative communication scenarios provided in the embodiments of the present invention; Figure 3 This is a schematic diagram of the architecture of a polar code blind identification and decoding device for non-cooperative communication scenarios provided in an embodiment of the present invention; Figure 4 This is an electronic device architecture diagram provided in an embodiment of the present invention. Detailed Implementation

[0030] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0031] With the large-scale commercialization of fifth-generation mobile communication systems (5G) and the continuous evolution of sixth-generation mobile communication systems (6G), modern communication systems not only place higher demands on transmission rates, spectral efficiency, and connection scale, but also impose more stringent requirements on signal perception, parameter acquisition, and intelligent processing capabilities in complex electromagnetic environments. Especially in application scenarios such as spectrum monitoring, electronic reconnaissance, unlicensed access, intelligent reception, and non-cooperative communication, the receiver typically cannot know in advance the channel coding parameters used by the transmitter. Therefore, it is necessary to rely on the analysis of intercepted signals to achieve blind identification of the coding scheme and related parameters, thereby providing a foundation for subsequent signal demodulation, decoding recovery, and communication behavior analysis.

[0032] Polar codes are the first channel coding schemes theoretically proven to asymptotically achieve Shannon channel capacity in symmetric binary discrete memoryless channels. They possess advantages such as regular coding structure, complete theoretical foundation, and low encoding / decoding complexity. They have been adopted by 3GPP standards as the coding scheme for 5G control channels and are considered one of the important channel coding candidates for future mobile communication systems. With the widespread application of polar codes in modern communication systems, research surrounding polar code parameter identification and blind identification has gradually attracted attention. Among these, the identification of the information set is one of the key issues in polar code blind identification, and its accuracy directly affects the subsequent decoding performance and parameter recovery results.

[0033] However, in non-cooperative communication scenarios, the receiver typically only receives the signal transmitted through the channel and cannot know in advance the coding parameters of the polar code, such as the code length, code rate, and information set. Existing blind polar code identification methods are mainly divided into two categories, both of which have significant technical shortcomings: The first category is blind polar code identification methods based on hard decision, with a typical scheme being a blind polar code parameter identification method based on information matrix estimation. This method is designed for non-cooperative signal processing scenarios. It utilizes the structural characteristics of the polar code generator matrix to construct a codeword matrix from the intercepted codeword bitstream, and obtains the estimated information matrix through matrix transformation. Based on this, it analyzes the distribution characteristics of each column element in the matrix to identify the polar code length, the number of information bits, and the distribution of information bit positions. For cases with bit errors, it further introduces zero-mean ratio measurement and combines it with threshold decision to complete the identification of relevant parameters. While this method is simple to implement and has strong engineering capabilities, it relies entirely on hard decision results from the received bitstream, failing to fully utilize the soft and reliability information in the received signal. Under strong noise conditions, it is prone to information loss, affecting the accuracy and stability of identification. Furthermore, it has a certain dependence on statistical feature differentiation and threshold decision, and there is still room for improvement in robustness to identification in complex channel environments.

[0034] The second category is blind polar code identification methods based on soft decision, with a typical scheme being a non-pruned polar code parameter identification method based on soft decision. This method addresses the problem of blind polar code parameter identification. Based on the coding structure of polar codes, it constructs codeword matrices and Kronecker matrices by traversing possible code length values. It then models the parameter identification process by combining the polar code code length, code rate, and the relationship between information bits and frozen bits. Furthermore, it introduces the log-likelihood ratio and utilizes the statistical characteristics of the soft decision sequence and corresponding decision criteria to distinguish the positions of information bits and frozen bits under the traversed code length, thereby achieving the identification of the polar code code length and information bit positions. Although this method introduces soft decision information, improving the utilization of received signal reliability information, it still mainly relies on code length traversal, dual space check relationship detection, and threshold decision. The identification process is relatively complex, and it does not deeply integrate the information set identification process with the polar code decoding algorithm. Therefore, there is still room for improvement in identification efficiency and robustness under complex channel conditions.

[0035] Furthermore, the Successive Cancellation List (SCL) decoding of polar codes can improve decoding performance while retaining multiple candidate paths. Its path search and metric update mechanism provides a new approach to the discrimination of information sets. However, existing technologies have not yet organically combined the multi-path search mechanism of SCL decoding with the blind recognition process of polar code information sets. As a result, the multi-path retention mechanism cannot suppress the cumulative spread of local misjudgments, which can easily lead to the failure of the overall recognition result due to early misjudgments.

[0036] In view of this, in order to solve the problems of insufficient utilization of soft information, disconnect between recognition and decoding processes, and insufficient recognition accuracy and robustness under low signal-to-noise ratio in existing polar code blind recognition, this invention organically combines the multi-path search mechanism of polar code SCL decoding with the information set recognition process. The information set blind recognition problem is transformed into a multi-path search and discrimination problem based on SCL decoding. During bit-by-bit processing, dual hypotheses of frozen bits and information bits are constructed simultaneously. Path measurement updates and filtering are performed by combining the statistical characteristics of soft information from multiple sets of received sequences. Finally, the information set recognition result is obtained through the optimal path, making full use of received soft information and improving recognition accuracy and robustness. The following are the specific contents of the embodiments of this invention.

[0037] See Figure 1 and Figure 2 This invention provides a blind polar code identification and decoding method for non-cooperative communication scenarios, comprising: S1. Obtain multiple sets of polar code received observation sequences to be identified, and construct a channel log-likelihood ratio (LLR) matrix based on the received observation sequences.

[0038] Specifically, in step S1, a polar code with code length N and information bit count K is used as an example for explanation. The transmitter generates the corresponding polar code information set and frozen set according to a preset construction method, and sets the frozen bits to 0. BPSK modulation is used, and the channel model is AWGN channel. The receiver intercepts M groups of receive vectors at a time; the list size is set to... This is used to retain better candidate paths during the identification process.

[0039] The transmitter randomly generates K information bits and fills them into a source bit vector of length N according to a preset information set. At the same time, the bits at the corresponding positions in the frozen set are set to 0, thus obtaining a complete source bit sequence. Subsequently, the source bit sequence is encoded using a polar code generator matrix to obtain codewords. Then type the words. BPSK modulation is performed to obtain the transmitted symbol sequence. The above process is repeated M times to obtain M sets of corresponding modulation symbol sequences. These M sets of modulation symbol sequences are then transmitted through an AWGN channel to obtain the corresponding received signal sequences. For each received symbol, its channel log-likelihood ratio is calculated based on the received value and the noise variance. ,in Indicates the first Group receive vector, Indicates the first in the codeword Each bit position. All The log-likelihood ratios of the group of received vectors at each bit position are arranged in a row-column manner to form a... The input soft information matrix is ​​used as the input to the blind recognition algorithm.

[0040] S2. Initialize the candidate path set based on the channel log-likelihood ratio (LLR) matrix, as well as the cumulative path metric, path label, recursive LLR state, and partial sum information corresponding to each candidate path, to form the initial identification state.

[0041] Specifically, in step S2, before identification begins, an initial candidate path is activated, and its cumulative path metric is initialized to 0. Simultaneously, a corresponding path label, recursive LLR state, and partial sum information are established for this initial path. The path label records the determination result of whether each bit position is assumed to be a frozen bit or an information bit; the recursive LLR state stores the intermediate log-likelihood ratio information of each layer node during the serial elimination list decoding process; and the partial sum information supports subsequent... Temporary feedback during computation. The aforementioned state variables will be dynamically updated during the bit-by-bit identification process.

[0042] S3. According to the order of the source bit positions, for all currently alive candidate paths, calculate the decision LLR corresponding to the current bit position for each path based on the recursive LLR state and partial sum information corresponding to each path.

[0043] Specifically, in step S3, starting from the 0th source bit position, all currently alive paths are processed one by one. For each alive path, based on its currently stored recursive LLR state and partial sum information, a recursive update method consistent with serial elimination list decoding is adopted. The decision log-likelihood ratio corresponding to the current bit position is calculated layer by layer through f and g operations. Specifically, the above recursive calculation is performed on each received vector to obtain the M decision log-likelihood ratios corresponding to the current bit position of the current path, providing a basis for the dual-hypothesis path expansion.

[0044] In one possible embodiment, the calculation formulas for the f operation and the g operation are respectively:

[0045]

[0046] In the formula, : The precise update function for the left node of the polar code serial cancellation decoding; : Right node update function for polar code serial cancellation decoding; Hyperbolic tangent function; : Inverse hyperbolic tangent function; , The log-likelihood ratio (LLR) of the two inputs in a recursive operation; : The bit value that has been decided.

[0047] Specifically, The operation is used to update the LLR value of the left node, and its output is determined by the sign and reliability of the two input LLRs. The operation is used to update the LLR value of the right node, and its result depends not only on the input LLR but also on the decided bit value. They are related. Together, they complete the recursive transfer of soft information between different nodes during the polar code decoding process.

[0048] S4. For each live path, based on the decision LLR corresponding to the current bit position, construct the freeze bit hypothesis and the information bit hypothesis respectively, expand to generate two candidate paths, calculate the path metric increment of the corresponding hypothesis respectively, and update the cumulative path metric of each candidate path.

[0049] Specifically, in step S4, for the current bit position This invention does not predetermine whether the position is a frozen position or an information position. Instead, it constructs both a frozen position hypothesis and an information position hypothesis for each surviving path and generates corresponding candidate paths for each.

[0050] Under the frozen bit assumption, the current bit position is assumed to be a frozen bit, and the temporary decision value of this position on all receive vectors is uniformly set to 0. Subsequently, based on the current bit position in all... The decision log-likelihood ratio on the received vectors is used to calculate the average path metric increment corresponding to the freeze bit assumption. This average path metric increment reflects the consistency of the current bit position in satisfying the freeze bit constraint; the more a position conforms to the freeze bit characteristics, the smaller its corresponding average path metric is usually. Under the information bit assumption, the current bit position is assumed to be an information bit. In this case, for each received vector, a temporary decision value can be determined according to the sign of the current decision log-likelihood ratio: when the decision log-likelihood ratio is greater than or equal to 0, the temporary decision value is 0; when the decision log-likelihood ratio is less than 0, the temporary decision value is 1. At the same time, to avoid the natural bias to the information bit assumption caused by directly using the traditional information bit branch metric, this embodiment introduces a reference path metric for the information bit assumption and uses it as the path metric increment under the information bit assumption.

[0051] In one possible embodiment, the formula for calculating the average path metric increment corresponding to the frozen position assumption is:

[0052] The formula for calculating the average path metric increment corresponding to the information bit assumption is as follows:

[0053] In the formula, : The average path metric increment corresponding to the frozen position assumption; The information bit assumption corresponds to the path metric increment; Increment symbol; : The total number of received observation sequences; Summation operator, lower limit of summation Upper limit ; : No. The group received the observation sequence in the first The decision log-likelihood ratio (LLR) for each bit position; To receive sequence number variables, This is a bit position index variable.

[0054] Specifically, in the frozen position hypothesis metric, This can be seen as a metric penalty resulting from the freeze-bit decision: when LLR is more consistent with a freeze-bit value of 0, this term is smaller; when LLR is inconsistent with a freeze-bit value, this term increases, thus worsening the metric for the corresponding path. Then, by... Averaging the values ​​of all samples yields the average penalty for classifying a position as a frozen bit. Under the information bit assumption, the source bit has two possible values: 0 and 1. To ensure a fairer penalty for information bits, the path metric when LLR=0 is used as the penalty. .

[0055] S5. Sort all expanded candidate paths according to cumulative path metric, retain a preset number of paths as surviving paths, and feed back the status information of the surviving paths to step S3 to proceed to the identification process of the next bit position.

[0056] Specifically, in step S5, for each survival path, the path metric increment corresponding to the freeze bit hypothesis and the path metric increment corresponding to the information bit hypothesis are added to the original cumulative path metric of that path, thereby forming two new candidate paths. In other words, at the current bit position, each original survival path is expanded into two candidate paths, corresponding to the freeze bit hypothesis and the information bit hypothesis, respectively. Then, all candidate paths generated at the current bit position are sorted in ascending order of cumulative path metric, and the top-ranked paths are retained. Candidate paths with smaller cumulative path metrics are selected as new surviving paths and moved to the next bit position for further identification.

[0057] While completing path sorting and retention, it is also necessary to synchronously update the path labels, recursive LLR states, and partial sum information of the corresponding surviving paths. Specifically, the path label records whether the current bit position is determined to be a frozen bit or an information bit under that path; the recursive LLR state is used to continue supporting the decision log-likelihood ratio calculation for the next bit position; and the partial sum information is used to complete the next stage. Temporary bit feedback required for computation. Through the above processing, the multipath search process can be guaranteed to continue stably throughout the entire code length.

[0058] S6. After processing all bit positions, select the candidate path with the best cumulative metric among the final surviving paths, and output the blind identification result of the polar code information set based on the path label of the best candidate path.

[0059] In step S6, the process is repeated starting from the 0th bit position until the 1st bit position is completed. The processing of each bit position involves several steps. As the bit positions progress, each surviving path gradually forms a complete path label sequence, with each path label sequence corresponding to a candidate information set pattern. Because this invention simultaneously constructs a frozen bit hypothesis and an information bit hypothesis at each bit position and combines this with path metrics for optimal retention, it can effectively suppress the cumulative spread of local misjudgments during the identification process, improving the overall stability of the identification. After all N bit positions have been processed, the candidate path with the smallest cumulative path metric is selected from all the remaining surviving paths, and the path label sequence corresponding to this candidate path is taken as the final identification result. Specifically, if a bit position is marked as an information bit in the optimal path label sequence, it is determined as the identified information bit position; if a bit position is marked as a frozen bit, it is determined as the identified frozen bit position. This allows the recovery of the polar code's information set estimation result.

[0060] The application scenarios of this invention are as follows: Military electronic reconnaissance and intelligence decryption scenarios: In battlefield environments, reconnaissance forces cannot obtain core prior coding parameters such as the polar code information set and code rate of the enemy's communication system, representing a typical scenario of strong non-cooperative communication. Simultaneously, the complex electromagnetic environment of the battlefield is characterized by strong interference, multipath fading, and low signal-to-noise ratio (SNR) of the received signal. Existing hard-decision blind identification methods suffer severe loss of effective information, while soft-decision methods lack robustness, failing to achieve rapid identification and decoding of enemy communication signals, directly impacting intelligence gathering efficiency. This invention requires no prior coding information; it can achieve high-precision blind identification of polar code information sets solely based on intercepted short-term multiple sets of received signals. Through the SCL multipath retention mechanism, it suppresses the spread of local misjudgments, maintaining high identification accuracy even in low SNR and strong interference environments. Furthermore, it deeply integrates the identification and decoding processes, directly outputting the decoding result upon identification completion, significantly reducing signal processing latency. It can be integrated into airborne, shipborne, and handheld reconnaissance terminals, enabling real-time reconnaissance, identification, and decryption of enemy polar code communication signals on the battlefield, significantly enhancing military intelligence gathering capabilities.

[0061] Civil radio spectrum monitoring and illegal signal supervision scenarios: Regulatory authorities lack prior information on the encoding of illegally transmitted unknown polar code signals, placing this in a non-cooperative regulatory scenario. Urban environments suffer from multipath effects due to building obstruction, co-channel and adjacent-channel interference, and significant fluctuations in the received signal-to-noise ratio. Existing identification methods suffer from insufficient accuracy and complex processes, hindering rapid identification, demodulation, and decoding of the encoding parameters of illegal signals. This makes it difficult to pinpoint the signal source and illegal purpose, limiting the efficiency of spectrum enforcement. This invention can be deployed at fixed spectrum monitoring stations, mobile monitoring vehicles, and UAV monitoring platforms to perform real-time blind identification of intercepted unknown polar code signals. It rapidly acquires core encoding parameters such as information set and code rate, simultaneously demodulating and decoding the signal to accurately identify the transmitting entity, service type, and compliance. Maintaining high robustness even in complex urban electromagnetic environments, it effectively identifies low-power, short-duration, and sudden illegal signals, significantly improving the efficiency and coverage of radio spectrum regulation.

[0062] 5G / 6G mobile communication network unauthorized access detection and security protection scenarios: Malicious terminals can gain unauthorized network access by forging polar code encoding parameters and using non-standard information sets, thereby launching malicious acts such as network attacks and data theft. Existing network-side security solutions can only verify the encoding parameters specified by standard protocols, and cannot quickly identify and intercept polar code access signals with unknown information sets, resulting in serious network security vulnerabilities. At the same time, base station access signals are subject to multi-user interference and channel fading, and the real-time performance and accuracy of existing identification methods cannot meet the deployment requirements of the current network. This invention can be integrated into the access network unit and core network security gateway of 5G / 6G base stations to perform real-time blind identification of uplink access polar code signals, quickly reverse-engineer the information set parameters of the signal, compare them with the legal encoding parameters specified by the 3GPP standard protocol, accurately identify malicious unauthorized access signals with non-standard encoding, and promptly block illegal access links. The algorithm's computational complexity is controllable, meeting the millisecond-level real-time processing requirements of the current network, and can be quickly deployed without modifying the existing network architecture, significantly improving the security protection capabilities of 5G / 6G mobile communication networks.

[0063] It should be noted that the method of this embodiment can also be applied to distributed scenarios, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the described polar code blind identification and decoding method for non-cooperative communication scenarios.

[0064] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] See Figure 3 This invention also provides a polar code blind identification and decoding device for non-cooperative communication scenarios, and a polar code blind identification and decoding method for non-cooperative communication scenarios using the above embodiments or any possible implementation thereof, including: The receiving data processing module 001 is used to acquire multiple sets of polar code receiving observation sequences to be identified, and to construct a channel log-likelihood ratio (LLR) matrix based on the receiving observation sequences. Initialization module 002 is used to initialize the candidate path set based on the channel log-likelihood ratio (LLR) matrix, as well as the cumulative path metric, path label, recursive LLR state and partial sum information corresponding to each candidate path, to form an initial identification state. The recursive decision calculation module 003 is used to calculate the decision LLR corresponding to the current bit position for each of the current live candidate paths according to the order of the source bit positions, based on the recursive LLR state and partial sum information of each path. The dual-hypothesis path expansion module 004 is used to construct a frozen bit hypothesis and an information bit hypothesis for each live path based on the decision LLR corresponding to the current bit position, expand and generate two candidate paths, calculate the path metric increment of the corresponding hypothesis, and update the cumulative path metric of each candidate path. The path filtering module 005 is used to sort all the expanded candidate paths according to the cumulative path metric, retain a preset number of paths as surviving paths, and feed back the status information of the surviving paths to the recursive decision calculation module to enter the identification processing of the next bit position. The identification output module 006 is used to select the candidate path with the best cumulative metric among the final survival paths after processing all bit positions, and output the blind identification result of the polar code information set based on the path label of the best candidate path.

[0066] The system described in the above embodiments is used to implement the polar code blind identification and decoding method for non-cooperative communication scenarios in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0067] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides an electronic device, including 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 polar code blind identification and decoding method for non-cooperative communication scenarios described in any of the above embodiments.

[0068] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 710, a memory 720, an input / output interface 730, a communication interface 740, and a bus 750. The processor 710, memory 720, input / output interface 730, and communication interface 740 are interconnected internally via the bus 750.

[0069] The processor 710 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0070] The memory 720 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 720 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 720 and is called and executed by the processor 710.

[0071] The input / output interface 730 is used to connect input / output modules to enable information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0072] The communication interface 740 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0073] Bus 750 includes a pathway for transmitting information between various components of the device, such as processor 710, memory 720, input / output interface 730, and communication interface 740.

[0074] It should be noted that although the above-described device only shows the processor 710, memory 720, input / output interface 730, communication interface 740, and bus 750, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0075] The electronic devices described above are used to implement the polar code blind identification and decoding method for non-cooperative communication scenarios in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0076] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, which are used to cause the computer to execute the polar code blind identification and decoding method for non-cooperative communication scenarios as described in any of the above embodiments.

[0077] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0078] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the polar code blind identification and decoding method for non-cooperative communication scenarios as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0079] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the embodiments of the invention as described above, which are not provided in detail for the sake of brevity.

[0080] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of the invention, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of the invention, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of the invention will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of the invention, it will be apparent to those skilled in the art that the embodiments of the invention may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0081] Although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., DRAM) may use the embodiments discussed.

[0082] The embodiments of this invention are intended to cover all such substitutions, modifications, and variations falling within the scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this invention should be included within the scope of protection of this invention.

Claims

1. A method for blind identification decoding of polar codes for non-cooperative communication scenarios, characterized in that, include: S1. Obtain multiple sets of polar code received observation sequences to be identified, and construct a channel log-likelihood ratio (LLR) matrix based on the received observation sequences; S2. Initialize the candidate path set based on the channel log-likelihood ratio (LLR) matrix, as well as the cumulative path metric, path label, recursive LLR state, and partial sum information corresponding to each candidate path, to form the initial identification state; S3. According to the order of the source bit positions, for all currently alive candidate paths, calculate the decision LLR corresponding to the current bit position for each path based on the recursive LLR state and partial sum information of each path. S4. For each live path, based on the decision LLR corresponding to the current bit position, construct the freeze bit hypothesis and the information bit hypothesis respectively, expand to generate two candidate paths, and calculate the path metric increment of the corresponding hypothesis respectively, and update the cumulative path metric of each candidate path. S5. Sort all expanded candidate paths according to cumulative path metric, retain a preset number of paths as surviving paths, and feed back the status information of the surviving paths to step S3 to proceed to the identification process of the next bit position. S6. After processing all bit positions, select the candidate path with the best cumulative metric among the final surviving paths, and output the blind identification result of the polar code information set based on the path label of the best candidate path.

2. The method of claim 1, wherein, Step S1 specifically includes: Obtain M sets of polar code received observation sequences after transmission through the channel. The length of each set of observation sequences is the polar code length N. For each received symbol in each set of observation sequences, the corresponding channel LLR is calculated based on the received value and noise variance, and an M×N dimension input soft information matrix is ​​constructed, where M is the number of sets of received observation sequences and N is the polar code length.

3. The method of claim 1, wherein, Step S2 specifically includes: Activate one initial candidate path and initialize the cumulative path metric of that initial candidate path to 0; Configure corresponding path labels, recursive LLR status, and partial sum information for the initial candidate paths; The path label is used to record the result of each bit position being determined as a frozen bit or an information bit. The recursive LLR state is used to save the intermediate LLR information of each layer node during the decoding recursion process. The partial information is used to support the temporary bit feedback in the decoding recursion operation.

4. The method of claim 1, wherein, Step S3 specifically includes: For each candidate survival path at the current bit position, based on the recursive LLR state and partial sum information of the path, a recursive update mechanism of polar code serial elimination list (SCL) decoding is adopted, and the decision LLR corresponding to the current bit position is calculated layer by layer through f and g operations. For each group of received observation sequences, the recursive calculation is performed to obtain M decision LLR results corresponding to the current bit position of the current path.

5. The method of claim 4, wherein, The calculation formulas for the f operation and the g operation are as follows: In the formula, : The precise update function for the left node of the polar code serial cancellation decoding; : Right node update function for polar code serial cancellation decoding; Hyperbolic tangent function; : Inverse hyperbolic tangent function; , The log-likelihood ratio (LLR) of the two inputs in a recursive operation; : The bit value that has been decided.

6. The method of claim 1, wherein, Step S4 specifically includes: For each live path at the current bit position, expand the frozen bit assumption branch and the information bit assumption branch respectively: Frozen bit assumption branch: Assuming the current bit position is frozen, the temporary decision value of this position in all received observation sequences is uniformly set to 0. Based on the M decision LLRs corresponding to the current bit position, the average path metric increment corresponding to the frozen bit assumption is calculated. Information bit assumption branch: Assuming the current bit position is an information bit, the temporary decision value of the corresponding received observation sequence is determined according to the symbol of each decision LLR, and a reference path metric is introduced as the path metric increment corresponding to the information bit assumption; The path metric increments corresponding to the two branches are added to the cumulative path metric of the original surviving path to generate two candidate paths corresponding to the freeze bit hypothesis and the information bit hypothesis, respectively.

7. The method of claim 6, wherein, The formula for calculating the average path metric increment corresponding to the freeze position assumption is as follows: The formula for calculating the average path metric increment corresponding to the information bit assumption is as follows: In the formula, : The average path metric increment corresponding to the frozen position assumption; The information bit assumption corresponds to the path metric increment; Increment symbol; : The total number of received observation sequences; Summation operator, lower limit of summation Upper limit ; : No. The group received the observation sequence in the first The decision log-likelihood ratio (LLR) for each bit position; To receive sequence number variables, This is a bit position index variable.

8. The method of claim 1, wherein, Step S5 specifically includes: Sort all candidate paths obtained by expanding the current bit position in ascending order of cumulative path metric; before reservation The candidate path with the minimum accumulated path metric is retained as the surviving path, and the identification process proceeds to the next bit position, wherein, is a predetermined list size; Synchronously update the path labels, recursive LLR status, and partial sum information corresponding to each surviving path.

9. The method according to claim 1, wherein step S6 specifically includes: The candidate path with the smallest cumulative path metric among the final surviving paths is selected as the optimal path. Based on the path label sequence of the optimal path, the bit positions marked as information bits are included in the identified information set, and the bit positions marked as frozen bits are included in the frozen set. The final blind identification result of the polar code information set is then output.

10. A polar code blind identification decoding device for a non-cooperative communication scenario, adopting the method of any one of claims 1 to 9, characterized in that, include: The receiving data processing module is used to acquire multiple sets of polar code receiving observation sequences to be identified, and to construct a channel log-likelihood ratio (LLR) matrix based on the receiving observation sequences. The initialization module is used to initialize the candidate path set based on the channel log-likelihood ratio (LLR) matrix, as well as the cumulative path metric, path label, recursive LLR state and partial sum information corresponding to each candidate path, to form an initial identification state. The recursive decision calculation module is used to calculate the decision LLR corresponding to the current bit position for each of the current live candidate paths in the order of the source bit positions, based on the recursive LLR state and partial sum information of each path. The dual-hypothesis path expansion module is used to construct a frozen bit hypothesis and an information bit hypothesis for each live path based on the decision LLR corresponding to the current bit position, expand and generate two candidate paths, calculate the path metric increment of the corresponding hypothesis, and update the cumulative path metric of each candidate path. The path filtering module is used to sort all the expanded candidate paths according to the cumulative path metric, retain a preset number of paths as surviving paths, and feed back the status information of the surviving paths to the recursive decision calculation module to enter the identification processing of the next bit position. The identification output module is used to select the candidate path with the best cumulative metric among the final surviving paths after processing all bit positions, and output the blind identification result of the polar code information set based on the path label of the best candidate path.

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